Major Causes of Accidents in Urban Transportation Network and Methods to Enhance its Safety

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As urbanization continues to accelerate, with more people moving to cities seeking better opportunities and a higher standard of living, the risks associated with urban accidents become more apparent. Investigating the reasons behind this issue and proposing effective solutions is a concern for policymakers. In some cases, the driver is responsible for accidents, while in others, the built environment contributes to the accidents. The aim of this article is to examine factors related to built-environment conditions. Overall, the parameters are divided into four categories: accident, demographic, traffic, and roadway. Relationships between each factor and the occurrence of accidents (in terms of fatalities, injuries, and damages) are examined. The factors in this report focus on external factors related to vehicle activities are the focus of this report. This study focuses on the causes of urban accidents in Tehran, based on aggregate data from 2000 to 2020 in each region. It is concluded that the main causes of this issue include the high number of motorcycles in the city center, excessive speed on congested roads, high vehicle density in certain parts of the city, and unsafe roadways. Effective measures include road repair and renovation, urban development, the implementation of speed limits on highways, the imposition of restrictions on motorcycles on high-speed roads, implementing demand management strategies to reduce traffic on congested roads, establishing feasible options for motorcycle riders, and rigorously enforcing traffic regulations. Urban Accidents Urban Transportation Network External Factors Safety 1. Introduction Road safety has always been a fundamental subject for scientific research. One aspect that is often examined is road accidents, which have a direct impact on the social environment due to the deaths or permanent disabilities of many individuals. These accidents are considered an economic problem due to the high costs associated with compensating for the damages to property or individuals (Abolvardi et al. 2022 ). Urban traffic and road accidents are significant concerns in modern cities, and the importance of road safety in urban areas cannot be overstated. Road accidents not only result in loss of life and permanent disabilities but also have a profound impact on the social fabric of communities. The economic implications of road accidents are substantial, as they impose high costs for compensating damages to both property and individuals. Numerous factors are involved in traffic accidents, generally classified into three groups: Vehicle-related, environmental-related, and human factors (de Vries et al. 2017 ). The investigation of risk factors related to fatalities in road accidents has been the subject of numerous studies (Abolvardi et al. 2022 ; Azami-Aghdash et al. 2019 ; Chang et al. 2020 ). Research conducted in the United States revealed that human variables were responsible for 57% of accidents, whereas environmental and roadway factors were responsible for 34%. Furthermore, it was shown that 30% of the components could be attributed to the interaction between the environment and human behavior, while 4% were entirely influenced by environmental circumstances (Peden et al. 2004 ). Previous research has found several human risk factors, including gender, age, excessive speed, not wearing a seat belt, smoking and alcohol intake, a driver's lack of focus, and poor vehicle control (Khosravi Shadmani et al. 2013 ; Soori and Khorasani-Zavareh 2019; Soori and Yousefinezhadi 2020 ). In urban environments, where population density is high and various modes of transportation coexist, ensuring road safety becomes even more critical. The complex interactions between pedestrians, cyclists, motorcyclists, and motorists pose unique challenges. Factors such as inadequate infrastructure, insufficient safety measures, driver negligence, and non-compliance with traffic regulations contribute to the occurrence of road accidents in urban areas. Addressing urban road accidents requires a comprehensive strategy that involves collaboration between urban planners, transportation authorities, law enforcement agencies, and the public. By prioritizing road safety through proactive measures, cities can create safer and more sustainable urban environments for their residents. According to a report by the World Health Organization in 2023, approximately 1.19 million people worldwide lost their lives due to road accidents. In addition to this staggering number, between 20 and 50 million people suffer from non-fatal injuries and disabilities annually as a result of road accidents. Currently, road accidents are the leading cause of death for individuals aged 5 to 29 and the eighth leading cause of death across all age groups (WHO 2023 ). The rate of fatalities is particularly significant in developing countries like Iran. In Iran, the average death rate due to road accidents is 30 to 39 per 100,000 population (Soori and Khorasani-Zavareh 2019), while the average rates for European and Eastern Mediterranean countries, respectively, are 17.4 and 26.4 per 100,000 population (Larson, Bavinger, and Henning 2016 ). These statistics highlight the substantial impact of road accidents on mortality rates, particularly in developing nations. With the rapid growth of urban populations, urban traffic has become one of the primary concerns for city dwellers. According to the Iran Statistics Center, in 2015, there were approximately 18.3 million daily trips within the metropolitan area of Tehran, Iran. Nearly 2 million of these trips used the intra-city metro network, meaning 16.3 million of them took place on Tehran's surface streets. According to the government statistics, there were around 63,574 urban traffic accidents recorded in Tehran that year. This means that there was one accident for every 93,584 trips, or, in other words, the probability of an accident occurring on a trip was 0.0011%. Additionally, considering the approximate population of 8.5 million residents in Tehran, the statistics indicate approximately 0.0075 accidents per resident. However, it is worth noting that, considering the increase in Tehran's population throughout the day, this number may slightly increase. For comparison, we can consider the city of London. In 2019, there were 25,000 urban traffic accidents recorded in London (Matters 2020 ). Given the comparable population sizes of London and Tehran, one could argue that the likelihood of traffic accidents in Tehran is 2.54 times higher than in London. The aim of this article is to examine the reasons behind the relatively high statistics of urban traffic accidents in Tehran and propose solutions to enhance the safety of the urban transportation network. The next section will begin with a review of the existing literature on transportation accidents, specifically focusing on the urban roadway network. Next, the available data is presented, which is the average data of each district of the 22 districts of Tehran (the data of each district is aggregated) over the past two decades. Then, the various elements that cause accidents are analyzed in three separate sections: a simple analysis, a combined analysis, and a classification analysis. Ultimately, a concise conclusion is presented. 2. Literature Review Building models based on existing variables is of high importance. Shahsavari et al. investigated and constructed one-variable count models and two-variable count models in order to identify the variables that influence the number of fatalities and injuries that occur in urban traffic accidents. The study uses accident data specifically from Kermanshah Province, Iran, over a period from March 2020 to March 2021. The findings indicate a significant association between many factors and incidence of fatalities, including motorcycle accidents, pedestrian accidents, right of way, speeding (Shahsavari et al. 2022 ). The frequency of on-street parking accidents across different road types and the influence of road geometry and traffic factors should also be considered. Agbelie's findings revealed a substantial variation among distinct observations for 66.7% of the estimated parameters (Agbelie 2020 ). The study provided evidence that the implementation of shoulder, center median, or one-way driving signs in road sections leads to a decrease in the probability of on-street parking accidents by 62.17%, 68.44%, and 95.91%, respectively. The results of this study can provide valuable insights for policymakers in evaluating the potential risks associated with specific aspects. Retallack and Ostendorf also investigated the number of accidents at 120 junctions in Adelaide, Australia, specifically investigating the relationship between the volume of traffic and the frequency of accidents. The research findings indicate that there is a linear correlation between traffic volume and accident frequency at lower volumes. However, it was observed that with the highest traffic volumes, the models exhibited a notable quadratic explanatory term as the frequency of accidents increased at a higher rate. The study provides evidence that targeting managerial resources towards the prevention of such circumstances can yield the most efficacious outcome in terms of reducing the incidence of accidents (Retallack and Ostendorf 2020). Kaygisiz et al. studied the influence of the urban built environment on transportation accidents within a developing nation. The data was gathered from a total of 107 road sections for the period spanning from 2008 to 2010, including both fatalities and injuries. The study employed both binary logistic regression and count-data regression. The findings offer three policy recommendations: the implementation of regulations for public transportation, improving traffic conditions and street networks through urban design, and a review of land use decisions to mitigate factors that contribute to an increase in accidents (Kaygisiz, Senbil, and Yildiz 2017). Also, Hossein and Arabani modeled accidents and established relationships between traffic flow parameters, geometric infrastructure characteristics, environmental factors, and roadway features on urban and rural roads. The findings indicate that the curve length, peak-hour traffic volume, and longitudinal slope are the major influencing factors. The findings indicate that the frequency of accidents rises as the length, peak-hour traffic volume, and longitudinal slope increase, whereas it declines as the density coefficient increases. The occurrence of accidents is also influenced by longitudinal friction and pavement conditions (Hossein and Arabani 2012 ). McAndrews et al. have worked on accident differences in urban and suburban areas. In their study, McAndrews et al. tried to figure out the relationship between urban and rural regions in terms of road safety, with a specific emphasis on differences in fatality rates per trip and per mile covered in the state of Wisconsin. The study utilized three distinct definitions of the urban-rural spectrum, which were derived from existing understandings of urban, suburban, and rural regions characterized by population density and the level of travel flow to metropolitan areas. The findings of the research indicate that areas that are characterized by low population densities, such as suburbs and exurbs, exhibit comparable rates of fatalities to rural places. This underscores the importance of comprehending road safety within the framework of regional development processes rather than relying just on urban-rural classifications (McAndrews et al. 2016 ). Bao et al. studied the spatial impacts of human activities on accidents in urban areas using multi-source big data. The study used the Geographically Weighted Poisson Regression (GWPR) technique to figure out the correlation between influential variables and the frequency of accidents in different regions. The findings indicate that human activity characteristics have a substantial impact on the spatial distribution of accidents and offer novel perspectives for the research of road safety. The findings of comparative analysis suggest that the utilization of several big data sources can enhance the efficacy of regional-level accident models by providing complementary information (Bao et al. 2021 ). Moreover, Haji Mirza Aghasi did a spatial-temporal analysis of urban traffic accidents in Tehran, Iran. The findings indicated that the presence of industrial lands and a greater number of roadways in suburban areas has been associated with a rise in the incidence of fatal and injury accidents. The enhanced safety measures implemented in the city's central business district (CBD) include restricted vehicle access, expanded pedestrian-only walkways, and increased police presence and monitoring. The spatial distribution of accidents exhibits substantial variation across different regions and historical periods, particularly during periods of high demand. Furthermore, the research findings indicate that the spatial arrangement of land utilization within the urban region is indicative of socio-economic and ecological influences (Aghasi 2018 ). Wang et al. have provided a comprehensive analysis of speed variation and accidents. They used high-frequency GPS data obtained from taxis to estimate fluctuations in speed. It integrated the spatiotemporal speed oscillation of individual vehicles with the speed differentials seen between vehicles. The dataset was obtained from a total of 234 segments of one-way arterial roads located in Shanghai. Arterial roads with similar signal spacing density were grouped together due to the potential difference in safety impacts resulting from average speed and speed fluctuations across different segments. To capture potential correlations between segments, a hierarchical log-normal Poisson model with random effects was created. The findings indicated that there was a positive correlation between a 1% rise in average speed on urban arterials and a 0.7% increase in the overall number of accidents. Furthermore, it was observed that greater speed variability was also linked to a higher frequency of accidents (Wang et al. 2018 ). Albalate's study investigates the factors affecting the severity of motorcycle accidents in Barcelona's urban area. The present study employed the local police database of Barcelona over the years 2002–2008 to examine the relationship between motorcycle usage and the likelihood of experiencing serious injuries. The findings indicate that violations of speed restrictions and alcohol consumption are associated with the most severe results. Various factors, including population and environmental risk factors, as well as the utilization of safety helmets, influence the severity of accidents (Albalate and Fernández-Villadangos 2009 ). Table 1 shows a summary of previous similar studies. In this table, it can be shown that different study trends have been followed. Various factors have been studied to determine the severity and frequency of accidents in urban environments. Some of the key factors include motorcycles, speed, on-street parking, roadway geometry, and traffic variables. Additionally, traffic volume and speed changes are also connected to the number of accidents. Fundamental aspects of the environment, such as urban planning and land use, play a substantial role in influencing the number of accidents. According to the said contents, there is always an up-to-date and extensive research gap in the field of urban transportation network safety. Table 1 Summary of previous studies about factors affecting urban transportation network safety. Researcher(s) and Year of Research Factors Shahsavari et al. 2022 Motorcycle accidents, pedestrian accidents, right of way, and speeding. Agbelie 2020 On-street parking accidents, the influence of roadway geometry, and traffic factors. Retallack and Ostendorf 2020 Traffic volume and accident frequency. Kaygisiz, Senbil, and Yildiz 2017 The urban built environment. Hossein and Arabani 2012 Geometric infrastructure characteristics, environmental factors, and roadway features. Bao et al. 2021 The spatial impacts of human activities. Aghasi 2018 Industrial land and the number of roadways and variation across regions and historical periods. Wang et al. 2018 Speed variation. 3. Methodology and data The study and investigation of the factors that cause urban accidents include a broad and comprehensive field of knowledge, as mentioned in the literature review. Numerous factors may influence the occurrence of an accident. Due to this explanation, there are multiple methodologies for carrying out this investigation. This article analyzes data derived from urban traffic monitoring systems, considering the study's limits and special conditions. Put simply, the data for this study excludes the perspectives and personal interactions of individual residents. This study primarily focuses on the analysis of demographic and traffic behavior data related to residents and drivers in 22 districts of Tehran. The data is based on two recent decades and has been gathered from the resources of the Tehran Municipality. However, the data lacking disaggregated access was not accessible, resulting in the provision of aggregated data. So This work does not present or employ specific mathematical methodologies. The primary objective is to examine the causal relationship by analyzing the aggregated data that is currently accessible. The analysis of the relationships and impacts of these parameters facilitates the identification of the causal relationship between accidents and their potential causes. Moreover, by identifying weaknesses within the urban system, an approach can be put forth to facilitate its advancement and mitigate the occurrence of accidents. The parameters investigated in this study are as follows: Accident data: accidents leading to injuries, damages, and fatalities. Demographic data: households with cars and motorcycles. Driver traffic behavior data: average speed, average speed in free-flow, kilometers traveled in smooth and congested traffic, generated and attraction trips, and their total. Urban roadway-related data: average travel time difference between the current and free-flow states, non-kerb/kerb parking area availability, volume-to-capacity ratio in congested and free-flow traffic states. Tables 2 , 3 , and 4 display the raw data for the 22 districts of Tehran in each of these parameters. It matters to acknowledge that there is no overlap in the accident data with regards to fatalities, injuries, or damages. In more general terms, an accident falling under the damage category is defined as one that does not cause any injuries or fatalities, while an accident falling under the injuries category is defined as one that does not result in fatalities. Also, free, smooth, and congested flow are defined in Eq. 1 . Tehran, due to its economic and political centrality, serves as a major attraction for a significant number of trips from surrounding cities as well. As a result, the number of cars in the 22 districts of Tehran is greater than the number of cars among the residents of Tehran. Based on statistics provided by Tehran Municipality, the number of vehicles in Tehran is approximately 4 million cars and 2 million motorcycles. $$\text{f}\text{r}\text{e}\text{e}=\frac{\text{v}\text{o}\text{l}\text{u}\text{m}\text{e}}{\text{c}\text{a}\text{p}\text{a}\text{c}\text{i}\text{t}\text{y}}<0.6, 0.6\le \text{s}\text{m}\text{o}\text{o}\text{t}\text{h}=\frac{\text{v}\text{o}\text{l}\text{u}\text{m}\text{e}}{\text{c}\text{a}\text{p}\text{a}\text{c}\text{i}\text{t}\text{y}}<0.9, \text{c}\text{o}\text{n}\text{g}\text{e}\text{s}\text{t}\text{e}\text{d}=\frac{\text{v}\text{o}\text{l}\text{u}\text{m}\text{e}}{\text{c}\text{a}\text{p}\text{a}\text{c}\text{i}\text{t}\text{y}}\ge 0.9$$ 1 Table 2 Accident and demographic data. Tehran District Damages (Per year) Injuries (Per year) Fatalities (Per year) Percentage of Households with Cars Percentage of Households with Motorcycles 1 2,662 709 2 70.90 8.18 2 3,712 1,271 3 61.56 8.80 3 1,805 481 1 68.07 6.33 4 4,850 1,661 16 67.81 9.74 5 4,332 1,973 15 70.05 9.62 6 1,199 739 1 70.63 11.93 7 1,705 454 1 42.19 10.50 8 2,235 765 15 39.96 9.12 9 882 402 1 38.09 9.91 10 1,555 958 5 32.36 16.65 11 1,421 994 6 35.50 16.11 12 979 1,074 4 28.40 28.27 13 1,257 572 4 61.40 15.12 14 2,722 932 9 43.30 26.08 15 3,370 1,154 22 36.30 20.68 16 1,227 858 15 32.54 17.71 17 1,355 617 25 32.30 22.36 18 2,111 961 14 38.98 24.54 19 1,194 835 15 34.23 17.47 20 1,846 796 19 36.27 19.11 21 846 592 18 58.15 8.68 22 893 407 1 70.40 9.17 Total 44,156 19,207 211 Table 3 Driver traffic behavior data. Number of Trips Percentage of kilometers traveled Tehran District Average Speed (km/h) Free-Flow Average Speed (km/h) Generation (year) Attraction (year) Total (year) Smooth-Flow Traffic Congested-Flow Traffic 1 25 54 404,315 337,856 742,171 40.56 19.60 2 33 59 707,552 506,783 1,214,335 36.65 20.90 3 25 59 438,008 574,354 1,012,363 42.47 19.21 4 43 54 831,092 585,616 1,416,709 64.01 14.67 5 43 59 595,242 349,117 944,359 47.20 2.97 6 19 51 460,470 968,519 1,428,989 26.00 13.65 7 19 49 359,391 450,474 809,865 27.78 19.69 8 25 49 404,315 225,237 629,552 38.42 17.37 9 33 54 157,234 180,190 337,423 35.09 13.39 10 25 54 404,315 551,831 956,146 42.86 26.04 11 19 51 393,084 394,165 787,249 25.75 20.81 12 19 51 381,853 1,047,352 1,429,205 23.92 21.64 13 25 54 202,158 191,451 393,609 25.48 26.21 14 25 54 438,008 247,761 685,769 23.81 25.86 15 33 59 505,394 337,856 843,250 59.37 2.59 16 33 59 213,389 225,237 438,626 48.87 16.44 17 19 59 190,927 168,928 359,854 35.89 10.37 18 43 49 336,929 236,499 573,428 65.25 4.07 19 43 59 190,927 123,880 314,807 69.58 1.36 20 33 51 303,236 247,761 550,997 56.08 1.84 21 43 54 157,234 146,404 303,638 60.15 5.89 22 62 78 78,617 56,309 134,926 66.28 4.76 Total 8,153,690 8,153,580 16,307,270 Table 4 Urban roadway-related data. Tehran District Average Difference in Travel Time of Current state vs Free-Flow (vehicle/h) Non-kerb parking area (m 2 ) kerb parking area (m 2 ) Percentage of Smooth-Flow Traffic During Day Percentage of Congested-Flow Traffic During Day 1 3,807 1,800 16,400 15.28 8.43 2 8,414 1,500 19,100 13.05 8.95 3 5,850 3,500 18,600 20.94 6.10 4 6,772 2,200 25,700 17.32 1.29 5 1,369 2,000 14,800 12.73 5.25 6 11,119 1,900 19,600 23.43 8.77 7 5,257 2,300 11,800 15.67 7.14 8 2,627 2,000 16,300 16.28 4.35 9 1,869 2,100 7,800 20.59 11.14 10 3,857 1,900 10,800 13.83 10.69 11 5,261 3,700 18,500 14.58 5.14 12 6,714 11,000 9,000 12.53 8.05 13 2,322 2,400 16,000 13.64 9.86 14 3,607 2,100 13,600 9.09 2.02 15 3,625 2,000 24,900 11.02 6.59 16 3,386 2,500 10,000 11.64 6.73 17 3,732 2,300 6,500 15.81 3.58 18 1,532 2,400 19,000 14.82 3.19 19 1,193 2,100 7,500 16.60 16.12 20 547 2,100 10,500 11.75 5.09 21 920 1,900 1,200 9.66 1.08 22 552 2,000 1,000 11.59 9.12 4. Results and discussion This section begins by studying simple relationships between factors. This means the accident data is directly compared with every factor, resulting in the identification of a direct or inverse relationship between them. Given the characteristics of the data, it is important to acknowledge the exclusion of driver activities within the car that do not have a measurable external influence. For example, talking on a mobile phone while driving can be a contributing factor to accidents. Nevertheless, as a result of the constraints associated with data collection, only external elements that are capable of being documented are considered, specifically the recorded characteristics related to driver behavior in this particular instance. Furthermore, it is important to note that the documentation of accidents is different from the actual incidence of accidents. One of the variables that led to the variations observed in the criteria is the underreporting of accidents in certain districts of the city, particularly in the southern geographical districts. However, the extensive documentation of incidents that take place within districts 1 to 5 not only fails to enhance the precision of statistics but also results in an unjustified exaggeration of that standard in those districts as compared to other districts. 4.1. Motorcycle and Car One of the most noticeable relationships among the data is the relationship between the percentage of motorcycle ownership by households and fatal accidents. It can be argued that the low safety of motorcycles leads to fatalities even in low-intensity accidents. According to Table 2 , it can be observed that in districts 10 to 20 of Tehran, where the percentage of motorcycle ownership is at its highest, the average number of fatal accidents is also high. However, in districts 4, 5, 8, and 21, this relationship is not significant, indicating that these types of accidents may be dependent on other factors. Among other relationships that can be initially mentioned, there is a direct relationship between the percentage of car ownership and accidents resulting in damages. These accidents, which are not severe and do not result in fatalities or injuries but only involve financial losses, mostly occur during car trips. According to Table 2 , it can be seen that in districts 1 to 5 and 13, where the percentage of vehicle ownership is high, a significant number of accidents resulting in damage have been recorded. Additionally, in districts 9, 11, 12, 16, 17, and 19, where vehicle ownership is low, there has been a decrease in accident statistics with damage. However, these statistics may not hold true for districts 7, 8, 14, 15, 18, and 20, and further examination of additional factors is required. 4.2. Speed and Volume Fatal accidents typically expect an extreme level of severity, with high-severity accidents typically happening at high speeds. This can be easily derived from Tables 2 , 3 and 4 . The average speed in districts 4, 5, 15, 18, 19, and 20 exceeds the average speed in Tehran city during typical traffic circumstances. In these districts, there is a larger incidence of fatal accidents in comparison to the majority of other districts, supporting the stated relationship. Nevertheless, within district 17, despite the relatively low average speed seen during typical traffic circumstances, the incidence of fatal accidents reaches its highest point. There is a notable difference in speed between when traffic is free-flowing and when traffic is common in these districts, which is commonly known as speeding. Hence, violating the speed limit is a significant element that contributes to fatal accidents. Additionally, this can also be attributed to districts 8, 11, and 12, where there has been an increase in accidents resulting in fatalities. The districts that have higher average speeds are those that are suitable for urban or suburban highways. The impact of roadway quality in zones 14 to 21, including suburban or intercity roads, can be explored. In fact, despite the high speeds of vehicles, the high safety level on urban highways has led to a reduction in fatal accidents. However, districts that have suburban and intercity highways have attributed a high number of fatalities to themselves, indicating the direct impact of unsafe highways on fatalities and injuries on the roads. The effect of vehicle speed in urban accidents is dependent upon multiple factors, including the state of the traffic and the capacity of the road. Typically, high traffic speeds can result in fatal accidents. However, in specific regions with a low volume-to-capacity ratio during periods of congested traffic, the impact varies. Based on the data presented in Tables 2 , 3 and 4 , it can be shown that districts with lower ratios have recorded lower fatality rates in accidents. The explanation behind this phenomenon can be attributed to the decline in mean speed coupled with a rise in the percentage of roadway capacity usage. On the other hand, there are districts with a high value for this ratio, but high rates of fatal accidents have been reported. Indeed, the carelessness of drivers, even during periods of high traffic, is a contributing factor to lethal accidents. The evidence presented in districts 4, 14, 17, 18, 20, and 21 suggests that a significant decrease in traffic volume relative to roadway capacity can result in a rise in accidents, potentially due to inattention to speed limits. In fact, the roadways with the lowest traffic volume relative to their capacity have experienced higher accident rates. Furthermore, research indicates that districts like districts 3, 6, and 9, which have higher traffic volumes compared to their capacity, have recorded a lower incidence of total accidents. This statement implies that the roadway design in these districts has taken into account the influence of peak-hour traffic in order to mitigate the intensity of capacity constraints, leading to a reduction in total accidents. 4.3. Travel Time Travel time increases when the existing traffic conditions differ significantly from free-flowing traffic conditions. This causes the total travel time to increase, indicating the level of responsiveness of the urban transportation system to daily trips. The greater this difference, the more severe the unresponsiveness of the transportation system in the respective district. Now, considering Tables 2 and 4 , it can be stated that in districts where travel time is low, the number of accidents increases due to non-compliance with the speed limit, as mentioned in the previous section. In the central districts of the city, however, there has been an increase in accidents and injuries due to the limited capacity of the roadways and the use of motorcycles. Districts where a high percentage of travel distance is covered under smooth traffic conditions have generally recorded the highest number of accidents. 4.4. Accidents, kerb Parking Area, and Trips Simply analyzing the frequency of incidents within a specific geographical district is inadequate. The analysis of accident volume in relation to the number of trips in each particular district serves as the metric to quantify the frequency of accidents. A high ratio indicates a higher likelihood of accidents per trip within a given region, hence reflecting a lower degree of safety on the roadways in such a district. Tables 2 and 3 present data indicating that districts 17, 19, and 21 have the highest number of fatal accidents per trip, making them the most fatal districts in Tehran. In terms of accidents resulting in injuries, areas 5, 19, and 22 have the highest rankings. The areas with the highest statistics in terms of accidents resulting in damage are areas 1, 5, and 22. In terms of accidents, the areas with the lowest accident rates in Tehran are areas 3, 6, and 12, respectively. The paper emphasizes that the western and southwestern districts of Tehran primarily focus the occurrence and magnitude of hazardous incidents. Kerb parking is a potentially influential factor in accidents. This parking arrangement lowers the roadway's capacity and concurrently reduces its breadth, resulting in a rise in accidents. In this context, it is possible to determine the accident volume ratio by considering the percentage of kerb parking in each district and subsequently comparing this ratio with statistics on the volume-to-capacity ratio during periods of congestion. This comparison demonstrates a significant correlation between kerb parking and its effects on traffic and accidents. Tables 2 and 4 show that accidents resulting in damage in districts 1 to 3, 5, 11, and 15 continue to show a notable vulnerability to kerb parking. In districts 4, 18, and 8, although there is a significant number of accidents resulting in damage, it would be unnecessary to attribute kerb parking as the cause of these accidents. The vehicle ownership rate among residents in a certain location does not necessarily serve as a reliable indicator of the accident statistics specific to that district. A significant number of people make every day travel to destinations beyond their immediate residential district. Hence, it is crucial to analyze the trip attractions in each district. For instance, in district 13, the motorcycle ownership rate stands at 15.12%. However, the comparatively low number of travelers to this location contributes to the growth in the accident ratio. Put simply, the goal is to determine the degree to which accidents in a specific district include people who live in that same district (Tables 2 and 3 ). Conversely, the substantial number of trips drawn to District 6 makes the influence of the percentage of vehicle ownership by residents in that district insignificant. The arguments suggest that accidents in districts like district 6 primarily involve individuals from different districts, while accidents in districts like district 13 primarily involve individuals from the same district. This approach has the potential to yield a more precise comprehension of the correlation between the proportion of vehicle ownership and the occurrence of accidents. 5. Conclusion It is evident that road accidents pose a major global public health concern. Efforts to improve road safety, enhance infrastructure, enforce traffic regulations, and promote safe driving behaviors are crucial to reducing the devastating consequences of road accidents. By implementing effective measures and raising awareness, cities can strive to reduce the incidence of road accidents and protect the lives and well-being of their citizens. Several main factors contribute to road accidents in the 22 districts of Tehran. One of these factors is the inadequate condition of the roadways. Insufficient, impaired, or inadequately maintained road infrastructure has the potential to increase the incidence of accidents within these districts. High speed is another contributing factor to accidents in the urban transportation network. Drivers who exceed the speed limit have a slower reaction time and are incapable of avoiding accidents. Moreover, in scenarios characterized by unrestricted traffic flow, certain drivers have a tendency to increase speed, thus posing potential hazards. Excessive speeds on congested roadways can result in severe accidents. High roadway occupancy is an additional element that contributes to accidents in urban areas. The existence of kerb parkings that are not suitable and the excessive usage of roadways by vehicles and businesses have the effect of limiting driving space, increasing traffic congestion, and worsening driving conditions. This may worsen traffic congestion and increase the probability of accidents. The large number of motorcycles is an additional aspect that can increase the likelihood of accidents. In urban areas, a significant number of people ride motorcycles as a mode of transportation. Motorcycles have the potential to cause accidents with both vehicles and people as a result of their high speed and violations of traffic regulations, resulting in accidents. Tehran's 22 districts could implement multiple strategies to reduce accidents. The carrying out of roadway repair and reconstruction attempts, in combination with improved maintenance practices, has the potential to reduce accidents. Expanding heavily trafficked roadways, as well as setting speed limits on highways and crowded streets, may efficiently reduce speed and manage traffic congestion. Moreover, putting regulations and restrictions on the utilization of motorcycles on specific high-speed roadways might reduce the likelihood of accidents. The construction of new and appropriate highways to support the effective movement of traffic loads off congested roadways can contribute to the reduction of traffic congestion and accidents. The implementation of suitable alternatives for motorcycle riders, such as the expansion of public transit infrastructure and the establishment of dedicated lanes specifically designated for motorcycles, might additionally help in the mitigation of accidents. In addition, the start of suitable educational initiatives targeting drivers and motorcycle users has the potential to enhance their understanding of traffic regulations and promote responsible conduct while driving. These programs might include the incorporation of safe driving concepts, attention to roadway signs, and the maintenance of a safe distance from other vehicles. It is imperative to enhance the oversight and execution of traffic regulations across the 22 districts of Tehran. In summary, reducing the number of accidents in urban settings necessitates the implementation of an integrated strategy covering several strategies and actions. These involve improving driving infrastructure, regulating speed and traffic, advocating for comprehensive driver education, establishing feasible options for motorcycle riders, and rigorously enforcing traffic regulations. Relying on the data from this study is stated, and it is recommended to use information related to driver behavior to explore additional variables and analyze the data in a more detailed manner. Because of the limitations of aggregated data, it is recommended to work on disaggregated data as well. Declarations Funding Declaration There was no funding received for this work. Author Contribution M.J. was responsible for the conception, data collection, analysis, interpretation, drafting, critical revision, editing, supervision, validation, and final approval of the published version. R.A. was responsible for the conception, data collection, analysis, interpretation, drafting, and final approval of the published version. A.Kh. was responsible for the conception, data collection, analysis, interpretation, drafting, critical revision, editing, supervision, validation, and final approval of the published version. Data Availability The Tehran municipality's sources provided the data, which are available in the article's context. References Abolvardi, Meisam, Nader Sharifi, Karamatollah Rahmanian, and Vahid Rahmanian. 2022. “Human Risk Factors for Severity of Injuries in Urban and Suburban Traffic Accidents in Southern Iran: An Insight from Police Data.” International Journal of High Risk Behaviors and Addiction 11(4). doi: 10.5812/ijhrba-129419. Agbelie, Bismark. 2020. “Accounting for Unobserved Heterogeneity in On-Street Parking Crash Frequency.” Journal of Transportation Safety & Security 12(8):997–1006. doi: 10.1080/19439962.2019.1571547. Aghasi, Niloofar Haji Mirza. 2018. “Spatio-Temporal Analysis on Urban Traffic Accidents: A Case Study of Tehran City, Iran.” Journal of Geographic Information System 10(5):603–42. doi: 10.4236/jgis.2018.105032. Albalate, Daniel, and Laura Fernández-Villadangos. 2009. “Exploring Determinants of Urban Motorcycle Accident Severity: The Case of Barcelona.” Azami-Aghdash, Saber, Hassan Abolghasem Gorji, Naser Derakhshani, and Homayoun Sadeghi-Bazargani. 2019. “Barriers to and Facilitators of Road Traffic Injuries Prevention in Iran; A Qualitative Study.” Bulletin of Emergency & Trauma 7(4):390–98. doi: 10.29252/beat-070408. Bao, Jie, Zhao Yang, Weili Zeng, and Xiaomeng Shi. 2021. “Exploring the Spatial Impacts of Human Activities on Urban Traffic Crashes Using Multi-Source Big Data.” Journal of Transport Geography 94:103118. doi: 10.1016/j.jtrangeo.2021.103118. Chang, Fang-Rong, He-Lai Huang, David C. Schwebel, Alan H. S. Chan, and Guo-Qing Hu. 2020. “Global Road Traffic Injury Statistics: Challenges, Mechanisms and Solutions.” Chinese Journal of Traumatology 23(4):216–18. doi: 10.1016/j.cjtee.2020.06.001. Hossein, S., and M. Arabani. 2012. “The Relationship between Urban Accidents, Traffic and Geometric Design in Tehran.” Pp. 575–88 in. A Coruna, Spain. Kaygisiz, Ömür, Metin Senbil, and Ahmet Yildiz. 2017. “Influence of Urban Built Environment on Traffic Accidents: The Case of Eskisehir (Turkey).” Case Studies on Transport Policy 5(2):306–13. doi: 10.1016/j.cstp.2017.02.002. Khosravi Shadmani, F., H. Soori, M. Karmi, F. Zayeri, and MR Mehmandar. 2013. “Estimating of Population Attributable Fraction of Unauthorized Speeding and Overtaking on Rural Roads of Iran.” Irje 8(4):9–14. Larson, K., Rebecca Bavinger, and K. Henning. 2016. “The Bloomberg Initiative for Global Road Safety 2015-2016: Addressing Road Traffic Fatalities in Low- and Middle-Income Countries.” The Journal of the Australasian College of Road Safety. Matters, Transport for London| Every Journey. 2020. “Casualties in Greater London during 2019 September.” Transport for London. Retrieved March 27, 2024 (https://www.tfl.gov.uk/corporate/publications-and-reports/road-safety). McAndrews, Carolyn, Kirsten Beyer, Clare E. Guse, and Peter Layde. 2016. “How Do the Definitions of Urban and Rural Matter for Transportation Safety? Re-Interpreting Transportation Fatalities as an Outcome of Regional Development Processes.” Accident Analysis & Prevention 97:231–41. doi: 10.1016/j.aap.2016.09.008. Peden, M., R. Scurfield, D. Sleet, D. Mohan, A. A. Hyder, and E. Jarawan. 2004. “World Report on Road Traffic Injury Prevention. World Health Organization.” Journal of Transportation Technologies 09(03):325–30. doi: 10.4236/jtts.2019.93020. Retallack, Angus Eugene, and Bertram Ostendorf. 2020. “Relationship Between Traffic Volume and Accident Frequency at Intersections.” International Journal of Environmental Research and Public Health 17(4):1393. doi: 10.3390/ijerph17041393. Shahsavari, Soodeh, Ali Mohammadi, Shayan Mostafaei, Ehsan Zereshki, Seyyed Mohammad Tabatabaei, Mohsen Zhaleh, Meisam Shahsavari, and Frouzan Zeini. 2022. “Analysis of Injuries and Deaths from Road Traffic Accidents in Iran: Bivariate Regression Approach.” BMC Emergency Medicine 22(1):130. doi: 10.1186/s12873-022-00686-6. Soori, H., and T. Yousefinezhadi. 2020. “Comparison and Analysis of Road Traffic Injuries in Iran and the Eastern Mediterranean Region: Findings from the Global Status Report on Road Safety–2018.” Irje 16(3):192–201. Soori, Hamid, and Davoud Khorasani-Zavareh. 2019. “Road Traffic Injuries Measures in the Eastern Mediterranean Region: Findings from the Global Status Report on Road Safety - 2015.” Journal of Injury & Violence Research 11(2):149–58. doi: 10.5249/jivr.v11i2.1122. de Vries, Jelle, René de Koster, Serge Rijsdijk, and Debjit Roy. 2017. “Determinants of Safe and Productive Truck Driving: Empirical Evidence from Long-Haul Cargo Transport.” Transportation Research Part E: Logistics and Transportation Review 97(C):113–31. Wang, Xuesong, Qingya Zhou, Mohammed Quddus, Tianxiang Fan, and Shou’en Fang. 2018. “Speed, Speed Variation and Crash Relationships for Urban Arterials.” Accident Analysis & Prevention 113:236–43. doi: 10.1016/j.aap.2018.01.032. WHO. 2023. “Road Traffic Injuries.” Retrieved March 27, 2024 (https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4592001","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321271448,"identity":"c9872a5f-0c20-42b4-ac6e-8a797ea4791a","order_by":0,"name":"Mohsen Jafari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYHCCBIYKBgYeIGT48AEhRkDLGYgWxpkzGBgkiNHCANIC0sQ4mwemBR/QbWB4+OFAzT0ZBp4zhs02NYfrGNgPP2B4uAe3FrMDDMkSB44V8zDw9hg25xw7LMHAk2bAkPAMr5YE6Q9sCTwM/LzbH+ewAbUw5AD9cgC/LT8O/ANr2dhs8Q+ohf8NQS1pEgfbgFp4ezc2M7YBtUgQsuUwQ5rFwb4EHjae8x8be/vSJdsknhkcwKvleE/yjQPfEuz5edISG358s+bn509++PAHHi0MzDwJYJoNJgBi4NMABOwE5EfBKBgFo2AUAADs2E7Hg76+xgAAAABJRU5ErkJggg==","orcid":"","institution":"Amirkabir University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Mohsen","middleName":"","lastName":"Jafari","suffix":""},{"id":321271449,"identity":"b37fc745-13eb-46d1-aaee-1dd859717d5f","order_by":1,"name":"Reza Amin","email":"","orcid":"","institution":"Amirkabir University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Reza","middleName":"","lastName":"Amin","suffix":""},{"id":321271450,"identity":"ea4b3b43-71cb-4816-8f73-43bc61840506","order_by":2,"name":"Ali Khodaii","email":"","orcid":"","institution":"Amirkabir University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Khodaii","suffix":""}],"badges":[],"createdAt":"2024-06-17 05:56:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4592001/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4592001/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67240473,"identity":"a93664cb-dfd9-437b-8f3f-18f85facb2d3","added_by":"auto","created_at":"2024-10-22 21:16:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":710151,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4592001/v1/4d13db06-7746-4025-81bf-a6efb2b17abf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMajor Causes of Accidents in Urban Transportation Network and Methods to Enhance its Safety\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRoad safety has always been a fundamental subject for scientific research. One aspect that is often examined is road accidents, which have a direct impact on the social environment due to the deaths or permanent disabilities of many individuals. These accidents are considered an economic problem due to the high costs associated with compensating for the damages to property or individuals (Abolvardi et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Urban traffic and road accidents are significant concerns in modern cities, and the importance of road safety in urban areas cannot be overstated. Road accidents not only result in loss of life and permanent disabilities but also have a profound impact on the social fabric of communities. The economic implications of road accidents are substantial, as they impose high costs for compensating damages to both property and individuals. Numerous factors are involved in traffic accidents, generally classified into three groups: Vehicle-related, environmental-related, and human factors (de Vries et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The investigation of risk factors related to fatalities in road accidents has been the subject of numerous studies (Abolvardi et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Azami-Aghdash et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chang et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Research conducted in the United States revealed that human variables were responsible for 57% of accidents, whereas environmental and roadway factors were responsible for 34%. Furthermore, it was shown that 30% of the components could be attributed to the interaction between the environment and human behavior, while 4% were entirely influenced by environmental circumstances (Peden et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Previous research has found several human risk factors, including gender, age, excessive speed, not wearing a seat belt, smoking and alcohol intake, a driver's lack of focus, and poor vehicle control (Khosravi Shadmani et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Soori and Khorasani-Zavareh 2019; Soori and Yousefinezhadi \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn urban environments, where population density is high and various modes of transportation coexist, ensuring road safety becomes even more critical. The complex interactions between pedestrians, cyclists, motorcyclists, and motorists pose unique challenges. Factors such as inadequate infrastructure, insufficient safety measures, driver negligence, and non-compliance with traffic regulations contribute to the occurrence of road accidents in urban areas. Addressing urban road accidents requires a comprehensive strategy that involves collaboration between urban planners, transportation authorities, law enforcement agencies, and the public. By prioritizing road safety through proactive measures, cities can create safer and more sustainable urban environments for their residents.\u003c/p\u003e \u003cp\u003eAccording to a report by the World Health Organization in 2023, approximately 1.19\u0026nbsp;million people worldwide lost their lives due to road accidents. In addition to this staggering number, between 20 and 50\u0026nbsp;million people suffer from non-fatal injuries and disabilities annually as a result of road accidents. Currently, road accidents are the leading cause of death for individuals aged 5 to 29 and the eighth leading cause of death across all age groups (WHO \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The rate of fatalities is particularly significant in developing countries like Iran. In Iran, the average death rate due to road accidents is 30 to 39 per 100,000 population (Soori and Khorasani-Zavareh 2019), while the average rates for European and Eastern Mediterranean countries, respectively, are 17.4 and 26.4 per 100,000 population (Larson, Bavinger, and Henning \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These statistics highlight the substantial impact of road accidents on mortality rates, particularly in developing nations.\u003c/p\u003e \u003cp\u003eWith the rapid growth of urban populations, urban traffic has become one of the primary concerns for city dwellers. According to the Iran Statistics Center, in 2015, there were approximately 18.3\u0026nbsp;million daily trips within the metropolitan area of Tehran, Iran. Nearly 2\u0026nbsp;million of these trips used the intra-city metro network, meaning 16.3\u0026nbsp;million of them took place on Tehran's surface streets. According to the government statistics, there were around 63,574 urban traffic accidents recorded in Tehran that year. This means that there was one accident for every 93,584 trips, or, in other words, the probability of an accident occurring on a trip was 0.0011%. Additionally, considering the approximate population of 8.5\u0026nbsp;million residents in Tehran, the statistics indicate approximately 0.0075 accidents per resident. However, it is worth noting that, considering the increase in Tehran's population throughout the day, this number may slightly increase. For comparison, we can consider the city of London. In 2019, there were 25,000 urban traffic accidents recorded in London (Matters \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Given the comparable population sizes of London and Tehran, one could argue that the likelihood of traffic accidents in Tehran is 2.54 times higher than in London. The aim of this article is to examine the reasons behind the relatively high statistics of urban traffic accidents in Tehran and propose solutions to enhance the safety of the urban transportation network.\u003c/p\u003e \u003cp\u003eThe next section will begin with a review of the existing literature on transportation accidents, specifically focusing on the urban roadway network. Next, the available data is presented, which is the average data of each district of the 22 districts of Tehran (the data of each district is aggregated) over the past two decades. Then, the various elements that cause accidents are analyzed in three separate sections: a simple analysis, a combined analysis, and a classification analysis. Ultimately, a concise conclusion is presented.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eBuilding models based on existing variables is of high importance. Shahsavari et al. investigated and constructed one-variable count models and two-variable count models in order to identify the variables that influence the number of fatalities and injuries that occur in urban traffic accidents. The study uses accident data specifically from Kermanshah Province, Iran, over a period from March 2020 to March 2021. The findings indicate a significant association between many factors and incidence of fatalities, including motorcycle accidents, pedestrian accidents, right of way, speeding (Shahsavari et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe frequency of on-street parking accidents across different road types and the influence of road geometry and traffic factors should also be considered. Agbelie's findings revealed a substantial variation among distinct observations for 66.7% of the estimated parameters (Agbelie \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The study provided evidence that the implementation of shoulder, center median, or one-way driving signs in road sections leads to a decrease in the probability of on-street parking accidents by 62.17%, 68.44%, and 95.91%, respectively. The results of this study can provide valuable insights for policymakers in evaluating the potential risks associated with specific aspects. Retallack and Ostendorf also investigated the number of accidents at 120 junctions in Adelaide, Australia, specifically investigating the relationship between the volume of traffic and the frequency of accidents. The research findings indicate that there is a linear correlation between traffic volume and accident frequency at lower volumes. However, it was observed that with the highest traffic volumes, the models exhibited a notable quadratic explanatory term as the frequency of accidents increased at a higher rate. The study provides evidence that targeting managerial resources towards the prevention of such circumstances can yield the most efficacious outcome in terms of reducing the incidence of accidents (Retallack and Ostendorf 2020).\u003c/p\u003e \u003cp\u003eKaygisiz et al. studied the influence of the urban built environment on transportation accidents within a developing nation. The data was gathered from a total of 107 road sections for the period spanning from 2008 to 2010, including both fatalities and injuries. The study employed both binary logistic regression and count-data regression. The findings offer three policy recommendations: the implementation of regulations for public transportation, improving traffic conditions and street networks through urban design, and a review of land use decisions to mitigate factors that contribute to an increase in accidents (Kaygisiz, Senbil, and Yildiz 2017). Also, Hossein and Arabani modeled accidents and established relationships between traffic flow parameters, geometric infrastructure characteristics, environmental factors, and roadway features on urban and rural roads. The findings indicate that the curve length, peak-hour traffic volume, and longitudinal slope are the major influencing factors. The findings indicate that the frequency of accidents rises as the length, peak-hour traffic volume, and longitudinal slope increase, whereas it declines as the density coefficient increases. The occurrence of accidents is also influenced by longitudinal friction and pavement conditions (Hossein and Arabani \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMcAndrews et al. have worked on accident differences in urban and suburban areas. In their study, McAndrews et al. tried to figure out the relationship between urban and rural regions in terms of road safety, with a specific emphasis on differences in fatality rates per trip and per mile covered in the state of Wisconsin. The study utilized three distinct definitions of the urban-rural spectrum, which were derived from existing understandings of urban, suburban, and rural regions characterized by population density and the level of travel flow to metropolitan areas. The findings of the research indicate that areas that are characterized by low population densities, such as suburbs and exurbs, exhibit comparable rates of fatalities to rural places. This underscores the importance of comprehending road safety within the framework of regional development processes rather than relying just on urban-rural classifications (McAndrews et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Bao et al. studied the spatial impacts of human activities on accidents in urban areas using multi-source big data. The study used the Geographically Weighted Poisson Regression (GWPR) technique to figure out the correlation between influential variables and the frequency of accidents in different regions. The findings indicate that human activity characteristics have a substantial impact on the spatial distribution of accidents and offer novel perspectives for the research of road safety. The findings of comparative analysis suggest that the utilization of several big data sources can enhance the efficacy of regional-level accident models by providing complementary information (Bao et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, Haji Mirza Aghasi did a spatial-temporal analysis of urban traffic accidents in Tehran, Iran. The findings indicated that the presence of industrial lands and a greater number of roadways in suburban areas has been associated with a rise in the incidence of fatal and injury accidents. The enhanced safety measures implemented in the city's central business district (CBD) include restricted vehicle access, expanded pedestrian-only walkways, and increased police presence and monitoring. The spatial distribution of accidents exhibits substantial variation across different regions and historical periods, particularly during periods of high demand. Furthermore, the research findings indicate that the spatial arrangement of land utilization within the urban region is indicative of socio-economic and ecological influences (Aghasi \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWang et al. have provided a comprehensive analysis of speed variation and accidents. They used high-frequency GPS data obtained from taxis to estimate fluctuations in speed. It integrated the spatiotemporal speed oscillation of individual vehicles with the speed differentials seen between vehicles. The dataset was obtained from a total of 234 segments of one-way arterial roads located in Shanghai. Arterial roads with similar signal spacing density were grouped together due to the potential difference in safety impacts resulting from average speed and speed fluctuations across different segments. To capture potential correlations between segments, a hierarchical log-normal Poisson model with random effects was created. The findings indicated that there was a positive correlation between a 1% rise in average speed on urban arterials and a 0.7% increase in the overall number of accidents. Furthermore, it was observed that greater speed variability was also linked to a higher frequency of accidents (Wang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlbalate's study investigates the factors affecting the severity of motorcycle accidents in Barcelona's urban area. The present study employed the local police database of Barcelona over the years 2002\u0026ndash;2008 to examine the relationship between motorcycle usage and the likelihood of experiencing serious injuries. The findings indicate that violations of speed restrictions and alcohol consumption are associated with the most severe results. Various factors, including population and environmental risk factors, as well as the utilization of safety helmets, influence the severity of accidents (Albalate and Fern\u0026aacute;ndez-Villadangos \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows a summary of previous similar studies. In this table, it can be shown that different study trends have been followed. Various factors have been studied to determine the severity and frequency of accidents in urban environments. Some of the key factors include motorcycles, speed, on-street parking, roadway geometry, and traffic variables. Additionally, traffic volume and speed changes are also connected to the number of accidents. Fundamental aspects of the environment, such as urban planning and land use, play a substantial role in influencing the number of accidents. According to the said contents, there is always an up-to-date and extensive research gap in the field of urban transportation network safety.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of previous studies about factors affecting urban transportation network safety.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearcher(s) and Year of Research\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShahsavari et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMotorcycle accidents, pedestrian accidents, right of way, and speeding.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgbelie \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOn-street parking accidents, the influence of roadway geometry, and traffic factors.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetallack and Ostendorf 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraffic volume and accident frequency.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaygisiz, Senbil, and Yildiz 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe urban built environment.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHossein and Arabani \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeometric infrastructure characteristics, environmental factors, and roadway features.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBao et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe spatial impacts of human activities.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAghasi \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndustrial land and the number of roadways and variation across regions and historical periods.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpeed variation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"3. Methodology and data","content":"\u003cp\u003eThe study and investigation of the factors that cause urban accidents include a broad and comprehensive field of knowledge, as mentioned in the literature review. Numerous factors may influence the occurrence of an accident. Due to this explanation, there are multiple methodologies for carrying out this investigation. This article analyzes data derived from urban traffic monitoring systems, considering the study's limits and special conditions. Put simply, the data for this study excludes the perspectives and personal interactions of individual residents. This study primarily focuses on the analysis of demographic and traffic behavior data related to residents and drivers in 22 districts of Tehran. The data is based on two recent decades and has been gathered from the resources of the Tehran Municipality. However, the data lacking disaggregated access was not accessible, resulting in the provision of aggregated data. So This work does not present or employ specific mathematical methodologies. The primary objective is to examine the causal relationship by analyzing the aggregated data that is currently accessible. The analysis of the relationships and impacts of these parameters facilitates the identification of the causal relationship between accidents and their potential causes. Moreover, by identifying weaknesses within the urban system, an approach can be put forth to facilitate its advancement and mitigate the occurrence of accidents. The parameters investigated in this study are as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAccident data: accidents leading to injuries, damages, and fatalities.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDemographic data: households with cars and motorcycles.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDriver traffic behavior data: average speed, average speed in free-flow, kilometers traveled in smooth and congested traffic, generated and attraction trips, and their total.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUrban roadway-related data: average travel time difference between the current and free-flow states, non-kerb/kerb parking area availability, volume-to-capacity ratio in congested and free-flow traffic states.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e display the raw data for the 22 districts of Tehran in each of these parameters. It matters to acknowledge that there is no overlap in the accident data with regards to fatalities, injuries, or damages. In more general terms, an accident falling under the damage category is defined as one that does not cause any injuries or fatalities, while an accident falling under the injuries category is defined as one that does not result in fatalities. Also, free, smooth, and congested flow are defined in Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Tehran, due to its economic and political centrality, serves as a major attraction for a significant number of trips from surrounding cities as well. As a result, the number of cars in the 22 districts of Tehran is greater than the number of cars among the residents of Tehran. Based on statistics provided by Tehran Municipality, the number of vehicles in Tehran is approximately 4\u0026nbsp;million cars and 2\u0026nbsp;million motorcycles.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\text{f}\\text{r}\\text{e}\\text{e}=\\frac{\\text{v}\\text{o}\\text{l}\\text{u}\\text{m}\\text{e}}{\\text{c}\\text{a}\\text{p}\\text{a}\\text{c}\\text{i}\\text{t}\\text{y}}\u0026lt;0.6, 0.6\\le \\text{s}\\text{m}\\text{o}\\text{o}\\text{t}\\text{h}=\\frac{\\text{v}\\text{o}\\text{l}\\text{u}\\text{m}\\text{e}}{\\text{c}\\text{a}\\text{p}\\text{a}\\text{c}\\text{i}\\text{t}\\text{y}}\u0026lt;0.9, \\text{c}\\text{o}\\text{n}\\text{g}\\text{e}\\text{s}\\text{t}\\text{e}\\text{d}=\\frac{\\text{v}\\text{o}\\text{l}\\text{u}\\text{m}\\text{e}}{\\text{c}\\text{a}\\text{p}\\text{a}\\text{c}\\text{i}\\text{t}\\text{y}}\\ge 0.9$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAccident and demographic data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTehran\u003c/p\u003e \u003cp\u003eDistrict\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDamages\u003c/p\u003e \u003cp\u003e(Per year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInjuries\u003c/p\u003e \u003cp\u003e(Per year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFatalities\u003c/p\u003e \u003cp\u003e(Per year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage of Households \u003c/p\u003e \u003cp\u003ewith Cars\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePercentage of Households with Motorcycles\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44,156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19,207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDriver traffic behavior data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eNumber of Trips\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003ePercentage of kilometers traveled\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTehran\u003c/p\u003e \u003cp\u003eDistrict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage Speed (km/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFree-Flow Average Speed (km/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneration\u003c/p\u003e \u003cp\u003e(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAttraction\u003c/p\u003e \u003cp\u003e(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSmooth-Flow Traffic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCongested-Flow Traffic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e404,315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e337,856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e742,171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e707,552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e506,783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,214,335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e438,008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e574,354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,012,363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e831,092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e585,616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,416,709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e 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align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e404,315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e225,237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e629,552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157,234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e180,190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e337,423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e 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align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e190,927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e168,928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e359,854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e336,929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e236,499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e573,428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e190,927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123,880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e314,807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e303,236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e247,761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e550,997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157,234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e146,404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e303,638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78,617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56,309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e134,926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,153,690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,153,580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16,307,270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUrban roadway-related data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTehran\u003c/p\u003e \u003cp\u003eDistrict\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage Difference in Travel Time of\u003c/p\u003e \u003cp\u003eCurrent state vs Free-Flow (vehicle/h)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-kerb parking area (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekerb\u003c/p\u003e \u003cp\u003e parking area (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage of \u003c/p\u003e \u003cp\u003eSmooth-Flow Traffic\u003c/p\u003e \u003cp\u003eDuring Day\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePercentage of \u003c/p\u003e \u003cp\u003eCongested-Flow Traffic\u003c/p\u003e \u003cp\u003eDuring Day\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19,100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Results and discussion","content":"\u003cp\u003eThis section begins by studying simple relationships between factors. This means the accident data is directly compared with every factor, resulting in the identification of a direct or inverse relationship between them. Given the characteristics of the data, it is important to acknowledge the exclusion of driver activities within the car that do not have a measurable external influence. For example, talking on a mobile phone while driving can be a contributing factor to accidents. Nevertheless, as a result of the constraints associated with data collection, only external elements that are capable of being documented are considered, specifically the recorded characteristics related to driver behavior in this particular instance. Furthermore, it is important to note that the documentation of accidents is different from the actual incidence of accidents. One of the variables that led to the variations observed in the criteria is the underreporting of accidents in certain districts of the city, particularly in the southern geographical districts. However, the extensive documentation of incidents that take place within districts 1 to 5 not only fails to enhance the precision of statistics but also results in an unjustified exaggeration of that standard in those districts as compared to other districts.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Motorcycle and Car\u003c/h2\u003e \u003cp\u003eOne of the most noticeable relationships among the data is the relationship between the percentage of motorcycle ownership by households and fatal accidents. It can be argued that the low safety of motorcycles leads to fatalities even in low-intensity accidents. According to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it can be observed that in districts 10 to 20 of Tehran, where the percentage of motorcycle ownership is at its highest, the average number of fatal accidents is also high. However, in districts 4, 5, 8, and 21, this relationship is not significant, indicating that these types of accidents may be dependent on other factors. Among other relationships that can be initially mentioned, there is a direct relationship between the percentage of car ownership and accidents resulting in damages. These accidents, which are not severe and do not result in fatalities or injuries but only involve financial losses, mostly occur during car trips. According to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it can be seen that in districts 1 to 5 and 13, where the percentage of vehicle ownership is high, a significant number of accidents resulting in damage have been recorded. Additionally, in districts 9, 11, 12, 16, 17, and 19, where vehicle ownership is low, there has been a decrease in accident statistics with damage. However, these statistics may not hold true for districts 7, 8, 14, 15, 18, and 20, and further examination of additional factors is required.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Speed and Volume\u003c/h2\u003e \u003cp\u003eFatal accidents typically expect an extreme level of severity, with high-severity accidents typically happening at high speeds. This can be easily derived from Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The average speed in districts 4, 5, 15, 18, 19, and 20 exceeds the average speed in Tehran city during typical traffic circumstances. In these districts, there is a larger incidence of fatal accidents in comparison to the majority of other districts, supporting the stated relationship. Nevertheless, within district 17, despite the relatively low average speed seen during typical traffic circumstances, the incidence of fatal accidents reaches its highest point. There is a notable difference in speed between when traffic is free-flowing and when traffic is common in these districts, which is commonly known as speeding. Hence, violating the speed limit is a significant element that contributes to fatal accidents. Additionally, this can also be attributed to districts 8, 11, and 12, where there has been an increase in accidents resulting in fatalities. The districts that have higher average speeds are those that are suitable for urban or suburban highways. The impact of roadway quality in zones 14 to 21, including suburban or intercity roads, can be explored. In fact, despite the high speeds of vehicles, the high safety level on urban highways has led to a reduction in fatal accidents. However, districts that have suburban and intercity highways have attributed a high number of fatalities to themselves, indicating the direct impact of unsafe highways on fatalities and injuries on the roads.\u003c/p\u003e \u003cp\u003eThe effect of vehicle speed in urban accidents is dependent upon multiple factors, including the state of the traffic and the capacity of the road. Typically, high traffic speeds can result in fatal accidents. However, in specific regions with a low volume-to-capacity ratio during periods of congested traffic, the impact varies. Based on the data presented in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e,\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, it can be shown that districts with lower ratios have recorded lower fatality rates in accidents. The explanation behind this phenomenon can be attributed to the decline in mean speed coupled with a rise in the percentage of roadway capacity usage. On the other hand, there are districts with a high value for this ratio, but high rates of fatal accidents have been reported. Indeed, the carelessness of drivers, even during periods of high traffic, is a contributing factor to lethal accidents. The evidence presented in districts 4, 14, 17, 18, 20, and 21 suggests that a significant decrease in traffic volume relative to roadway capacity can result in a rise in accidents, potentially due to inattention to speed limits. In fact, the roadways with the lowest traffic volume relative to their capacity have experienced higher accident rates. Furthermore, research indicates that districts like districts 3, 6, and 9, which have higher traffic volumes compared to their capacity, have recorded a lower incidence of total accidents. This statement implies that the roadway design in these districts has taken into account the influence of peak-hour traffic in order to mitigate the intensity of capacity constraints, leading to a reduction in total accidents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Travel Time\u003c/h2\u003e \u003cp\u003eTravel time increases when the existing traffic conditions differ significantly from free-flowing traffic conditions. This causes the total travel time to increase, indicating the level of responsiveness of the urban transportation system to daily trips. The greater this difference, the more severe the unresponsiveness of the transportation system in the respective district. Now, considering Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, it can be stated that in districts where travel time is low, the number of accidents increases due to non-compliance with the speed limit, as mentioned in the previous section. In the central districts of the city, however, there has been an increase in accidents and injuries due to the limited capacity of the roadways and the use of motorcycles. Districts where a high percentage of travel distance is covered under smooth traffic conditions have generally recorded the highest number of accidents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Accidents, kerb Parking Area, and Trips\u003c/h2\u003e \u003cp\u003eSimply analyzing the frequency of incidents within a specific geographical district is inadequate. The analysis of accident volume in relation to the number of trips in each particular district serves as the metric to quantify the frequency of accidents. A high ratio indicates a higher likelihood of accidents per trip within a given region, hence reflecting a lower degree of safety on the roadways in such a district. Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e present data indicating that districts 17, 19, and 21 have the highest number of fatal accidents per trip, making them the most fatal districts in Tehran. In terms of accidents resulting in injuries, areas 5, 19, and 22 have the highest rankings. The areas with the highest statistics in terms of accidents resulting in damage are areas 1, 5, and 22. In terms of accidents, the areas with the lowest accident rates in Tehran are areas 3, 6, and 12, respectively. The paper emphasizes that the western and southwestern districts of Tehran primarily focus the occurrence and magnitude of hazardous incidents.\u003c/p\u003e \u003cp\u003eKerb parking is a potentially influential factor in accidents. This parking arrangement lowers the roadway's capacity and concurrently reduces its breadth, resulting in a rise in accidents. In this context, it is possible to determine the accident volume ratio by considering the percentage of kerb parking in each district and subsequently comparing this ratio with statistics on the volume-to-capacity ratio during periods of congestion. This comparison demonstrates a significant correlation between kerb parking and its effects on traffic and accidents. Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show that accidents resulting in damage in districts 1 to 3, 5, 11, and 15 continue to show a notable vulnerability to kerb parking. In districts 4, 18, and 8, although there is a significant number of accidents resulting in damage, it would be unnecessary to attribute kerb parking as the cause of these accidents.\u003c/p\u003e \u003cp\u003eThe vehicle ownership rate among residents in a certain location does not necessarily serve as a reliable indicator of the accident statistics specific to that district. A significant number of people make every day travel to destinations beyond their immediate residential district. Hence, it is crucial to analyze the trip attractions in each district. For instance, in district 13, the motorcycle ownership rate stands at 15.12%. However, the comparatively low number of travelers to this location contributes to the growth in the accident ratio. Put simply, the goal is to determine the degree to which accidents in a specific district include people who live in that same district (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Conversely, the substantial number of trips drawn to District 6 makes the influence of the percentage of vehicle ownership by residents in that district insignificant. The arguments suggest that accidents in districts like district 6 primarily involve individuals from different districts, while accidents in districts like district 13 primarily involve individuals from the same district. This approach has the potential to yield a more precise comprehension of the correlation between the proportion of vehicle ownership and the occurrence of accidents.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIt is evident that road accidents pose a major global public health concern. Efforts to improve road safety, enhance infrastructure, enforce traffic regulations, and promote safe driving behaviors are crucial to reducing the devastating consequences of road accidents. By implementing effective measures and raising awareness, cities can strive to reduce the incidence of road accidents and protect the lives and well-being of their citizens.\u003c/p\u003e \u003cp\u003eSeveral main factors contribute to road accidents in the 22 districts of Tehran. One of these factors is the inadequate condition of the roadways. Insufficient, impaired, or inadequately maintained road infrastructure has the potential to increase the incidence of accidents within these districts. High speed is another contributing factor to accidents in the urban transportation network. Drivers who exceed the speed limit have a slower reaction time and are incapable of avoiding accidents. Moreover, in scenarios characterized by unrestricted traffic flow, certain drivers have a tendency to increase speed, thus posing potential hazards. Excessive speeds on congested roadways can result in severe accidents. High roadway occupancy is an additional element that contributes to accidents in urban areas. The existence of kerb parkings that are not suitable and the excessive usage of roadways by vehicles and businesses have the effect of limiting driving space, increasing traffic congestion, and worsening driving conditions. This may worsen traffic congestion and increase the probability of accidents. The large number of motorcycles is an additional aspect that can increase the likelihood of accidents. In urban areas, a significant number of people ride motorcycles as a mode of transportation. Motorcycles have the potential to cause accidents with both vehicles and people as a result of their high speed and violations of traffic regulations, resulting in accidents.\u003c/p\u003e \u003cp\u003eTehran's 22 districts could implement multiple strategies to reduce accidents. The carrying out of roadway repair and reconstruction attempts, in combination with improved maintenance practices, has the potential to reduce accidents. Expanding heavily trafficked roadways, as well as setting speed limits on highways and crowded streets, may efficiently reduce speed and manage traffic congestion. Moreover, putting regulations and restrictions on the utilization of motorcycles on specific high-speed roadways might reduce the likelihood of accidents. The construction of new and appropriate highways to support the effective movement of traffic loads off congested roadways can contribute to the reduction of traffic congestion and accidents. The implementation of suitable alternatives for motorcycle riders, such as the expansion of public transit infrastructure and the establishment of dedicated lanes specifically designated for motorcycles, might additionally help in the mitigation of accidents. In addition, the start of suitable educational initiatives targeting drivers and motorcycle users has the potential to enhance their understanding of traffic regulations and promote responsible conduct while driving. These programs might include the incorporation of safe driving concepts, attention to roadway signs, and the maintenance of a safe distance from other vehicles. It is imperative to enhance the oversight and execution of traffic regulations across the 22 districts of Tehran. In summary, reducing the number of accidents in urban settings necessitates the implementation of an integrated strategy covering several strategies and actions. These involve improving driving infrastructure, regulating speed and traffic, advocating for comprehensive driver education, establishing feasible options for motorcycle riders, and rigorously enforcing traffic regulations.\u003c/p\u003e \u003cp\u003eRelying on the data from this study is stated, and it is recommended to use information related to driver behavior to explore additional variables and analyze the data in a more detailed manner. Because of the limitations of aggregated data, it is recommended to work on disaggregated data as well.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no funding received for this work.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.J. was responsible for the conception, data collection, analysis, interpretation, drafting, critical revision, editing, supervision, validation, and final approval of the published version. R.A. was responsible for the conception, data collection, analysis, interpretation, drafting, and final approval of the published version. A.Kh. was responsible for the conception, data collection, analysis, interpretation, drafting, critical revision, editing, supervision, validation, and final approval of the published version.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe Tehran municipality's sources provided the data, which are available in the article's context.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbolvardi, Meisam, Nader Sharifi, Karamatollah Rahmanian, and Vahid Rahmanian. 2022. \u0026ldquo;Human Risk Factors for Severity of Injuries in Urban and Suburban Traffic Accidents in Southern Iran: An Insight from Police Data.\u0026rdquo; International Journal of High Risk Behaviors and Addiction 11(4). doi: 10.5812/ijhrba-129419.\u003c/li\u003e\n\u003cli\u003eAgbelie, Bismark. 2020. \u0026ldquo;Accounting for Unobserved Heterogeneity in On-Street Parking Crash Frequency.\u0026rdquo; Journal of Transportation Safety \u0026amp; Security 12(8):997\u0026ndash;1006. doi: 10.1080/19439962.2019.1571547.\u003c/li\u003e\n\u003cli\u003eAghasi, Niloofar Haji Mirza. 2018. \u0026ldquo;Spatio-Temporal Analysis on Urban Traffic Accidents: A Case Study of Tehran City, Iran.\u0026rdquo; Journal of Geographic Information System 10(5):603\u0026ndash;42. doi: 10.4236/jgis.2018.105032.\u003c/li\u003e\n\u003cli\u003eAlbalate, Daniel, and Laura Fern\u0026aacute;ndez-Villadangos. 2009. \u0026ldquo;Exploring Determinants of Urban Motorcycle Accident Severity: The Case of Barcelona.\u0026rdquo;\u003c/li\u003e\n\u003cli\u003eAzami-Aghdash, Saber, Hassan Abolghasem Gorji, Naser Derakhshani, and Homayoun Sadeghi-Bazargani. 2019. \u0026ldquo;Barriers to and Facilitators of Road Traffic Injuries Prevention in Iran; A Qualitative Study.\u0026rdquo; Bulletin of Emergency \u0026amp; Trauma 7(4):390\u0026ndash;98. doi: 10.29252/beat-070408.\u003c/li\u003e\n\u003cli\u003eBao, Jie, Zhao Yang, Weili Zeng, and Xiaomeng Shi. 2021. \u0026ldquo;Exploring the Spatial Impacts of Human Activities on Urban Traffic Crashes Using Multi-Source Big Data.\u0026rdquo; Journal of Transport Geography 94:103118. doi: 10.1016/j.jtrangeo.2021.103118.\u003c/li\u003e\n\u003cli\u003eChang, Fang-Rong, He-Lai Huang, David C. 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As urbanization continues to accelerate, with more people moving to cities seeking better opportunities and a higher standard of living, the risks associated with urban accidents become more apparent. Investigating the reasons behind this issue and proposing effective solutions is a concern for policymakers. In some cases, the driver is responsible for accidents, while in others, the built environment contributes to the accidents. The aim of this article is to examine factors related to built-environment conditions. Overall, the parameters are divided into four categories: accident, demographic, traffic, and roadway. Relationships between each factor and the occurrence of accidents (in terms of fatalities, injuries, and damages) are examined. The factors in this report focus on external factors related to vehicle activities are the focus of this report. This study focuses on the causes of urban accidents in Tehran, based on aggregate data from 2000 to 2020 in each region. It is concluded that the main causes of this issue include the high number of motorcycles in the city center, excessive speed on congested roads, high vehicle density in certain parts of the city, and unsafe roadways. Effective measures include road repair and renovation, urban development, the implementation of speed limits on highways, the imposition of restrictions on motorcycles on high-speed roads, implementing demand management strategies to reduce traffic on congested roads, establishing feasible options for motorcycle riders, and rigorously enforcing traffic regulations.\u003c/p\u003e","manuscriptTitle":"Major Causes of Accidents in Urban Transportation Network and Methods to Enhance its Safety","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-08 15:20:58","doi":"10.21203/rs.3.rs-4592001/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a2340435-0c6c-40c5-b456-2add959be7f0","owner":[],"postedDate":"July 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-22T21:08:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-08 15:20:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4592001","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4592001","identity":"rs-4592001","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}