Modeling of Motorists’ reactions on the use of Second Niger Bridge in Southern Part of Nigeria

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I. Ogunjiofor, W. C. Anene, I. C. Udekwe, E. C. Ejike, A. F. Clement, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7805600/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study analyzed motorists’ reactions to the use of the Second Niger Bridge. It focused on safety perceptions, driving behavior, and traffic flow dynamics. Data were collected through field observations during peak and off-peak hours. Surveys were distributed to motorists using the bridge, either directly or through online platforms. Experimental data were also collected by observing driver responses. Data analysis used Response Surface Methodology (RSM) to model relationships between independent variables (e.g., expansion joint condition, corrosion level, traffic density) and dependent variables (e.g., motorist reactions, traffic flow). Results showed that most users perceive safety risks due to visible deterioration, insufficient safety features, and poor maintenance. Regarding travel time, 58% acknowledged a significant reduction due to the bridge. Improving expansion joints will also help achieve fuel savings, as 78% of users reported. Motorists adapt by reducing speed, changing lanes, and avoiding damaged sections. However, this behavior results in increased travel time, higher fuel consumption, and traffic inefficiency. 81% believe that accident risk rises during congestion, and 51% believe traffic improves when the bridge is in good condition. The study concludes that the bridge is crucial for regional transport. Still, its current state undermines safety, efficiency, and public confidence. Stronger maintenance policies, better safety features, and improved user engagement are essential for sustainability. The peak of the curve, located at a deviation of 0.000, signifies the highest design precision at that specific set of factor levels. Motorist Driving Behaviour Second Niger Bridge Traffic flow Response Surface Methodology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1.0 INTRODUCTION Bridges are critical components of transportation infrastructure, providing essential connectivity for economic, social, and industrial activities. The Second Niger Bridge was conceived to alleviate chronic traffic congestion and relieve pressure from the aging first Niger Bridge that was constructed in 1965, and improve traffic flow across the Niger River, which for decades served as the primary link between Nigeria’s southeastern and southwestern regions (Nnaemeka and Adelekun 2023 ). As a major engineering project, its long-term performance and user experience are of paramount importance. Motorist reaction to bridge conditions—such as the smoothness of the ride across expansion joints, visible signs of deterioration, and changes in traffic flow—is a vital but often underexplored aspect of performance evaluation (Kim, et al., 2005 ). These reactions can influence speed patterns, lane usage, and even the overall perception of safety (Ogunjiofor, et al., 2023 ) and structural adequacy (Anene, et al., 2022 ). While the new bridge promises enhanced connectivity and improved traffic flow, concerns have arisen regarding the long-term performance of its structural components—particularly the expansion joints, which are prone to degradation due to repetitive traffic loads, environmental exposure, and corrosion (Jean-Charles, et al., 2011 ). One of the most significant elements affecting the performance and longevity of modern bridges is the expansion joint system. Expansion joints accommodate structural movements due to thermal expansion, traffic loads, and seismic activities, thereby preventing damage to the bridge structure (Ogunjiofor and Umeonyiagu, 2025). However, these joints are often susceptible to wear, corrosion, and failure, especially in regions with high traffic volumes and challenging environmental conditions. The degradation of expansion joints not only poses safety risks but also influences driver comfort and traffic behavior. Corrosion protection of metallic bridge components, particularly within expansion joints, is also a key durability concern. Moisture, salts, and pollutants can accelerate corrosion, leading to costly maintenance and reduced structural integrity (Koch et al., 2001). Effective corrosion protection strategies are vital in extending the service life of bridge components and ensuring safe usage by motorists ( Ogunjiofor, et al., 2025). Furthermore, the influence of these structural conditions on driver behavior has not been systematically studied (Kohm, et al., 2023 ). Many motorists unconsciously adjust their driving patterns—such as speed reduction, lane switching, or erratic maneuvers—when encountering damaged or uncomfortable bridge joints. Such reactions can in turn affect traffic flow efficiency, increase the risk of accidents, and undermine the perceived safety of the infrastructure (AKM, et al., 2014). Existing studies often isolate structural assessments from human-centered traffic behavior, creating a gap in understanding the interaction between bridge performance and motorist response. There is a critical need for an integrated analytical approach that considers the complex relationship among expansion joint durability, corrosion resistance, traffic conditions, and driver behavior (Camara and Reyes-Aldasoro, 2024; Chen, et al., 2009 ). This study seeks to address this gap by applying Response Surface Methodology (RSM) to model and analyze the influence of these interacting variables on motorist reactions. Without such analysis, infrastructure planners and engineers may lack the insights needed to make informed decisions about maintenance priorities, traffic management strategies, and design improvements that enhance both structural longevity and user safety. To analyze such complex interrelationships, Response Surface Methodology (RSM) provides a powerful statistical and mathematical tool. RSM is particularly useful for modeling and analyzing problems where multiple variables influence a response of interest and the goal is to optimize this response (Myery et al, 2016; Ogunjiofor and Ayodele, 2023 ). In the context of the Second Niger Bridge, RSM can be employed to model how factors like expansion joint durability, corrosion conditions, and traffic density affect motorist reactions, offering insights that traditional analysis might miss. The study also considers real-time traffic characteristics, including vehicle density and flow rates, as they interact with the condition of the bridge infrastructure. Geographically, the scope is limited to the Second Niger Bridge corridor, and the analysis will not extend to other bridges or road networks. While it includes observational assessments and statistical modeling, it does not cover long-term predictive structural deterioration or extensive material testing of all bridge components. The primary focus remains on the short- to medium-term interaction between bridge joint conditions, corrosion factors, traffic patterns, and driver response, using RSM as the analytical framework. 2.0 MATERIALS AND METHODS 2.1 Research Design The research design for this study follows a quantitative research approach aimed at systematically analyzing the effects of bridge condition, specifically expansion joint durability and corrosion protection, on motorist reactions and traffic flow using Response Surface Methodology (RSM). This design is chosen because it allows for the efficient exploration of the relationship between multiple variables (e.g., joint condition, traffic density, motorist behavior) and provides a way to optimize these factors in real-world conditions. The study utilized experimental design techniques to manipulate different variables and observe their impact on the response variables (motorist reactions and traffic flow). This design is suitable for generating predictive models that can inform bridge maintenance and improve traffic management strategies. 2.2 Design of Study This study adopts a cross-sectional research design, where data were collected at specific points in time to analyze the current conditions of the Second Niger Bridge and the resulting motorist reactions. The study employed a combination of surveys and observations to collect relevant data. Surveys were used to collect subjective data on drivers’ perceptions of safety and comfort, while observational data tracked traffic flow under varying conditions. This design allows for the collection of both qualitative and quantitative data, which were analyzed through RSM. 2.3 Area of Study The area of study is the Second Niger Bridge, located in Nigeria, which serves as a major transportation link between the southeastern and southwestern parts of the country. The bridge is an important infrastructure that facilitates the movement of people and goods, but it is also exposed to heavy traffic volumes and environmental conditions that may affect its structural integrity, particularly in the areas of expansion joints and corrosion protection. The area of study is crucial for the analysis as it represents a real-world scenario where infrastructure conditions directly affect traffic behavior and safety. 2.4 Population of Study The population of this study consists of all motorists who use the Second Niger Bridge during the study period. This includes private vehicle owners, commercial vehicle drivers, and public transport operators who traverse the bridge. Given the bridge’s role in connecting different regions, the population is diverse, with varying driving behaviors influenced by factors such as vehicle type, speed, and traffic density. The exact population size will be determined based on traffic volume data for the bridge, which will be obtained from traffic management authorities or the Nigerian Federal Ministry of Works. 2.5 Sample and Sampling Technique A stratified random sampling technique was used to select the sample for this study. The samples were drawn from the motorist population, with different strata based on vehicle type (private vehicles, commercial vehicles, etc.) and traffic flow categories (peak and off-peak hours). This technique ensures that the sample adequately represents the diversity of motorists who use the bridge. A sample of approximately 500 motorists was targeted, ensuring a wide representation of different driver categories and conditions. 2.6 Instruments for Data Collection The following instruments were used for data collection: 1.Questionnaires: Structured questionnaires were administered to motorists to gather subjective data on their perceptions of the bridge’s condition, safety concerns, and behavioral responses. The questionnaires include both closed-ended and Likert scale questions to capture various aspects of driver behavior and risk perception. 2.Observation Checklist: An observation checklist was used by field researchers to record visual assessments of bridge condition, such as the extent of expansion joint deterioration and corrosion. This data will be correlated with traffic behavior observations to evaluate the impact of infrastructure quality on driver reactions. 3.Experimental Set-Up for RSM: A designed experiment, based on Response Surface Methodology (RSM), was used to manipulate the structural conditions of the bridge and monitor corresponding changes in motorist behavior and traffic flow. 2.7 Method of Data Collection and Analysis Data were collected through a combination of field observations, surveys. Field observations were made during peak and off-peak traffic hours to assess the flow and motorist reactions under different conditions. Surveys were distributed to motorists using the bridge, either through direct interaction or via online platforms. Experimental data was collected through observing the responses of drivers. Data analysis was conducted using Response Surface Methodology (RSM) to model the relationships between independent variables (e.g., expansion joint condition, corrosion level, traffic density) and dependent variables (e.g., motorist reactions, traffic flow). The RSM approach allowed for the identification of optimal conditions that minimize negative driver reactions and maximize traffic flow efficiency. Statistical analysis was carried out using Analysis of Variance (ANOVA) to test the significance of different factors on motorist behavior and traffic performance. Additionally, regression analysis was performed to determine the strength and nature of the relationships between variables. 3.0 RESULTS AND DISCUSSIONS The results and findings of the study are hereby presented in accordance to the research hypothesis guiding the study. 3.1 Demographic Information Table 1 Demographic Information Variable Categories Frequency Percentage (%) Age Below 20 2 6 21–30 14 42 31–40 13 39 41–50 4 12 Above 50 0 0 Gender Male 18 55 Female 15 45 Type of Vehicle Private Car 14 42 Commercial Vehicle 15 15 45 Motorcycle 3 9 Others 1 3 Driving Experience Less than 1 year 7 21 1–5 years 10 30 The demographic data (Table 1 ) shows that 81% of respondents are aged between 21–40 years, which implies a young and active user base. This age group is likely more exposed to daily commutes, and thus, their responses reflect current usage realities. Gender distribution shows a fair balance (55% male, 45% female), ensuring diverse representation in driving behavior and risk perception. Vehicle type is nearly evenly split between private (42%) and commercial vehicles (45%), indicating that both individual and business transport users are affected by the bridge’s condition. Driving experience ranges widely: 30% have 1–5 years and 27% have 6–10 years, suggesting that most respondents have sufficient experience to assess road safety and infrastructure quality. 3.2 Bridge Use and Impact Based on the findings presented in Fig. 1 , only 15% of users cross the bridge daily, while 30% use it weekly and another 30% rarely. This shows a mixed level of dependence on the bridge. Notably, 36% agreed that bridge conditions affect their driving behavior, indicating a significant safety concern. Experiences of physical discomfort are high: 27% reported very frequent bumps, while 45% experience them occasionally. This indicates that the bridge surface or expansions joints require attention. 3.3 Safety and Structural Confidence It was found during the study that visible deterioration is a major concern—42% of respondents are very concerned, while 45% are somewhat concerned. Only 12% are not concerned, confirming that structural appearance influences user confidence. Findings about the commuters feeling while driving on the bridge in Fig. 2 shows that, 63% reported feeling either unsafe or very unsafe and only 21% felt safe. Although 48% expressed confidence in the structural integrity, the remaining 52% were neutral or not confident, highlighting a public perception gap regarding safety and structural soundness. 3.4 Safety Features and Risks Safety features such as signs, barriers, and lighting are considered insufficient by 33% of respondents, while 18% are uncertain as reported in Table 3 . Over half (54%) believe the expansion joints pose either a slight or significant safety risk. These perceptions are critical, as expansion joints are essential for load transfer and smooth traffic flow. Moreover, 76% of respondents noted that the bridge condition affects travel time, either significantly or slightly, suggesting a direct link between structural performance and travel efficiency. Table 2 Safety Features and Risks Question Response Categories Frequency Percentage (%) Are current safety features sufficient? Yes 16 48 No 11 33 Somewhat 6 18 Does current state of expansion joints pose safety risk? Significant risk 8 24 Slight risk 10 30 Not a risk 5 15 Not sure 10 30 Does bridge condition affect travel time? Significantly, 7 21 Slightly 18 55 No 5 15 Not sure 3 9 3.5 Driving Behavior Driving patterns are clearly influenced by the bridge's physical state. As in Table 3 , a combined 72% of drivers reduce speed either always or frequently when crossing the bridge, and 78% change lanes when noticing visible deterioration. Table 3 Driving Behavior Question Response Categories Frequency Percentage (%) How often do you reduce speed on bridge? Always 12 36 Frequently 12 36 Occasionally 3 9 Never 6 18 Do you change lanes when noticing deterioration ? Always, 11 33 Sometimes 15 45 Never 7 21 How does expansion joint condition affect speed? Slow down considerably 7 21 Slightly, Maintain 14 42 Speed up 12 36 Additionally, 63% of respondents adjust their speed based on the condition of the expansion joints, with 21% slowing considerably. This adaptive behavior reflects users' efforts to navigate safety risks independently. 3.6 Risk Avoidance and Reporting Avoidance behavior is evident, as 36% of users completely avoid deteriorated sections and 27% slow down. These are defensive actions in response to perceived danger. Only 24% continue unaffected as in Fig. 3 , which suggests the majority of users do perceive risk. However, reporting of issues is low—only 18% are very likely to report damage, while 45% are not likely and 30% have never considered it. This shows a lack of public engagement or perhaps a lack of confidence in the effectiveness of reporting mechanisms. 3.7 Government Effort and Maintenance Table 4 Government Effort and Maintenance Question Response Categories Frequency Percentage (%) Has government done enough? Yes 8 24 No 19 58 Not sure 6 18 Has government been proactive in maintenance? Very proactive 4 12 Somewhat proactive 10 30 Not proactive 14 42 Not sure 5 15 Should government invest more in maintenance? Significantly 17 52 Small amount 10 30 No 2 6 Not sure 4 12 The perception of government response is predominantly negative as in Table 4 . Only 24% believe enough has been done to maintain the bridge, while 58% believe not enough effort has been made. Similarly, only 12% consider the government “very proactive” in maintenance. However, 52% believe the government should significantly invest in maintenance, reflecting public demand for better infrastructure management and consistent repair. 3.8 Fuel, and Travel Time Respondents in Fig. 4 strongly associate bridge quality with fuel efficiency. A combined 87% (42% significantly, 45% moderately) say the bridge has increased fuel consumption due to frequent slowing, braking, or detours. Regarding travel time, 58% acknowledged a significant reduction due to the bridge, which highlights its importance in easing regional movement—but ongoing issues reduce its efficiency. Improved expansion joints were also linked to fuel savings by 78% of users. 3.9 Risk Perception and Congestion Risk perception during heavy traffic or poor conditions is high: 30% rated the risk as high and 52% as slight, indicating that 82% associate danger with congestion or deterioration (Fig. 5 ). Additionally, 81% believe that accident risk increases during traffic congestion, and 51% believe traffic improves significantly or slightly when the bridge is in good condition 3.10 Modeling of the motorist Experience The model equation in terms of coded factors is presented in Eq. (1) and can be used to make predictions about the response for given levels of each factor. By default, the high levels of the factors are coded as + 1 and the low levels are coded as -1. The coded equation is useful for identifying the relative impact of the factors by comparing the factor coefficients. C = Vehicles. Graphs of the Traffic models are presented in Fig. 6 . Figure 6 (a)a shows the graph of Residual Vs Run of the traffic flow, where the lowest point of the traffic flow indicated by the colour blue is 75 and the highest point of the traffic flow in the graph is indicated by the colour red which is 96. The traffic flow does not follow a uniform pattern. Figure 6 (b) shows a graph of Traffic flow which has its unit in percentage against "A" which is the code factor for Drivers. The graph has it lowest point indicated with the colour code blue at 1, and has it's highest point indicated with the colour code red at 27. The flow doesn't follow a particular trend. The contour plot in Fig. 7 illustrates the relationship between "Bridge Durability (%)" and two variables: "A Drivers" and "B Experience," while "C Vehicles" is held as an actual factor. The plot uses a color gradient to represent bridge durability, ranging from lower values (red/orange) to higher values (green). Observing the plot, higher bridge durability, indicated by the green regions, appears to be achieved with a combination of higher "B Experience" and moderate to higher "A Drivers." Conversely, lower durability, shown in red and orange, is associated with lower "B Experience" and varying levels of "A Drivers." There is a distinct peak in durability, represented by the darker green area, suggesting an optimal combination of "A Drivers" and "B Experience" for maximizing bridge durability. The contours also reveal that the impact of "A Drivers" on durability seems to be more pronounced at lower levels of "B Experience," where changes in "A Drivers" lead to steeper gradients in durability. As "B Experience" increases, the contours become less steep, indicating a more stable or less sensitive response of durability to changes in "A Drivers". The three dimensional plot in Fig. 8 a visually represents the relationship between "Std Error of Design" and two input factors, "A: Drivers" and "B: Experience." The plot demonstrates a response surface where the standard error of design is minimized within a specific range of the independent variables. The lowest point on the surface, indicating optimal conditions for minimizing the standard error, appears to be centrally located within the displayed range of drivers and experience. The concentric circles on the base of the plot further highlight the contours of this response surface, illustrating how the standard error changes as the levels of drivers and experience vary. The design points, marked in red, indicate specific combinations of drivers and experience where data was collected to construct this model. This visualization is crucial for understanding the interplay between the two factors and their combined effect on the standard error of design, guiding decisions towards achieving a more robust and reliable design. This perturbation plot in Fig. 8 b illustrates the impact of individual factors on the Standard Error of Design, with all other factors held constant at a reference point, typically the center of the design space. The x-axis, "Deviation from Reference Point (Coded Units)," represents the change in a specific factor from its nominal or central value in a standardized unit, while the y-axis, "Std Error of Design," indicates the precision of the estimated response. The parabolic shape of the curve suggests a non-linear relationship between the factor's deviation and the standard error, indicating that moving away from the reference point, either positively or negatively, generally leads to an increase in the standard error of the design. This implies that the model's prediction precision diminishes as the system moves further from the optimal or central operating conditions. The peak of the curve, located at a deviation of 0.000, corresponds to the reference point where the standard error is minimized, signifying the highest precision in the design at that specific set of factor levels. This analysis is crucial for understanding the robustness of a design and identifying the ranges within which factor variations have the least impact on the experimental precision. 4.0 CONCLUSION AND RECOMMENDATIONS 4.1 Conclusions The study investigated motorists’ reactions and perceptions of the Second Niger Bridge using survey data. The findings reveal that motorists’ behavior, safety perception, and travel efficiency are strongly influenced by the condition of the bridge. A majority of respondents expressed concern over visible deterioration, insufficient safety features, and poor maintenance practices. Driving behavior was found to be highly adaptive, as motorists reduced speed, changed lanes, and avoided deteriorated sections to mitigate risks. The RSM modeling further highlighted the interaction between drivers, driving experience, and vehicle type as significant factors influencing traffic flow and risk perception. The contour and surface plots indicated that traffic stability and perceived bridge durability improved with higher driver experience and moderate driver volume. However, structural concerns, particularly around expansion joints, contributed to safety risks, fuel inefficiency, and increased travel time. Overall, the study concludes that the bridge remains vital for regional mobility but requires urgent, systematic maintenance and improved safety features to restore motorist confidence and optimize traffic performance. 4.2 Implications The results portray important implications for policymakers, engineers, and transport managers which include but not limited to: Infrastructure Management : The perception of poor maintenance undermines public trust. Timely inspections and proactive maintenance are critical. Safety on the Road : Motorist behavior such as lane switching and speed reduction directly indicates infrastructural deficiencies. Addressing expansion joint failures and surface deterioration could reduce accident risks. Traffic Flow & Economy : Increased fuel consumption and extended travel time suggest that deterioration has economic implications beyond safety concerns. Improvements will not only enhance safety but also reduce operational costs for transport operators. Policy Engagement: The low willingness of motorists to report defects indicates weak stakeholder engagement, suggesting a need for participatory governance and stronger feedback channels. 4.3 Recommendations Based on the study, the following recommendations are made: Strengthen Proactive Maintenance : Regular inspection and repair of expansion joints, barriers, and surfacing should be institutionalized. Enhance Safety Features : Improved lighting, road markings, and traffic monitoring systems should be installed to reassure motorists. Stakeholder Engagement : Establishing public reporting platforms (mobile apps, hotlines) will encourage users to report damage early. Policy Investment : Government should allocate dedicated funds for long-term bridge management rather than reactive repairs. 5. Traffic Education : Awareness campaigns should guide motorists on safe navigation of bridges under high traffic or deteriorated conditions. 4.4 Limitations This study faced several limitations; The sample size may not fully represent the entire motorist population using the bridge. The study relied heavily on self-reported perceptions, which may be subjective and influenced by recent experiences. RSM modeling was limited to selected factors (drivers, experience, and vehicle type) and may not capture all real-world complexities such as weather, enforcement, or structural design parameters. Lack of long-term observational data on traffic accidents and congestion restricted the validation of reported perceptions with empirical accident records. 4.5 Suggestions for Further Studies Future research should consider: Expanding the sample size and including multiple categories of bridge users (pedestrians, cyclists, heavy truck operators). Incorporating real-time traffic and accident data to complement survey responses. Extending the RSM model to include environmental variables (rainfall, flooding) and policy variables (traffic enforcement). A comparative study of the Second Niger Bridge with other major Nigerian bridges (e.g., Carter Bridge, Third Mainland Bridge) to generalize findings. A cost-benefit analysis of proactive vs. reactive maintenance strategies to guide government investment. Declarations Competing interests: The authors declare that they have no competing interests Funding: This research work was self funded by every author that participated in the research. Ethics approval and Accordance: Ethical approval was waived by the Research Ethics Committee of Chukwuemeka Odumegwu Ojukwu University, given the retrospective nature of the observational study. All research was conducted in accordance with the Committee's guidelines, as detailed in the ethics statement. Consent to Participate: Informed consent was obtained from all the drivers and motorists participants included in the study. Consent to Publish : Informed consent was obtained from all the interviewed drivers and motorists participants to publish. Also, all Authors gave in their consents for the article publication. Data Availability : Data is provided within the manuscript Authors' contributions: Anene conceptualized the topic. Ogunjiofor modeled and analyzed the results gotten from the questionnaire. Ejike gathered the information needed about the bridge. Clement and Udekwe made researches on motorists actions towards the bridge and was a major contributed in writing the scripts. Amadi and Ikeorizu distributed the questionnaires to the road users. All authors read and approved the final manuscript. Acknowledgements: We wish to express our profound gratitude to Engr. Walter Anene and Engr. Dr. Ogunjiofor Emmanuel for their invaluable contributions, guidance, and support throughout the course of this project. Their professional advice and constructive criticism were instrumental in shaping this work to its present form. Their dedication, expertise, and willingness to render assistance at every stage of the research not only enriched the quality of this project but also provided us with deeper insight into the subject matter. We remain truly indebted for the time and effort they devoted to ensuring the successful completion of this work. To both of them, we say a heartfelt thank you. References Nnaemeka, Enemchukwu and Adelekun, Isaac. (2023). The Niger Briddge and the Biafran Economy During and after the Nigerian-Biafran war, 1967-1970. 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(2023) “Utilization of Response Surface Methodology in Optimization of locally sourced Aggregates” Journal of Asian Scientific Research, Vol. 13, Issue 1, pp. 54 – 67. Available: https://doi.org/10.55493/5003.v13i1.4771 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-7805600","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":544214278,"identity":"8e1be9dd-9a9e-4baa-a7bc-7865363f359a","order_by":0,"name":"E. I. 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2","display":"","copyAsset":false,"role":"figure","size":90398,"visible":true,"origin":"","legend":"\u003cp\u003eSafety and Structural Confidence\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7805600/v1/bc81b1a64e8959fe3c56faea.png"},{"id":96372806,"identity":"678a1ea7-abac-428b-ac5c-b840f0c4227b","added_by":"auto","created_at":"2025-11-20 10:35:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":263774,"visible":true,"origin":"","legend":"\u003cp\u003eRisk Avoidance and Reporting\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7805600/v1/6a262406458ae8f41afa0903.png"},{"id":96372805,"identity":"bf689d4d-e5d7-40c8-8337-71e2631a181f","added_by":"auto","created_at":"2025-11-20 10:35:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":93371,"visible":true,"origin":"","legend":"\u003cp\u003eFuel and Travel Time\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7805600/v1/c922063078025a7038225c92.png"},{"id":96452973,"identity":"529a4401-98a5-44d0-bc18-7e2469e3c20b","added_by":"auto","created_at":"2025-11-21 09:56:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":87524,"visible":true,"origin":"","legend":"\u003cp\u003eRisk Perceptions and Congestion\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7805600/v1/0ae8943232bac50437372deb.png"},{"id":96453405,"identity":"0c457e8c-d28d-4a10-8071-7dd53b63e05d","added_by":"auto","created_at":"2025-11-21 09:59:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":116820,"visible":true,"origin":"","legend":"\u003cp\u003eGraph of Traffic Flow Residual Vs Run (a) and Drivers Experience (b)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7805600/v1/dd4859e494a4fbb32992bb36.png"},{"id":96372822,"identity":"69c344dc-2b8d-4827-bd18-322ce6697639","added_by":"auto","created_at":"2025-11-20 10:35:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":158215,"visible":true,"origin":"","legend":"\u003cp\u003eContour Plot\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7805600/v1/72ca43f4c184c28b436c6272.png"},{"id":96452954,"identity":"8ac1faec-bef4-4156-95cc-ff4a8521143c","added_by":"auto","created_at":"2025-11-21 09:55:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":249857,"visible":true,"origin":"","legend":"\u003cp\u003e3-D Plot of the model and Perturbation\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7805600/v1/8454329ae33ad809a23788d1.png"},{"id":98626849,"identity":"6f9bcb14-ed61-4be7-af1d-90d77fc957eb","added_by":"auto","created_at":"2025-12-19 17:10:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2057679,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7805600/v1/74fedaab-b874-446c-98d1-68579965cd21.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling of Motorists’ reactions on the use of Second Niger Bridge in Southern Part of Nigeria","fulltext":[{"header":"1.0 INTRODUCTION","content":"\u003cp\u003eBridges are critical components of transportation infrastructure, providing essential connectivity for economic, social, and industrial activities. The Second Niger Bridge was conceived to alleviate chronic traffic congestion and relieve pressure from the aging first Niger Bridge that was constructed in 1965, and improve traffic flow across the Niger River, which for decades served as the primary link between Nigeria\u0026rsquo;s southeastern and southwestern regions (Nnaemeka and Adelekun \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As a major engineering project, its long-term performance and user experience are of paramount importance. Motorist reaction to bridge conditions\u0026mdash;such as the smoothness of the ride across expansion joints, visible signs of deterioration, and changes in traffic flow\u0026mdash;is a vital but often underexplored aspect of performance evaluation (Kim, et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). These reactions can influence speed patterns, lane usage, and even the overall perception of safety (Ogunjiofor, et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and structural adequacy (Anene, et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While the new bridge promises enhanced connectivity and improved traffic flow, concerns have arisen regarding the long-term performance of its structural components\u0026mdash;particularly the expansion joints, which are prone to degradation due to repetitive traffic loads, environmental exposure, and corrosion (Jean-Charles, et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOne of the most significant elements affecting the performance and longevity of modern bridges is the expansion joint system. Expansion joints accommodate structural movements due to thermal expansion, traffic loads, and seismic activities, thereby preventing damage to the bridge structure (Ogunjiofor and Umeonyiagu, 2025). However, these joints are often susceptible to wear, corrosion, and failure, especially in regions with high traffic volumes and challenging environmental conditions. The degradation of expansion joints not only poses safety risks but also influences driver comfort and traffic behavior. Corrosion protection of metallic bridge components, particularly within expansion joints, is also a key durability concern. Moisture, salts, and pollutants can accelerate corrosion, leading to costly maintenance and reduced structural integrity (Koch et al., 2001). Effective corrosion protection strategies are vital in extending the service life of bridge components and ensuring safe usage by motorists ( Ogunjiofor, et al., 2025).\u003c/p\u003e\u003cp\u003eFurthermore, the influence of these structural conditions on driver behavior has not been systematically studied (Kohm, et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Many motorists unconsciously adjust their driving patterns\u0026mdash;such as speed reduction, lane switching, or erratic maneuvers\u0026mdash;when encountering damaged or uncomfortable bridge joints. Such reactions can in turn affect traffic flow efficiency, increase the risk of accidents, and undermine the perceived safety of the infrastructure (AKM, et al., 2014). Existing studies often isolate structural assessments from human-centered traffic behavior, creating a gap in understanding the interaction between bridge performance and motorist response. There is a critical need for an integrated analytical approach that considers the complex relationship among expansion joint durability, corrosion resistance, traffic conditions, and driver behavior (Camara and Reyes-Aldasoro, 2024; Chen, et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study seeks to address this gap by applying Response Surface Methodology (RSM) to model and analyze the influence of these interacting variables on motorist reactions. Without such analysis, infrastructure planners and engineers may lack the insights needed to make informed decisions about maintenance priorities, traffic management strategies, and design improvements that enhance both structural longevity and user safety.\u003c/p\u003e\u003cp\u003eTo analyze such complex interrelationships, Response Surface Methodology (RSM) provides a powerful statistical and mathematical tool. RSM is particularly useful for modeling and analyzing problems where multiple variables influence a response of interest and the goal is to optimize this response (Myery et al, 2016; Ogunjiofor and Ayodele, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the context of the Second Niger Bridge, RSM can be employed to model how factors like expansion joint durability, corrosion conditions, and traffic density affect motorist reactions, offering insights that traditional analysis might miss.\u003c/p\u003e\u003cp\u003eThe study also considers real-time traffic characteristics, including vehicle density and flow rates, as they interact with the condition of the bridge infrastructure. Geographically, the scope is limited to the Second Niger Bridge corridor, and the analysis will not extend to other bridges or road networks. While it includes observational assessments and statistical modeling, it does not cover long-term predictive structural deterioration or extensive material testing of all bridge components. The primary focus remains on the short- to medium-term interaction between bridge joint conditions, corrosion factors, traffic patterns, and driver response, using RSM as the analytical framework.\u003c/p\u003e"},{"header":"2.0 MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003e2.1 Research Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research design for this study follows a quantitative research approach aimed at systematically analyzing the effects of bridge condition, specifically expansion joint durability and corrosion protection, on motorist reactions and traffic flow using Response Surface Methodology (RSM). This design is chosen because it allows for the efficient exploration of the relationship between multiple variables (e.g., joint condition, traffic density, motorist behavior) and provides a way to optimize these factors in real-world conditions. The study utilized experimental design techniques to manipulate different variables and observe their impact on the response variables (motorist reactions and traffic flow). This design is suitable for generating predictive models that can inform bridge maintenance and improve traffic management strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Design of Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adopts a cross-sectional research design, where data were collected at specific points in time to analyze the current conditions of the Second Niger Bridge and the resulting motorist reactions. The study employed a combination of surveys and observations to collect relevant data. Surveys were used to collect subjective data on drivers\u0026rsquo; perceptions of safety and comfort, while observational data tracked traffic flow under varying conditions. This design allows for the collection of both qualitative and quantitative data, which were analyzed through RSM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Area of Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe area of study is the Second Niger Bridge, located in Nigeria, which serves as a major transportation link between the southeastern and southwestern parts of the country. The bridge is an important infrastructure that facilitates the movement of people and goods, but it is also exposed to heavy traffic volumes and environmental conditions that may affect its structural integrity, particularly in the areas of expansion joints and corrosion protection. The area of study is crucial for the analysis as it represents a real-world scenario where infrastructure conditions directly affect traffic behavior and safety.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Population of Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe population of this study consists of all motorists who use the Second Niger Bridge during the study period. This includes private vehicle owners, commercial vehicle drivers, and public transport operators who traverse the bridge. Given the bridge\u0026rsquo;s role in connecting different regions, the population is diverse, with varying driving behaviors influenced by factors such as vehicle type, speed, and traffic density. The exact population size will be determined based on traffic volume data for the bridge, which will be obtained from traffic management authorities or the Nigerian Federal Ministry of Works.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Sample and Sampling Technique\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA stratified random sampling technique was used to select the sample for this study. The samples were drawn from the motorist population, with different strata based on vehicle type (private vehicles, commercial vehicles, etc.) and traffic flow categories (peak and off-peak hours). This technique ensures that the sample adequately represents the diversity of motorists who use the bridge. A sample of approximately 500 motorists was targeted, ensuring a wide representation of different driver categories and conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Instruments for Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following instruments were used for data collection:\u003c/p\u003e\n\u003cp\u003e1.Questionnaires: Structured questionnaires were administered to motorists to gather subjective data on their perceptions of the bridge\u0026rsquo;s condition, safety concerns, and behavioral responses. The questionnaires include both closed-ended and Likert scale questions to capture various aspects of driver behavior and risk perception.\u003c/p\u003e\n\u003cp\u003e2.Observation Checklist: An observation checklist was used by field researchers to record visual assessments of bridge condition, such as the extent of expansion joint deterioration and corrosion. This data will be correlated with traffic behavior observations to evaluate the impact of infrastructure quality on driver reactions.\u003c/p\u003e\n\u003cp\u003e3.Experimental Set-Up for RSM: A designed experiment, based on Response Surface Methodology (RSM), was used to manipulate the structural conditions of the bridge and monitor corresponding changes in motorist behavior and traffic flow.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Method of Data Collection and Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were collected through a combination of field observations, surveys. Field observations were made during peak and off-peak traffic hours to assess the flow and motorist reactions under different conditions. Surveys were distributed to motorists using the bridge, either through direct interaction or via online platforms. Experimental data was collected through observing the responses of drivers.\u003c/p\u003e\n\u003cp\u003eData analysis was conducted using Response Surface Methodology (RSM) to model the relationships between independent variables (e.g., expansion joint condition, corrosion level, traffic density) and dependent variables (e.g., motorist reactions, traffic flow). The RSM approach allowed for the identification of optimal conditions that minimize negative driver reactions and maximize traffic flow efficiency. Statistical analysis was carried out using Analysis of Variance (ANOVA) to test the significance of different factors on motorist behavior and traffic performance. Additionally, regression analysis was performed to determine the strength and nature of the relationships between variables.\u003c/p\u003e"},{"header":"3.0 RESULTS AND DISCUSSIONS","content":"\u003cp\u003eThe results and findings of the study are hereby presented in accordance to the research hypothesis guiding the study.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Demographic Information\u003c/h2\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\u003eDemographic Information\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBelow 20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbove 50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of Vehicle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrivate Car\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCommercial Vehicle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMotorcycle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDriving Experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than 1 year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;5 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe demographic data (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) shows that 81% of respondents are aged between 21\u0026ndash;40 years, which implies a young and active user base. This age group is likely more exposed to daily commutes, and thus, their responses reflect current usage realities. Gender distribution shows a fair balance (55% male, 45% female), ensuring diverse representation in driving behavior and risk perception.\u003c/p\u003e\u003cp\u003eVehicle type is nearly evenly split between private (42%) and commercial vehicles (45%), indicating that both individual and business transport users are affected by the bridge\u0026rsquo;s condition. Driving experience ranges widely: 30% have 1\u0026ndash;5 years and 27% have 6\u0026ndash;10 years, suggesting that most respondents have sufficient experience to assess road safety and infrastructure quality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e3.2 Bridge Use and Impact\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eBased on the findings presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, only 15% of users cross the bridge daily, while 30% use it weekly and another 30% rarely. This shows a mixed level of dependence on the bridge. Notably, 36% agreed that bridge conditions affect their driving behavior, indicating a significant safety concern. Experiences of physical discomfort are high: 27% reported very frequent bumps, while 45% experience them occasionally. This indicates that the bridge surface or expansions joints require attention.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Safety and Structural Confidence\u003c/h2\u003e\u003cp\u003eIt was found during the study that visible deterioration is a major concern\u0026mdash;42% of respondents are very concerned, while 45% are somewhat concerned. Only 12% are not concerned, confirming that structural appearance influences user confidence.\u003c/p\u003e\u003cp\u003eFindings about the commuters feeling while driving on the bridge in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that, 63% reported feeling either unsafe or very unsafe and only 21% felt safe. Although 48% expressed confidence in the structural integrity, the remaining 52% were neutral or not confident, highlighting a public perception gap regarding safety and structural soundness.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Safety Features and Risks\u003c/h2\u003e\u003cp\u003eSafety features such as signs, barriers, and lighting are considered insufficient by 33% of respondents, while 18% are uncertain as reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Over half (54%) believe the expansion joints pose either a slight or significant safety risk. These perceptions are critical, as expansion joints are essential for load transfer and smooth traffic flow.\u003c/p\u003e\u003cp\u003eMoreover, 76% of respondents noted that the bridge condition affects travel time, either significantly or slightly, suggesting a direct link between structural performance and travel efficiency.\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\u003eSafety Features and Risks\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuestion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResponse\u003c/p\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAre current safety features sufficient?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSomewhat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eDoes current state of expansion joints pose safety risk?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSignificant risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlight risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot a risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot sure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eDoes bridge condition affect travel time?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSignificantly,\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlightly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot sure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Driving Behavior\u003c/h2\u003e\u003cp\u003eDriving patterns are clearly influenced by the bridge's physical state. As in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, a combined 72% of drivers reduce speed either always or frequently when crossing the bridge, and 78% change lanes when noticing visible deterioration.\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\u003eDriving Behavior\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuestion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResponse\u003c/p\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eHow often do you reduce speed on bridge?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlways\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequently\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOccasionally\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDo you change lanes when noticing deterioration\u003c/p\u003e\u003cp\u003e?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlways,\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSometimes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHow does expansion joint condition affect speed?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlow down considerably\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlightly, Maintain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpeed up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAdditionally, 63% of respondents adjust their speed based on the condition of the expansion joints, with 21% slowing considerably. This adaptive behavior reflects users' efforts to navigate safety risks independently.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Risk Avoidance and Reporting\u003c/h2\u003e\u003cp\u003eAvoidance behavior is evident, as 36% of users completely avoid deteriorated sections and 27% slow down. These are defensive actions in response to perceived danger. Only 24% continue unaffected as in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which suggests the majority of users do perceive risk.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHowever, reporting of issues is low\u0026mdash;only 18% are very likely to report damage, while 45% are not likely and 30% have never considered it. This shows a lack of public engagement or perhaps a lack of confidence in the effectiveness of reporting mechanisms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Government Effort and Maintenance\u003c/h2\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\u003eGovernment Effort and Maintenance\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuestion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResponse Categories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHas government done enough?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot sure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eHas government been proactive in maintenance?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery proactive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSomewhat proactive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot proactive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot sure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eShould government invest more in maintenance?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSignificantly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSmall amount\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot sure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe perception of government response is predominantly negative as in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Only 24% believe enough has been done to maintain the bridge, while 58% believe not enough effort has been made. Similarly, only 12% consider the government \u0026ldquo;very proactive\u0026rdquo; in maintenance.\u003c/p\u003e\u003cp\u003eHowever, 52% believe the government should significantly invest in maintenance, reflecting public demand for better infrastructure management and consistent repair.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Fuel, and Travel Time\u003c/h2\u003e\u003cp\u003eRespondents in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e strongly associate bridge quality with fuel efficiency. A combined 87% (42% significantly, 45% moderately) say the bridge has increased fuel consumption due to frequent slowing, braking, or detours.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRegarding travel time, 58% acknowledged a significant reduction due to the bridge, which highlights its importance in easing regional movement\u0026mdash;but ongoing issues reduce its efficiency. Improved expansion joints were also linked to fuel savings by 78% of users.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.9 Risk Perception and Congestion\u003c/h2\u003e\u003cp\u003eRisk perception during heavy traffic or poor conditions is high: 30% rated the risk as high and 52% as slight, indicating that 82% associate danger with congestion or deterioration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, 81% believe that accident risk increases during traffic congestion, and 51% believe traffic improves significantly or slightly when the bridge is in good condition\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.10 Modeling of the motorist Experience\u003c/h2\u003e\u003cp\u003eThe model equation in terms of coded factors is presented in Eq.\u0026nbsp;(1) and can be used to make predictions about the response for given levels of each factor. By default, the high levels of the factors are coded as +\u0026thinsp;1 and the low levels are coded as -1. The coded equation is useful for identifying the relative impact of the factors by comparing the factor coefficients.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/132203_cef980177e9a226b/132203_custom_files/img1763634514.png\" style=\"width: 645px; height: 261.712px;\" width=\"645\" height=\"261.712\"\u003e\u003c/p\u003e\n\u003cp\u003eC\u0026thinsp;=\u0026thinsp;Vehicles.\u003c/p\u003e\u003cp\u003eGraphs of the Traffic models are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(a)a shows the graph of Residual Vs Run of the traffic flow, where the lowest point of the traffic flow indicated by the colour blue is 75 and the highest point of the traffic flow in the graph is indicated by the colour red which is 96. The traffic flow does not follow a uniform pattern.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e(b) shows a graph of Traffic flow which has its unit in percentage against \"A\" which is the code factor for Drivers. The graph has it lowest point indicated with the colour code blue at 1, and has it's highest point indicated with the colour code red at 27. The flow doesn't follow a particular trend.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe contour plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the relationship between \"Bridge Durability (%)\" and two variables: \"A Drivers\" and \"B Experience,\" while \"C Vehicles\" is held as an actual factor. The plot uses a color gradient to represent bridge durability, ranging from lower values (red/orange) to higher values (green).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eObserving the plot, higher bridge durability, indicated by the green regions, appears to be achieved with a combination of higher \"B Experience\" and moderate to higher \"A Drivers.\" Conversely, lower durability, shown in red and orange, is associated with lower \"B Experience\" and varying levels of \"A Drivers.\" There is a distinct peak in durability, represented by the darker green area, suggesting an optimal combination of \"A Drivers\" and \"B Experience\" for maximizing bridge durability. The contours also reveal that the impact of \"A Drivers\" on durability seems to be more pronounced at lower levels of \"B Experience,\" where changes in \"A Drivers\" lead to steeper gradients in durability. As \"B Experience\" increases, the contours become less steep, indicating a more stable or less sensitive response of durability to changes in \"A Drivers\".\u003c/p\u003e\u003cp\u003eThe three dimensional plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea visually represents the relationship between \"Std Error of Design\" and two input factors, \"A: Drivers\" and \"B: Experience.\" The plot demonstrates a response surface where the standard error of design is minimized within a specific range of the independent variables. The lowest point on the surface, indicating optimal conditions for minimizing the standard error, appears to be centrally located within the displayed range of drivers and experience. The concentric circles on the base of the plot further highlight the contours of this response surface, illustrating how the standard error changes as the levels of drivers and experience vary. The design points, marked in red, indicate specific combinations of drivers and experience where data was collected to construct this model. This visualization is crucial for understanding the interplay between the two factors and their combined effect on the standard error of design, guiding decisions towards achieving a more robust and reliable design.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis perturbation plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb illustrates the impact of individual factors on the Standard Error of Design, with all other factors held constant at a reference point, typically the center of the design space. The x-axis, \"Deviation from Reference Point (Coded Units),\" represents the change in a specific factor from its nominal or central value in a standardized unit, while the y-axis, \"Std Error of Design,\" indicates the precision of the estimated response.\u003c/p\u003e\u003cp\u003eThe parabolic shape of the curve suggests a non-linear relationship between the factor's deviation and the standard error, indicating that moving away from the reference point, either positively or negatively, generally leads to an increase in the standard error of the design. This implies that the model's prediction precision diminishes as the system moves further from the optimal or central operating conditions. The peak of the curve, located at a deviation of 0.000, corresponds to the reference point where the standard error is minimized, signifying the highest precision in the design at that specific set of factor levels. This analysis is crucial for understanding the robustness of a design and identifying the ranges within which factor variations have the least impact on the experimental precision.\u003c/p\u003e\u003c/div\u003e"},{"header":"4.0 CONCLUSION AND RECOMMENDATIONS","content":"\u003cp\u003e\u003cstrong\u003e4.1 Conclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study investigated motorists\u0026rsquo; reactions and perceptions of the Second Niger Bridge using survey data. The findings reveal that motorists\u0026rsquo; behavior, safety perception, and travel efficiency are strongly influenced by the condition of the bridge. A majority of respondents expressed concern over visible deterioration, insufficient safety features, and poor maintenance practices. Driving behavior was found to be highly adaptive, as motorists reduced speed, changed lanes, and avoided deteriorated sections to mitigate risks.\u003c/p\u003e\n\u003cp\u003eThe RSM modeling further highlighted the interaction between drivers, driving experience, and vehicle type as significant factors influencing traffic flow and risk perception. The contour and surface plots indicated that traffic stability and perceived bridge durability improved with higher driver experience and moderate driver volume. However, structural concerns, particularly around expansion joints, contributed to safety risks, fuel inefficiency, and increased travel time.\u003c/p\u003e\n\u003cp\u003eOverall, the study concludes that the bridge remains vital for regional mobility but requires urgent, systematic maintenance and improved safety features to restore motorist confidence and optimize traffic performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results portray important implications for policymakers, engineers, and transport managers which include but not limited to:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003e\u003cstrong\u003eInfrastructure Management\u003c/strong\u003e: The perception of poor maintenance undermines public trust. Timely inspections and proactive maintenance are critical.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSafety on the Road\u003c/strong\u003e: Motorist behavior such as lane switching and speed reduction directly indicates infrastructural deficiencies. Addressing expansion joint failures and surface deterioration could reduce accident risks.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTraffic Flow \u0026amp; Economy\u003c/strong\u003e: Increased fuel consumption and extended travel time suggest that deterioration has economic implications beyond safety concerns. Improvements will not only enhance safety but also reduce operational costs for transport operators.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePolicy Engagement:\u003c/strong\u003e The low willingness of motorists to report defects indicates weak stakeholder engagement, suggesting a need for participatory governance and stronger feedback channels.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Recommendations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the study, the following recommendations are made:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003e\u003cstrong\u003eStrengthen Proactive Maintenance\u003c/strong\u003e: Regular inspection and repair of expansion joints, barriers, and surfacing should be institutionalized.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEnhance Safety Features\u003c/strong\u003e: Improved lighting, road markings, and traffic monitoring systems should be installed to reassure motorists.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eStakeholder Engagement\u003c/strong\u003e: Establishing public reporting platforms (mobile apps, hotlines) will encourage users to report damage early.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePolicy Investment\u003c/strong\u003e: Government should allocate dedicated funds for long-term bridge management rather than reactive repairs.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e5. \u003cstrong\u003eTraffic Education\u003c/strong\u003e: Awareness campaigns should guide motorists on safe navigation of bridges under high traffic or deteriorated conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study faced several limitations;\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003eThe sample size may not fully represent the entire motorist population using the bridge.\u003c/li\u003e\n \u003cli\u003eThe study relied heavily on self-reported perceptions, which may be subjective and influenced by recent experiences.\u003c/li\u003e\n \u003cli\u003eRSM modeling was limited to selected factors (drivers, experience, and vehicle type) and may not capture all real-world complexities such as weather, enforcement, or structural design parameters.\u003c/li\u003e\n \u003cli\u003eLack of long-term observational data on traffic accidents and congestion restricted the validation of reported perceptions with empirical accident records.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Suggestions for Further Studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFuture research should consider:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003eExpanding the sample size and including multiple categories of bridge users (pedestrians, cyclists, heavy truck operators).\u003c/li\u003e\n \u003cli\u003eIncorporating real-time traffic and accident data to complement survey responses.\u003c/li\u003e\n \u003cli\u003eExtending the RSM model to include environmental variables (rainfall, flooding) and policy variables (traffic enforcement).\u003c/li\u003e\n \u003cli\u003eA comparative study of the Second Niger Bridge with other major Nigerian bridges (e.g., Carter Bridge, Third Mainland Bridge) to generalize findings.\u003c/li\u003e\n \u003cli\u003eA cost-benefit analysis of proactive vs. reactive maintenance strategies to guide government investment.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research work was self funded by every author that participated in the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and Accordance:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical approval was waived by the Research Ethics Committee of Chukwuemeka Odumegwu Ojukwu University, given the retrospective nature of the observational study. All research was conducted in accordance with the Committee\u0026apos;s guidelines, as detailed in the ethics statement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all the drivers and motorists participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e: Informed consent was obtained from all the interviewed drivers and motorists participants to publish. Also, all Authors gave in their consents for the article publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e: Data is provided within the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eAnene conceptualized the topic. Ogunjiofor modeled and analyzed the results gotten from the questionnaire. Ejike gathered the information needed about the bridge. Clement and Udekwe made researches on motorists actions towards the bridge and was a major contributed in writing the scripts. Amadi and Ikeorizu distributed the questionnaires to the road users. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe wish to express our profound gratitude to Engr. Walter Anene and Engr. Dr. Ogunjiofor Emmanuel for their invaluable contributions, guidance, and support throughout the course of this project. Their professional advice and constructive criticism were instrumental in shaping this work to its present form.\u003c/p\u003e\n\u003cp\u003eTheir dedication, expertise, and willingness to render assistance at every stage of the research not only enriched the quality of this project but also provided us with deeper insight into the subject matter. We remain truly indebted for the time and effort they devoted to ensuring the successful completion of this work. To both of them, we say a heartfelt thank you.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNnaemeka, Enemchukwu and Adelekun, Isaac. (2023). The Niger Briddge and the Biafran Economy During and after the Nigerian-Biafran war, 1967-1970. Mediterranean Journal of Social Science. 14(5). 49. DOI: https://doi.org/1036941/mjss-2023-0031 \u003c/li\u003e\n\u003cli\u003eKim, Sang-Hyo \u0026amp; Lee, Yong-Seon \u0026amp; Cho, Kwang-Yil. (2005). Analysis of Horizontal Reactions Due to Moving Vehicle Loads in Curved Bridges with Varied Support Conditions. Advances in Structural Engineering - ADV STRUCT ENG. 8. 529-545. https://doi.org/10.1260/136943305774858025 \u003c/li\u003e\n\u003cli\u003eOgunjiofor, E.I., Okoye N.G. and Ezeonyi I.E. (2023) \u0026ldquo;Analysis of Safety Behaviour of workers Under Small-Scale Construction site: A case study of Anambra State\u0026rdquo; American Journal of Interdisciplinary Research and Innovation (AJIRI), Vol. 2, Issue 2, pp. 44 \u0026ndash; 50\u003c/li\u003e\n\u003cli\u003eW.C. Anene, P.T. Agudosi, E.I. Ogunjiofor (2022), \u0026ldquo;Design of Traffic Signal Control System at Orlu Junction, Ihiala, Anambra State, Nigeria\u0026rdquo;, International Journal of Transportation Engineering and Traffic System, 8(2): pp. 38\u0026ndash;49. DOI: https://doi.org/10.37628/IJTETS\u003c/li\u003e\n\u003cli\u003eJean-Charles, Wyss, Di Su, Yozo Fujino, (2011). Prediction of vehicle-induced local responses and application to a skewed girder bridge, Engineering Structures, Volume 33, Issue 4, Pp. 1088-1097, https://doi.org/10.1016/j.engstruct.2010.12.020 \u003c/li\u003e\n\u003cli\u003eEmmanuel Ogunjiofor and Ikechukwu Umeonyiagu (2025), \u0026ldquo;Use of PBridge for analysis and design of single span prestressed concrete bridge subjected to wind and thermal loads\u0026rdquo;, World Journal of Advanced Engineering Technology and Sciences, Vol 15(03), pp. 027\u0026ndash;038. DOI: https://doi.org/10.30574/wjaets.2025.15.3.0862\u003c/li\u003e\n\u003cli\u003eKoch, G., Brongers, M. P. H., Thompson, N. G., Virmani, Y. P., \u0026amp; Payer, J. H. (2002). Corrosion cost and preventive strategies in the United States. Report FHWA-RD-01-156, Federal Highway Administration.\u003c/li\u003e\n\u003cli\u003eEmmanuel Ogunjiofor, Ikechukwu Umeonyiagu, Emmanuel Uzuh (2025), \u0026ldquo;Analysis and Design of Wind and Temperature Load of a Continuous Span I-beam Prestressed Concrete Bridge using PBridge Software\u0026rdquo;, International Journal of Engineering Research \u0026amp; Technology (IJERT), Vol. 14 Issue 05, ISSN: 2278-0181, Available on http://www.ijert.org\u003c/li\u003e\n\u003cli\u003eKohm, M., Stempniewski, L. \u0026amp; Stark, A. Influence of vehicle traffic on modal-based bridge monitoring. J Civil Struct Health Monit 13, 219\u0026ndash;234 (2023). https://doi.org/10.1007/s13349-022-00630-z\u003c/li\u003e\n\u003cli\u003eAKM, Anwarul Islam, Frank Li, Hiwa Hamid, Amer Jaroo, (2014) Bridge Condition Assessment and Load Rating using Dynamic Response, Youngstown State University, One University Plaza, Youngstown, Ohio,\u003c/li\u003e\n\u003cli\u003eAlfredo Camara, and Constantino Carlos Reyes-Aldasoro, (2024), Dynamic analysis of the effects of vehicle movement over bridges observed with CCTV images, Engineering Structures, Volume 317, https://doi.org/10.1016/j.engstruct.2024.118653 \u003c/li\u003e\n\u003cli\u003eChen, Yangbo \u0026amp; Feng, Maria \u0026amp; Tan, Chin An. (2009). Bridge Structural Condition Assessment Based on Vibration and Traffic Monitoring. Journal of Engineering Mechanics-asce - J ENG MECH-ASCE. 135. https://doi.org/10.1061/(ASCE)0733-9399(2009)135:8(747) \u003c/li\u003e\n\u003cli\u003eMyers, R. H., Montgomery, D. C., \u0026amp; Anderson-Cook, C. M. (2016). Response surface methodology: Process and product optimization using designed experiments (4\u003csup\u003eth\u003c/sup\u003e ed.). Wiley\u003c/li\u003e\n\u003cli\u003eOgunjiofor, E.I. and Ayodele F.O. (2023) \u0026ldquo;Utilization of Response Surface Methodology in Optimization of locally sourced Aggregates\u0026rdquo; Journal of Asian Scientific Research, Vol. 13, Issue 1, pp. 54 \u0026ndash; 67. Available: https://doi.org/10.55493/5003.v13i1.4771 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Motorist, Driving Behaviour, Second Niger Bridge, Traffic flow, Response Surface Methodology","lastPublishedDoi":"10.21203/rs.3.rs-7805600/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7805600/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study analyzed motorists\u0026rsquo; reactions to the use of the Second Niger Bridge. It focused on safety perceptions, driving behavior, and traffic flow dynamics. Data were collected through field observations during peak and off-peak hours. Surveys were distributed to motorists using the bridge, either directly or through online platforms. Experimental data were also collected by observing driver responses. Data analysis used Response Surface Methodology (RSM) to model relationships between independent variables (e.g., expansion joint condition, corrosion level, traffic density) and dependent variables (e.g., motorist reactions, traffic flow). Results showed that most users perceive safety risks due to visible deterioration, insufficient safety features, and poor maintenance. Regarding travel time, 58% acknowledged a significant reduction due to the bridge. Improving expansion joints will also help achieve fuel savings, as 78% of users reported. Motorists adapt by reducing speed, changing lanes, and avoiding damaged sections. However, this behavior results in increased travel time, higher fuel consumption, and traffic inefficiency. 81% believe that accident risk rises during congestion, and 51% believe traffic improves when the bridge is in good condition. The study concludes that the bridge is crucial for regional transport. Still, its current state undermines safety, efficiency, and public confidence. Stronger maintenance policies, better safety features, and improved user engagement are essential for sustainability. The peak of the curve, located at a deviation of 0.000, signifies the highest design precision at that specific set of factor levels.\u003c/p\u003e","manuscriptTitle":"Modeling of Motorists’ reactions on the use of Second Niger Bridge in Southern Part of Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 10:35:05","doi":"10.21203/rs.3.rs-7805600/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":"e139f627-8da7-4e96-be27-d4c776526e9c","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-19T06:09:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 10:35:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7805600","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7805600","identity":"rs-7805600","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}