{"paper_id":"0c74a3bd-5d1a-4327-8e24-dbf1464ddf92","body_text":"Strategic Cooperation in Ride-Hailing: Analyzing Operational Efficiency in San Francisco | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Strategic Cooperation in Ride-Hailing: Analyzing Operational Efficiency in San Francisco Ke Liu, Gaozhe Jiang, Xi Cheng, Xin Hu, Chang Che This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4954010/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 Ride-hailing services, such as Uber and Lyft, have become integral to urban transportation systems, offering convenience and flexibility to local residents. However, the expansion of these services can contribute to congestion and efficiency problems, particularly in densely populated cities like San Francisco. This study investigates the potential of information sharing between ride-hailing service providers to mitigate congestion and enhance operational efficiency. By leveraging the BEAM (Behavior, Energy, Autonomy, and Mobility) simulation platform, we model various scenarios in which Uber and Lyft either cooperate by sharing operational data or operate independently. The simulation examines key metrics such as passenger waiting time, vehicle repositioning, and the impact of surge pricing on user welfare and supplier profit. Our findings suggest that strategic cooperation between service providers can reduce passenger waiting times and improve resource allocation, though it may also lead to a reduction in supplier profits due to the decreased effectiveness of surge pricing. The results have significant implications for urban transportation policy and the optimization of ride-hailing operations, highlighting the trade-offs between user benefits and supplier profitability in a collaborative framework. Future research should explore the long-term societal impacts of such cooperation, including its effects on traffic congestion and overall social welfare. Physical sciences/Engineering/Aerospace engineering Physical sciences/Engineering/Civil engineering Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Engineering/Energy infrastructure Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Ride-hailing has become an important component in US transportation system and high vacancy rate of ride-hailing is regarded as one of the major reasons for congestion problem (Olayode et al, 2023). Plenty of feasible suggestions have been raised to relieve the congestion caused by ride-hailing services, such as encouraging carpooling (Stiglic et al., 2015; Cohen et al., 2023; Cheng et al., 2024a), implementing dedicated ride-hailing zones (Ranjbari et al., 2021; Wei et al., 2022), dynamic pricing models (Sun et al., 2019; Wang et al., 2016; Cheng et al., 2024b), and utilization of high-occupancy vehicle (HOV) lanes (Cohen and Shaheen, 2018; Zafar et al., 2022), and etc. Besides for these, a new solution is proposed to explore the possibility of increasing the efficiency of information sharing between ride-hailing service providers. In this paper, San Francisco is chosen to be investigated, and Uber and Lyft are two companies that are used to conduct the research. The problem solution was inspired by the Wild Goose Chase Effects, in which high demand of service depletes the platform of idle drivers and vehicles have to be sent to pick up distant customer (Ouyang and Yang, 2023). As a result, time and resource wasted throughout the process erodes the drivers and customers’ surplus. This paper focuses on the suppliers’ side with operation of surge price system for welfare-maximizing purpose. The efficiency of information sharing and rebalancing are set to be important variables in this study to explore its significance of value. Perfect competition of the two agents can be beneficial to the carpooling market equilibrium. However, during peak hours, ‘wild goose chase’ can be a severe issue which indicates an inefficient allocation of resources. One strategy of solution is carpool, which releases more riding opportunities for the same amount of vehicles, as each vehicle could take up to 4 passengers and it is allowed to carry more than one trip at a time. Another strategy is on surge pricing which can be an effective way to solve the problem, but we presuppose cooperation between two service suppliers can reduce the peak hour pressure as well and meanwhile, relieve the congestion problem in the Bay Area. After referring to the traffic and ride-hailing conditions in the Bay Area, an analysis based in San Francisco is expected to be conducted in a simulation software named BEAM. The industry profit and user welfare will be analyzed, and the traffic conditions of the considered region after the simulation will also be used to determine the pros and cons of the co-partner operation. This study intends to answer whether or not, and in which conditions, does two carsharing suppliers work among a society can maximize the user benefits and their own profits. We aim to analyze the operating strategy and come up with implications for best social net benefits. Methods Considering the car-sharing situation in Berkeley, Uber and Lyft are operating separately with main service of individual car hailing and carpool. Regarding carpool as an effective way to reduce WGC effects and improve the net benefits of society, we can let certain portion of users for individual trip (e.g. UberX), and the left for carpool (e.g. UberPool). Initial design At initial stage, we set the idealized area of several blocks (n by n) with similar street condition as indicated in the graph above. If it was possible, we could also define each street with different road condition (i.e. capacity, free-floating speed, travel directions) and different intersection cycle time. Next, we define demand and supply. For supply, multi-agent system is adopted and i will set certain amount of users appearing in one unit time and pop out randomly in the design region. The destination is uniformly distributed inside region and is randomly assigned to each user (more than 1km). For the supply side, drivers are also distributed uniformly in the region at initial setting. In this case, we can analyze the effects of waiting time and en route distance/time by changing demand/ supply levels. When running the simulation model, we set the algorithm as, (1) if users wait more than 10 min, the trip will be cancelled; (2) if the distance between user and all the idle drivers is more than its maximum dispatch radius (i.e. 10km), the order will be declined; (3) if situation (2) doesn't happen, idle driver who is closest to the users will accept the order and go for the users in the minimum-distance routes; (4) when an occupied car drops its users, it will turn into to idle car again and move randomly inside the region until it receive order again.; (5) the shortest path algorithm is used to inform the driver about the best way to travel from origin to the destination. Figure 1 exhibits our algorithm for initial setting with a logic diagram. The algorithm in Figure 1 is designed for cooperated operation. For the independent operation, we will set two groups with same algorithm. We will adopt three measurements of effectiveness as, user waiting time (matching process), time wasted on pickups and trip monetary costs. Under this setting, the case study aims to investigate how the operation relation between two suppliers can affects users’ surplus and net benefits of society. Based on our understanding and relevant paperwork, we expect the following results: when too many cars enter a road, speed decreases to an extent that the total throughout of the road falls and correspondingly the average travel time will increase. At certain limit, the user waiting time and driver wasting time will be minimized. The suppliers are regarded as welfare-maximizing platform, so the cooperation strategy is supposed to reduce the benefits for supplier and can somehow increase user surplus. Finalized model design Although interesting at first about carpooling problem (route optimization, combination of services), our designs in such a way that, unfortunately, we are not able to clearly answer the whole picture of this problem, but fortunately, with the great help and support from BEAM team, we can handle the initial investigation into the questions. BEAM (The Modeling Framework for Behavior, Energy, Autonomy and Mobility) is a Multi-Modal Urban System extending the Multi-Agent Transportation Simulation Framework (MATSim) to enable powerful and scalable analysis of urban transportation systems. Compared to our original algorithm, the new model we designed is multi-agent transportation including walking, biking, driving (alone), walking to transit, driving to transit (park and ride), and ride hailing. Among these traffic modes, the car sharing system is modeled as ride hailing with a fleet of vehicles controlled by a centralized manager which responds to requests from customer and dispatches vehicle accordingly. Real data on household (information on individual, activity events, and travel plans), transit vehicles and schedules (real), parking and charging infrastructure (zone, type, price) and ride hail fleet. Objective function is to minimize users’ generalized cost (i.e. their cost and travel time). Individual agents will express preferences through a utility-maximizing evolutionary algorithm that minimizes each individual’s cost and time spent traveling via diverse modal options through several iterations until results is within acceptable confidence level. To progress the model, we designed general parameters (including number of agents, threshold for walking in meters, threshold for making parking choice in meters, mode choice parameters (i.e. utility parameter), vehicles and population, and ride hail management. Initial parameters of utility for multinominal logit model are designed as Equation (1), Where ASC (alternative specific constant) parameters as well as the Beta parameters can be configured in the BEAM configuration file and default to the following values: Through each iteration, the parameters will be calibrated to achieve maximum agent utility with formula as Equation (2), Useful outputs contain several documents of distribution of trip origin/destination, average travel time, energy use, leg histogram, passenger per trip, revenue, waiting time and trip distance with respect with ride hailing. Assumptions The two operational scenarios are based on the following assumptions: (1) Assume the fraction of driver between the two operators are 3.5:6.5 (adopted from the market report in 2017 for Uber and Lyft in San Francisco). (2) The initial number of users for a given supplier is proportional to the number of vehicles offered by the supplier. (3) There is no overlap among drivers in the two operators (i.e. no drivers will belong to Uber and Lyft at the same time) (4) Assume the two scenarios have the reposition strategy Default manager (operating independently): no reposition is adopted Low-waiting-time manager (operate together) The comparison of these two scenarios is presented in Table 1. Table 1. Comparison of two scenarios Scenario 1 Scenario 2 Two suppliers operate separately. Users calls for the cars on its own apps. Idle cars of selected supplier with minimum distance or estimated pickup time will go to for the users. Drivers of two suppliers are independent. The number of users selecting the two supplier is proportional to its price and coupon. Two suppliers operate corporately and share the information of users and location of drivers. Users calls for the cars on the combined app, no preference on the suppliers. Idle car with minimum distance or estimated pickup time will go to pick the users. Drivers of two suppliers are combined into one group. And they will have the same price level. Test Set-up Although interesting at first about carpooling problem (route optimization, combination of services), our designs in such a way that, unfortunately, we are not able to clearly answer the whole picture of this problem, but fortunately, with the great help and support from BEAM team, we can handle the initial investigation into the questions. BEAM (The Modeling Framework for Behavior, Energy, Autonomy and Mobility) is a Multi-Modal Urban System extending the Multi-Agent Transportation Simulation Framework (MATSim) to enable powerful and scalable analysis of urban transportation systems. BEAM is a joint effort between Lawrence Berkeley National Laboratory and the Institute for Transportation Studies at UC Berkeley. Compared to our original algorithm, the new model we designed is multi-agent transportation including walking, biking, driving (alone), walking to transit, driving to transit (park and ride), and ride hailing. Among these traffic modes, the car sharing system is modeled as ride hailing with a fleet of vehicles controlled by a centralized manager which responds to requests from customer and dispatches vehicle accordingly. Real data on household (information on individual, activity events, and travel plans), transit vehicles and schedules (real), parking and charging infrastructure (zone, type, price) and ride hail fleet. Objective function is to minimize users’ generalized cost (i.e. their cost and travel time). Individual agents will express preferences through a utility-maximizing evolutionary algorithm that minimizes each individual’s cost and time spent traveling via diverse modal options through several iterations until results is within acceptable confidence level. Based on the conclusion of section 2.3, three factors are selected to be the variables for this study test, which are (1). the total population of the city (2). the fraction of total population as the driver (3). allocation mode Following this principle, 12 control tests were simulated according to the table shown below. For the determination of fraction of population as the driver, reports were referenced that 45 thousand people in San Francisco are working as ride-hailing driver with the total population in San Francisco 880 thousand. Due to the limitation of processing power, the population of San Francisco in this study is scaled to be thousands, where a variation of 1k, 5k and 10k is set to observe its sensitivity. Note for notation, LWT stands for low-waiting time strategy, where reposition function was inserted to help reallocate the idle drive to enhance the efficiency of information delivery. As a comparison, DEF represents default mode, where no reposition, function is operated. The details of test setting up is listed in Table 2. Table 2. Test Set-Up Test Case Population Fraction of Population Manager 1 1k 0.05 LWT 2 1k 0.0175 DEF 3 1k 0.0325 DEF 4 5k 0.05 LWT 5 5k 0.0175 DEF 6 5k 0.0325 DEF 7 10k 0.05 LWT 8 10k 0.0175 DEF 9 10k 0.0325 DEF 10 1k 0.05 DEF 11 5k 0.05 DEF 12 10k 0.05 DEF (Note: LWT- low waiting time manager; DEF-Default manager) Figure 2 shows the distribution of origins and destinations generated during a day according to different population size. Results and analysis In this section, we analyze the results of simulation model on relation of suppliers with population size of 1K, 5K and 10K. The passenger waiting time is calculated by filtering the ‘NumberOfPassenger’ equals to zero in the ‘event’ file, and calculate the time difference between the ride hail vehicle departure and arrival. The dead heading distance is calculated by the coordinates of the departing and arriving events. Comparison of user waiting time At initial stage, we set the idealized area of several blocks (n by n) with similar street condition as indicated in the graph above. If it was possible, we could also define each street with different road condition (i.e. capacity, free-floating speed, travel directions) and different intersection cycle time. Next, we define demand and supply. For supply, multi-agent system is adopted, and we will set certain amount of users appearing in one unit time and pop out randomly in the design region. The destination is uniformly distributed inside region and is randomly assigned to each user (more than 1km). For the supply side, drivers are also distributed uniformly in the region at initial setting. In this case, we can analyze the effects of waiting time and en route distance/time by changing demand/ supply levels. When running the simulation model, we set the algorithm as, The distribution of user waiting times are shown in Figure 3 as below: The graphs above show the waiting time of the passengers with complete information from both service supplier. In this case, the low-waiting time manager is assumed to maximize the sharing resources of the two service suppliers. It is realized that the in the shared-information scenario, the average waiting time is shorter than the separated operation scenario. For the population of 1k case, it shows that the ride hailing waiting time (% of longer waiting time) decreased for the combined supplier case. This is a valid statement as the shared information would lead to a higher efficiency of vehicles relocation and reduced resources consumed. Also, it is worth noting that total frequency of using ride hailing increased in the combined case with population of 1K, which is believed to be the result of unsaturated traffic condition of low travel demands (i.e. car, ride hail, transit). However, in the 5k and 10k cases, it follows the same pattern though, where the waiting time decreased (shown by increased green area), the total frequency of ride and hailing did not go up correspondingly. The potential reasons for this may be caused by the increased population, or the congestion problem limited by the traffic capacity. In a short summary, the combined supplied case can reduce the individual waiting time for ride hailing passengers, while the total frequency of using ride hailing may not change significantly, due to the limitations from road capacity and other traffic modes. Comparison of dead distance From the graphs shown in Figure 4, with reposition of the cars, the total traveling distance is slightly increased due to extra driving time. Either from Uber of Lyft to the combined supplier case, a decreased proportion in blue area could be clearly observed, which indicates the decrease of en route distance. This reduction is believed to be substituted by the green area, which is the reposition distance by re-allocation, though it is idle. Therefore, it is safe to conclude that by combining the suppliers, the waiting time will decrease thanks to the contribution of reposition. In other words, the wild goose chase is also partially solved in this case. However, it should be noted that for each supplier, the total profit would most likely decrease due to the lower revenue received from customer and cost associated with repositioning. Analysis on Supplier Revenue & User Welfare Based on results from the multi-agent logic choice model, the waiting time in the shared-information scenario is shorter which is beneficial to the system users. Figure 5 shows the distribution of supplier revenue. The pickup distance is also reduced due to better reposition strategy generated by the shared-information optimization. Shorter distance and lower waiting time indicate less cost of users and higher benefits. Therefore, it’s a user beneficial strategy. For the suppliers, with reference to the graphs above, the supplier profit seems to be reduced with the strategy. The reason is that the algorithm of surging price is assumed to be the same. As stated in the previous section, the idling time (reposition time) is increased in the shared-information scenario proposing a higher fuel and operation cost in the system. Therefore, the cost is increased in the strategy. In the independent operation scenario, during peak hours, surging price occurs to manipulate the demand instead of repositioning. When the total number of drivers is a constant, the controlled demand is the same in these two scenarios. The only difference is the surging price which indicates a higher revenue in the independent operation scenario. With reference to the profit equation (Profit = Revenue - Cost), the supplier profit is reduced with the strategy. However, the defections of the assumption are obvious: When two suppliers corporate and forms monopoly, the price can be taken by the suppliers themselves which means they can raise the price by whatever they want. Accordingly, the surging price algorithm in this system is not applicable. In addition, monopoly is undesirable by the government since it may break the balance of market. One of the alternative solutions is to set up a platform to manipulate the operation. The primary objective of the platform is to provide users with a shared-information system without forming monopoly in the operation of the system. The price can be kept the same level by the suppliers, and interfered and supervised by the government. After the implementation of the platform, the user welfare is expected to increase which may, at the same time, improve the net social benefits. In all, the user welfare is increased with controlled supplier profit. The net social benefits is not quantified and deterministic in the current stage and might be evaluated in the future works of this study. Discussion From the perspective of users, it is agreed that the user benefits will be increased when these two suppliers cooperate with each other, by sharing part of the information of operation. However, it is arguable whether the operator revenue would benefit from reposition, as it also leads to extra costs. Furthermore, it is realized that the surging pricing system could change dramatically after the combination as the service suppliers could charge according to the buyers’ willingness to pay under monopoly. Therefore, the revenue of the cooperation could even rise up under this new pricing strategy, though it cannot be reflected in this BEAM system. Also, this dilemma also applies to the total benefit, i.e. social surplus. As indicated by the results from simulation model, it shows that by combining the service supplier, the total waiting time and fare would be reduced for customers thanks to the reposition and corresponding increased efficiency. On the other side, it also shows that this cooperation would most likely affect the revenue and surplus of service supplier, as it largely reduces the revenue generated from surging, which accounts for around 20% of the total revenue. However, a defect was pointed out that the previous pricing system would not apply in the monopoly market, which is just the case of cooperation. Hence, i would state that the revenue or profit from the supplier would not decline, at least in this case. Besides, the limitation of simulation was also discussed in the previous section, which may affect the accuracy and reliability of the data. Hence, a corresponding regression analysis was conducted to test the sensitivity of the factors chosen in this model. It is also worth noting the existence of error in this study. For example, the utility values of traffic are believed to be a constant after combination of suppliers, to simplify our calculation. Besides, it is realized that the total population used in this test is far less than the real situation in San Francisco, due to the limitation from processing power. Considering the fixed capacity of road network, the real situation may differ from what has been concluded in this study. In addition, carpooling is one of the modes that we wished to introduce the BEAM system, while involvement of a new mode requires a comprehensive change in software architecture, which is not feasible in this study for the time being. There are several implications and lessons learnt from this result. First of all, the initiative of this study is to investigate the effect of combing suppliers in ride hailing market to enhance its efficiency of information sharing. In other words, it is designed to optimize the welfare on demand side (user/passenger). From this process, it is noted that such change would lead to a decrease in the total revenue on supply side (service supplier), which also explains why there is no reason for themselves to combine automatically. Due to the complexity of the study, the total surplus of the society remains unknown as too many factors are involved. However, it is still suggested to provide the subsidy from government to improve the traffic congestion problem. Ride-hailing has become more and more popular in US traffic system and a suggestion has been proposed in this study to relieve the congestion problem associated with the ride-hailing. The assumption has been tested by utilizing the simulation software BEAM to prove its feasibility. Corresponding regression model and sensitivity analysis were also conducted to strengthen its reliability. Certain limitations have been introduced in the sections of assumption and discussion, and whether these factors would lead to a deviated result have been investigated. As a conclusion, it shows that the cooperation between ride hailing service suppliers would apparently benefit the customers/passengers as it can hugely reduce the waiting time and potential high price due to surging. For supplier side, it is still beneficial due to a new pricing strategy under monopoly, despite the increase cost raised by repositioning. Furthermore, the total surplus for the society remains unknown as it involves extra important factors, which is not included in the discussion of this study. For future improvement, it is suggested a more comprehensive test should be conducted to eliminate the minor errors introduced by the limitations. For example, the amount of the population should be close the real situation of San Francisco, though it requires powerful processing machines. A car-pooling model is also suggested to be included in the BEAM simulation system, where the results of this study may be referenced for the car-pooling research. Declarations Competing interests The authors declare no competing interests. Author Contribution K.L. conceived the experiment(s). K.L. and X.C. conducted the experiment(s). K.L. and X. H. performed statistical analysis. G. J. and C.C. prepared figures and tables. K. L. wrote the draft. X.C. and X. H. edited the manuscript. All authors reviewed the manuscript. Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. References Olayode, I. O., Severino, A., Alex, F. J., Macioszek, E., & Tartibu, L. K. (2023). Systematic review on the evaluation of the effects of ride-hailing services on public road transportation. Transportation research interdisciplinary perspectives, 22, 100943. Stiglic, M., Agatz, N., Savelsbergh, M., & Gradisar, M. (2015). The benefits of meeting points in ride-sharing systems. Transportation Research Part B: Methodological , 82 , 36-53. Cohen, M. C., Fiszer, M. D., Ratzon, A., & Sasson, R. (2023). Incentivizing commuters to carpool: A large field experiment with waze. Manufacturing & Service Operations Management , 25 (4), 1263-1284. Cheng, X., Mamalis, T., Bose, S., & Varshney, L. R. 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Simulation of a carpool system in an artificial road traffic network using NetLogo Gonçalo Homem de Almeida Correia. (2008). A conceptual model for carpooling systems simulation. Article in Journal of Simulation · March 2009 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-4954010\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":360901871,\"identity\":\"6f2e5966-acd9-47a6-aabe-cf071a46830a\",\"order_by\":0,\"name\":\"Ke 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design\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4954010/v1/fc20eceefbe78d3b32eb812f.png\"},{\"id\":65932522,\"identity\":\"39c60b20-eb5e-4876-bb77-fac5ced947af\",\"added_by\":\"auto\",\"created_at\":\"2024-10-04 14:20:24\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":678644,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eLayout of O-D distribution (1K, 5K, 10K)\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4954010/v1/a463d51e138ad0b4a7eea8ee.png\"},{\"id\":65932527,\"identity\":\"54aa24fe-45ba-4b37-af3e-d8b6d1cc1a2e\",\"added_by\":\"auto\",\"created_at\":\"2024-10-04 14:20:24\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1241960,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDistribution of user waiting time (1K, 5K, 10K)\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4954010/v1/2126c656a605f925472f7ea5.png\"},{\"id\":65933468,\"identity\":\"b29f4c4b-02a5-4f0b-8fba-47653d46d178\",\"added_by\":\"auto\",\"created_at\":\"2024-10-04 14:28:24\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":770957,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDistribution of dead distance (1K, 5K, 10K)\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4954010/v1/6dc80b1e89f7a61e69afccab.png\"},{\"id\":65932523,\"identity\":\"75e15fc5-1d1e-4005-9e89-b58d4a00d570\",\"added_by\":\"auto\",\"created_at\":\"2024-10-04 14:20:24\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":773416,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDistribution of supplier revenue (1K, 5K, 10K)\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4954010/v1/9c8647230828aa48c8790390.png\"},{\"id\":67321441,\"identity\":\"d08615b0-ed73-4f2d-9b4f-be8b86ff5a04\",\"added_by\":\"auto\",\"created_at\":\"2024-10-23 15:46:39\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":3452799,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4954010/v1/d9bc5fd7-709e-48b2-9c20-c86021a2ae6a.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Strategic Cooperation in Ride-Hailing: Analyzing Operational Efficiency in San Francisco\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eRide-hailing has become an important component in US transportation system and high vacancy rate of ride-hailing is regarded as one of the major reasons for congestion problem (Olayode et al, 2023). Plenty of feasible suggestions have been raised to relieve the congestion caused by ride-hailing services, such as encouraging carpooling (Stiglic et al., 2015; Cohen et al., 2023; Cheng et al., 2024a), implementing dedicated ride-hailing zones (Ranjbari et al., 2021; Wei et al., 2022), dynamic pricing models (Sun et al., 2019; Wang et al., 2016; Cheng et al., 2024b), and utilization of high-occupancy vehicle (HOV) lanes (Cohen and Shaheen, 2018; Zafar et al., 2022), and etc. Besides for these, a new solution is proposed to explore the possibility of increasing the efficiency of information sharing between ride-hailing service providers. In this paper, San Francisco is chosen to be investigated, and Uber and Lyft are two companies that are used to conduct the research.\\u003c/p\\u003e\\n\\u003cp\\u003eThe problem solution was inspired by the Wild Goose Chase Effects, in which high demand of service depletes the platform of idle drivers and vehicles have to be sent to pick up distant customer (Ouyang and Yang, 2023). As a result, time and resource wasted throughout the process erodes the drivers and customers\\u0026rsquo; surplus. This paper focuses on the suppliers\\u0026rsquo; side with operation of surge price system for welfare-maximizing purpose. The efficiency of information sharing and rebalancing are set to be important variables in this study to explore its significance of value.\\u003c/p\\u003e\\n\\u003cp\\u003ePerfect competition of the two agents can be beneficial to the carpooling market equilibrium. However, during peak hours, \\u0026lsquo;wild goose chase\\u0026rsquo; can be a severe issue which indicates an inefficient allocation of resources. One strategy of solution is carpool, which releases more riding opportunities for the same amount of vehicles, as each vehicle could take up to 4 passengers and it is allowed to carry more than one trip at a time. Another strategy is on surge pricing which can be an effective way to solve the problem, but we presuppose cooperation between two service suppliers can reduce the peak hour pressure as well and meanwhile, relieve the congestion problem in the Bay Area. After referring to the traffic and ride-hailing conditions in the Bay Area, an analysis based in San Francisco is expected to be conducted in a simulation software named BEAM. The industry profit and user welfare will be analyzed, and the traffic conditions of the considered region after the simulation will also be used to determine the pros and cons of the co-partner operation.\\u003c/p\\u003e\\n\\u003cp\\u003eThis study intends to answer whether or not, and in which conditions, does two carsharing suppliers work among a society can maximize the user benefits and their own profits. We aim to analyze the operating strategy and come up with implications for best social net benefits.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003eConsidering the car-sharing situation in Berkeley, Uber and Lyft are operating separately with main service of individual car hailing and carpool. Regarding carpool as an effective way to reduce WGC effects and improve the net benefits of society, we can let certain portion of users for individual trip (e.g. UberX), and the left for carpool (e.g. UberPool).\\u003c/p\\u003e\\n\\u003cp\\u003eInitial design\\u003c/p\\u003e\\n\\u003cp\\u003eAt initial stage, we set the idealized area of several blocks (n by n) with similar street condition as indicated in the graph above. If it was possible, we could also define each street with different road condition (i.e. capacity, free-floating speed, travel directions) and different intersection cycle time. Next, we define demand and supply. For supply, multi-agent system is adopted and i will set certain amount of users appearing in one unit time and pop out randomly in the design region. The destination is uniformly distributed inside region and is randomly assigned to each user (more than 1km). For the supply side, drivers are also distributed uniformly in the region at initial setting. In this case, we can analyze the effects of waiting time and en route distance/time by changing demand/ supply levels. When running the simulation model, we set the algorithm as,\\u003c/p\\u003e\\n\\u003cp\\u003e(1) if users wait more than 10 min, the trip will be cancelled;\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e(2) if the distance between user and all the idle drivers is more than its maximum dispatch radius (i.e. 10km), the order will be declined;\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e(3) if situation (2) doesn\\u0026apos;t happen, idle driver who is closest to the users will accept the order and go for the users in the minimum-distance routes;\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e(4) when an occupied car drops its users, it will turn into to idle car again and move randomly inside the region until it receive order again.;\\u003c/p\\u003e\\n\\u003cp\\u003e(5) the shortest path algorithm is used to inform the driver about the best way to travel from origin to the destination.\\u003c/p\\u003e\\n\\u003cp\\u003eFigure 1 exhibits our algorithm for initial setting with a logic diagram.\\u003c/p\\u003e\\n\\u003cp\\u003eThe algorithm in Figure 1 is designed for cooperated operation. For the independent operation, we will set two groups with same algorithm. We will adopt three measurements of effectiveness as, user waiting time (matching process), time wasted on pickups and trip monetary costs. Under this setting, the case study aims to investigate how the operation relation between two suppliers can affects users\\u0026rsquo; surplus and net benefits of society. Based on our understanding and relevant paperwork, we expect the following results:\\u003c/p\\u003e\\n\\u003cul\\u003e\\n \\u003cli\\u003ewhen too many cars enter a road, speed decreases to an extent that the total throughout of the road falls and correspondingly the average travel time will increase.\\u0026nbsp;\\u003c/li\\u003e\\n \\u003cli\\u003eAt certain limit, the user waiting time and driver wasting time will be minimized.\\u0026nbsp;\\u003c/li\\u003e\\n \\u003cli\\u003eThe suppliers are regarded as welfare-maximizing platform, so the cooperation strategy is supposed to reduce the benefits for supplier and can somehow increase user surplus.\\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003eFinalized model design\\u003c/p\\u003e\\n\\u003cp\\u003eAlthough interesting at first about carpooling problem (route optimization, combination of services), our designs in such a way that, unfortunately, we are not able to clearly answer the whole picture of this problem, but fortunately, with the great help and support from BEAM team, we can handle the initial investigation into the questions. BEAM (The Modeling Framework for Behavior, Energy, Autonomy and Mobility) is a Multi-Modal Urban System extending the Multi-Agent Transportation Simulation Framework (MATSim) to enable powerful and scalable analysis of urban transportation systems.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eCompared to our original algorithm, the new model we designed is multi-agent transportation including walking, biking, driving (alone), walking to transit, driving to transit (park and ride), and ride hailing. Among these traffic modes, the car sharing system is modeled as ride hailing with a fleet of vehicles controlled by a centralized manager which responds to requests from customer and dispatches vehicle accordingly. Real data on household (information on individual, activity events, and travel plans), transit vehicles and schedules (real), parking and charging infrastructure (zone, type, price) and ride hail fleet. Objective function is to minimize users\\u0026rsquo; generalized cost (i.e. their cost and travel time). Individual agents will express preferences through a utility-maximizing evolutionary algorithm that minimizes each individual\\u0026rsquo;s cost and time spent traveling via diverse modal options through several iterations until results is within acceptable confidence level.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eTo progress the model, we designed general parameters (including number of agents, threshold for walking in meters, threshold for making parking choice in meters, mode choice parameters (i.e. utility parameter), vehicles and population, and ride hail management. Initial parameters of utility for multinominal logit model are designed as Equation (1),\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cimg src=\\\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAhYAAAAjCAYAAADIb0PkAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAA+ZSURBVHhe7Z3LjxXFF8eL3x6QwRUhLhQXBBITFTSIJJDggGzYIIa4MIHEwMJEia8BVjwMxLAgBHBBwoLwkLADQU1kgRIENBo1LhBjXLAC4T/gdz81/R3OLav7dt+5M9zhnk/SuV3V9Th16tSj63Z3TXvQIjiO4ziO4/SA/xW/juM4juM448YnFo7jOI7j9AyfWDiO4ziO0zN8YuE4juM4Ts/wiYXjOI7jOFm+++67sGXLlsKV59SpU2Hfvn2FKzOx4OK0adPiYQPKj2Oq8Ndff0V5UYzl7t27Ydu2bWFoaGisTG+++WZxdRTCoEyF4Zc4/YitMx3Ia+vPGVxWrVr1H/uYN29e+PLLL4sQjtPOVO5TGOSwb2TG9p3uQZe7du0Khw4dKnwejqtcE4yff//998MJCK+bWu7cufPgmWeeefDiiy8WPqMcOXKE11If/Pzzz4VPNZs3b36wfv36wvVoGB4ejmU5efJk4TPKyMhILN+tW7eim+tWVvxnzZoV/ThHJ4Tfu3dvEaKabsuOfi9fvly4moF8lEs0rS/KmDGHvmIqyFjF+fPno012A7bXbVzpjfzlxkax8bpgW3Xtvw7Ya87W8cd2pwpldYq+HnX/h37H016mYp9CfWDXyEj5u20zzug4yPhpYaykTjnScRXoV2KYwt0GlZFWCBF62bFMNBgVjYJypHJjeFVlyZWf8DlF9hIqq9uJBQaggQMwiiadALqiI+lnpoKMZdDJYlO2o24C9pfaZF1kC8ggsOW6EwtNtK19jQfSSeWZioy3Tiea8U4splqfovpgrHLGD7osG/Owg9w12UjW6qgY24lhSE0MJCbcOh5lx4G8MrR0EsGdBNdRQooaY93GkzKeshOvm4mFKlOovtIGhj9lJywDhQyDsJKbw85SkUfXpcs6Awx5EV5p2rtQzslDeVldk7auceiur0rGTlhbrgvlREfkZQdgdC0dcthBhTpH51ZGyoO//HQ07fzGM7Ggnm1cZLL1L8hDuic8chM2ld3Gs3WJLtBNle0TJk0PnardqV5JAxmQk+vkyTnXuZa6Befolmuk12TQR4a0ryiDfGwZOFSncqt/kazIonJxHd2lbgu6l91TzqZ9knTaDf3Wp5AP6RNPbtKUPJLXHuRjyelT+idta2f4DTJp/adwTXWdgm6zMdNOjPO0kqqgAq0hVSHjrzqaVjLyq9DETTsLjAkDsw1BYKgy3m5oUvYUytpEz4IypDpLy6yGqc6LTi6t47RjIw5pyV/5oL8qcnlJz8jFNZUz1beNR5hOMlZB2shCPBoKv51kB8lEHMKrPjlHPq5zrsanThHZCMs1DjpcW5+ERZ5uSNtkE5CXvHVQhrQjT8tMOMnOL3FSKG9VXZZBWTRhtGAnpKFzZCE92Y/kIox1K3/chOc655SR650gPnnwi2yUK20/ZZB+Wqe2D6AMpI0f9SfZ8LOy4rb1S/msbglbVyZB3Drlz6G2bo80/1w7t2XgPG2vxCEt+SsfdFAFaROGsJRLerPoOjpPKdMn6crOZFfUFeeDDHqoGsfQM2FyoLuOEwsio/wmED/XcUwGGJc1buQokwU5ZagCZaYNqAlNyk7enY46UJG2jpCBRmQbHjpRuWg4aUdFXmkHSRzbwGyHWUUaTyAPclmDJE1bTtInbtppQE7GKkiDDow0Ka+t5zLUGec6J/SVlt/qkXLZzsuCH9fqQprIUXXY+qsCmdEzoBPs07YRycY1yk261n5wpzbNtbQuscFcvaeQdzrgAP5pZ5WW07qRAbfqCn+VCztJy1kFOkB2yqRBrBNldYocqR7wS3Vq3VZ30i3pc15lVxauyzbKjrr6QJZ+6lMEeRA+1w+QFjKm1NEnslrZBx10VFUv6Cttq4L6rZxYUAkkbo2pDsTNdRyTQSxU0Yh0VDWmXGMoU1gdxlN28u7UeeSgDKnMNi3qT7rgQEYbnnBpg1QcK0+dgUPxyho+16w9kSbyCwYJ5EMem0ZOxk6QFoMLNkw+OZlSCFc2MURO29mqrBq0gbpHzlRP2FhZunVQm2yKZLQTJXSJn6BMuDnQFbLb8DmbVl1acnaYg3i5usA/J6fsJXWngxL5c52Dc3Rmba0Kykc5OWx/UEVZnZJGqgf8rO2kbqs76ZYDWyIP2w7rktZzE3J1SVqSQ3alIy0z4XrVp1gIX9aOyq510qf0VNdWBgHqcjwTi+x3LBYuXBhft9yxY0c4cOBAmD17dnGlHl999VVYsGBB4aqGV0HtK025o+4rQ6T19ddf05LGjlbjL67maVq2TjQpey+gnm7cuBEWL15c+ISx1winT58ef//444/4K51cvHix7fXaK1eutMUHxVm6dGn8JZ/Dhw+HZcuWRXcZivfcc8/FX8tvv/0WWh1Wm85Pnz4d1q1bV7hCePrpp6N8n376aVi+fHnhm5exE5999ln45JNP4qtn77//fkyTclTx448/hueff75wtYOeX3nllcIVwjfffBNaHVV46aWXCp8Q3nnnnXDz5s1oh59//nnhG8K3335bmu5E8sMPP4RWBxH1Ki5cuBDlFpSZdoJt/Pnnn/HVMhs+Z9PUZWswKVyjbS+1wxx69Tu1D/yRyeb7+++/xzxkL6n7+++/D61BIp4D+bcGiViO69evhw8//LBW+ybvf/75J2zfvj2sWLEiPPXUU211V0ZZnaKv+fPnF65Rrl27FlavXl242t1qwytXroxu6ZZy/Pvvv/G1PrXDyaDf+hTxyy+/RFtFthy0uVx9dNJnaldOCHPnzi3OuiM7sZgxY0asPN5Xff311wvfeqjjqNsQCCfjLDsw2jq899578Z1bC+/WajChcWD8ctN50AksWbIkugEDO3bsWFucoaGheN6JpmXvBQwctkNGhnfffTcO4Oq858yZE39pmEAYO1mjg3ziiSdimXkPmXDqQAiLHRw9ejTcu3cvGhzf85B+UtK8aMT6/gcTVtmV8qKhb9y4MV5HJq7lyMnYCQZI6YBOA1k6dR7o8aeffornyIK9SCZ0eu7cuXiOXpDj448/Hku76h1/7GzmzJkxrcl8t57BF7kFcu7Zsye07ioKn/Yyo1/KoYFVNo09qMxAWRgcCY//iRMnxvyrvvfCgMNEh3jkoXw0EFGv8rt06VJ44YUX4jmkbgYZ8qNM6JV0GSRAdVfHTmivu3fvLlyj7+QzQexErk6lL7UTQAbajiYbqVsDLlB2JjZM8NARUD7pfTLotz4FCM+NAW361q1bMQ1rZ8Slb7F9ueikT+yKCaXzEOoXPaP3pjDBY+D+D1rGssuSdWE5qjU4F67Jg+WtVpnaltVYqsSPg+Uwlm5ajWPMj3P8LSzRIr/CoAe7ZFnFeMtOfnaJrhMs3dnycLB8hRzpsp6W6AmDjDYfLRVy3S4Do0v80S3pEY8wnf5SIH+ll8qia0rXpmX1Tjls3ZTJ2GuQh7zJC91aPdlr/Fo5tHQo+dFdrtxpuepCXk1tC3lV5zooU/q3Bu1cupd+reyyMcKovrguf4UnLn42bgp5SSarI9UveciPcFZXqRud4qclWerK1k/ZUm2vyNWpyoefdIW+0YtI3Ypjy662Jz110xejD+I3gfxVrzooS9qOYbL6FOJYuyIucawtKL8yqvSZpuWMQt2kbUg2ZXVpQa/4T8PROukZixYtCq+99lrbHcCgMMhldxzHeVSwesGdMn+BOb2B1QrGM1Z76sJKEH9tdbVXSO5zrxwsMbEcpaXtx5FBLrulTA+OAyyLp7YxmX8BOVOPbvsU/uLgWY2tW7cWPk4v4K8wHi3gGbU6UH/AX4k9WbGgYp988sn4HycPezZ9LmMqM8hldxzHeZTwrMWrr74aRkZGfKV4gkDHPENl9wtJ4caaB6B5YBp6/leI4ziO4ziDi2+b7jiO4zhOz/CJheM4juM4PcMnFo7jOI7j9AyfWDgDBQ/bxqeWiyfOeTDJcRzH6R0+sXAmBAbwOq+KTTa88jg0NBS/6Do8/PCT1I7jOE5v8LdCnAmhHz9Yw+fZ16xZEz9Vq88VO47jOL3FVyz6mCYfFOI9Yu7E9c19faBIHy3hW/186IQwFtzaYIi4xMFtw/N3Qequgi+Qsh8FHwwjPeJJJuLzRTfJa8tIusQlDv7I3kk2XQfO8SccB395CL4Ix6QC+OZITrfkp/hcR2blhVwgOaxeCas8CdfN9/Udx3EeF3xi0WfoGQB+gcHWDpA5GMh+/fXXcPz48bgxE7vS8jETPhrDZkDA5kZnz56Nm/4IBkXc7M7JQM/OncRho60vvvgifsqVQfjgwYNtbjaMqoJViuHh4XDkyJH4lwPx9u/fH2UiPruOAvJq5QBZ+NDNpk2bYpy33347fPTRR6WyaddOdi4Ub731Vvjggw9i/MuXL4f79+8XV0Y3JCP+5s2b4/V0YzsmHmfOnImrLHfu3IkbTLE5FHrDj0kSk4o33ngjpsPGRsDGSGy2RJqshKBPXw1xHGegaXWITp/BRi5s3MPmOK0BuvYGRIqjzXpws2GP0CZNIt0MCciPQ6RuwtfZ3AnTym0shL/dlEiQh93Qhk2BrKyQk40yCMKTRrpZkkB2G16gF+nN6l7paOMdflPIj3Tr1pHjOM7jjq9Y9CG3b9+O21iz1M820WzNXAdWJ7gj19bguNeuXRvP4cKFC3FTGcEqgXUDd+pskSysm1UU7tzZ3roKVllaA/PYFstCf6Gk+6mQLvls2LCh8Bnd7ruObCtXrixco9v1snrz7LPPxrJZJPvLL79c+DwEvbDSwKfZyZOt9gkrPbJC05o8ZLfD37lzZ4zD6gkrK47jOANPMcFw+gTuku2Wwtz12zv5KrjL1l018aleeydtVxtIl+t2u2D5idRNWNLoBHf8dmVBsFqQ89eKgEB2ymJXRnKypCsagnyIbyG8jW9BptwqiqA+qq6D5OPXcRxnkPEViz6Du2TufHW3zF1/1eYvQs9LzJ8/P7p5PgBY7eDZAOAufMaMGTEszykAbq5zR3/16tV4Zy5SN88zIBfx7UOTKayU8NwBafLsglYPLl26FFasWBHPLdOnT4+/rGiw4nD06NFYlrlz55bKhiw8VEnaPAfBA5RVD02yAtKaQBSudngmghUiIC8ezCRNwUrIkiVLCtdDKJtWYRzHcZyCYoLhTHHS5yVYqeCuHT89K8DKB1Wu5xC4UyeM7rK5Mx8ZGYnnkLp112/9cigcads7fdxcyyHZ7GpNlWzKQ6s5hMfNwUpGmg96KFt1QFeKL5mlM61EyG1BHsLbeI7jOIOOf8fCeexhFYLnJ1qTDd/W3nEcZ4Lxv0KcruAvAH27wR79+NcAf63wcKVPKhzHcSaaEP4Ppk4eXxRkF5sAAAAASUVORK5CYII=\\\" width=\\\"534\\\" height=\\\"35\\\"\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWhere ASC (alternative specific constant) parameters as well as the Beta parameters can be configured in the BEAM configuration file and default to the following values:\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cimg src=\\\"data:image/png;base64,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\\\" width=\\\"569\\\" height=\\\"251\\\"\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThrough each iteration, the parameters will be calibrated to achieve maximum agent utility with formula as Equation (2),\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cimg src=\\\"data:image/png;base64,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\\\" width=\\\"541\\\" height=\\\"176\\\"\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eUseful outputs contain several documents of distribution of trip origin/destination, average travel time, energy use, leg histogram, passenger per trip, revenue, waiting time and trip distance \\u0026nbsp;with respect with ride hailing.\\u003c/p\\u003e\\n\\u003cp\\u003eAssumptions\\u003c/p\\u003e\\n\\u003cp\\u003eThe two operational scenarios are based on the following assumptions:\\u003c/p\\u003e\\n\\u003cp\\u003e(1) Assume the fraction of driver between the two operators are 3.5:6.5 (adopted from the market report in 2017 for Uber and Lyft in San Francisco).\\u003c/p\\u003e\\n\\u003cp\\u003e(2) The initial number of users for a given supplier is proportional to the number of vehicles offered by the supplier.\\u003c/p\\u003e\\n\\u003cp\\u003e(3) There is no overlap among drivers in the two operators (i.e. no drivers will belong to Uber and Lyft at the same time)\\u003c/p\\u003e\\n\\u003cp\\u003e(4) \\u0026nbsp; Assume the two scenarios have the reposition strategy\\u003c/p\\u003e\\n\\u003cp\\u003eDefault manager (operating independently): no reposition is adopted\\u003c/p\\u003e\\n\\u003cp\\u003eLow-waiting-time manager (operate together)\\u003c/p\\u003e\\n\\u003cp\\u003eThe comparison of these two scenarios is presented in Table 1.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 1. Comparison of two scenarios\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"608\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47.3684%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eScenario 1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 52.6316%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eScenario 2\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47.3684%;\\\"\\u003e\\n \\u003cul\\u003e\\n \\u003cli\\u003eTwo suppliers operate separately.\\u003c/li\\u003e\\n \\u003cli\\u003eUsers calls for the cars on its own apps.\\u003c/li\\u003e\\n \\u003cli\\u003eIdle cars of selected supplier with minimum distance or estimated pickup time will go to for the users.\\u003c/li\\u003e\\n \\u003cli\\u003eDrivers of two suppliers are independent.\\u003c/li\\u003e\\n \\u003cli\\u003eThe number of users selecting the two supplier is proportional to its price and coupon.\\u003c/li\\u003e\\n \\u003c/ul\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 52.6316%;\\\"\\u003e\\n \\u003cul\\u003e\\n \\u003cli\\u003eTwo suppliers operate corporately and share the information of users and location of drivers.\\u0026nbsp;\\u003c/li\\u003e\\n \\u003cli\\u003eUsers calls for the cars on the combined app, no preference on the suppliers.\\u003c/li\\u003e\\n \\u003cli\\u003eIdle car with minimum distance or estimated pickup time will go to pick the users.\\u003c/li\\u003e\\n \\u003cli\\u003eDrivers of two suppliers are combined into one group. And they will have the same price level.\\u0026nbsp;\\u003c/li\\u003e\\n \\u003c/ul\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eTest Set-up\\u003c/p\\u003e\\n\\u003cp\\u003eAlthough interesting at first about carpooling problem (route optimization, combination of services), our designs in such a way that, unfortunately, we are not able to clearly answer the whole picture of this problem, but fortunately, with the great help and support from BEAM team, we can handle the initial investigation into the questions. BEAM (The Modeling Framework for Behavior, Energy, Autonomy and Mobility) is a Multi-Modal Urban System extending the Multi-Agent Transportation Simulation Framework (MATSim) to enable powerful and scalable analysis of urban transportation systems. BEAM is a joint effort between Lawrence Berkeley National Laboratory and the Institute for Transportation Studies at UC Berkeley.\\u003c/p\\u003e\\n\\u003cp\\u003eCompared to our original algorithm, the new model we designed is multi-agent transportation including walking, biking, driving (alone), walking to transit, driving to transit (park and ride), and ride hailing. Among these traffic modes, the car sharing system is modeled as ride hailing with a fleet of vehicles controlled by a centralized manager which responds to requests from customer and dispatches vehicle accordingly. Real data on household (information on individual, activity events, and travel plans), transit vehicles and schedules (real), parking and charging infrastructure (zone, type, price) and ride hail fleet. Objective function is to minimize users\\u0026rsquo; generalized cost (i.e. their cost and travel time). Individual agents will express preferences through a utility-maximizing evolutionary algorithm that minimizes each individual\\u0026rsquo;s cost and time spent traveling via diverse modal options through several iterations until results is within acceptable confidence level.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eBased on the conclusion of section 2.3, three factors are selected to be the variables for this study test, which are\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e(1). the total population of the city\\u003c/p\\u003e\\n\\u003cp\\u003e(2). the fraction of total population as the driver\\u003c/p\\u003e\\n\\u003cp\\u003e(3). allocation mode\\u003c/p\\u003e\\n\\u003cp\\u003eFollowing this principle, 12 control tests were simulated according to the table shown below. For the determination of fraction of population as the driver, reports were referenced that 45 thousand people in San Francisco are working as ride-hailing driver with the total population in San Francisco 880 thousand. Due to the limitation of processing power, the population of San Francisco in this study is scaled to be thousands, where a variation of 1k, 5k and 10k is set to observe its sensitivity. Note for notation, LWT stands for low-waiting time strategy, where reposition function was inserted to help reallocate the idle drive to enhance the efficiency of information delivery. As a comparison, DEF represents default mode, where no reposition, function is operated. \\u0026nbsp;The details of test setting up is listed in Table 2.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2. Test Set-Up\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"624\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 17.1474%;\\\"\\u003e\\n \\u003cp\\u003eTest Case\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22.4359%;\\\"\\u003e\\n \\u003cp\\u003ePopulation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 30.609%;\\\"\\u003e\\n \\u003cp\\u003eFraction of Population\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 29.8077%;\\\"\\u003e\\n \\u003cp\\u003eManager\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 17.1474%;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22.4359%;\\\"\\u003e\\n \\u003cp\\u003e1k\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 30.609%;\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 29.8077%;\\\"\\u003e\\n \\u003cp\\u003eLWT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 17.1474%;\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22.4359%;\\\"\\u003e\\n \\u003cp\\u003e1k\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 30.609%;\\\"\\u003e\\n \\u003cp\\u003e0.0175\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 29.8077%;\\\"\\u003e\\n \\u003cp\\u003eDEF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 17.1474%;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22.4359%;\\\"\\u003e\\n \\u003cp\\u003e1k\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 30.609%;\\\"\\u003e\\n \\u003cp\\u003e0.0325\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 29.8077%;\\\"\\u003e\\n \\u003cp\\u003eDEF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 17.1474%;\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22.4359%;\\\"\\u003e\\n \\u003cp\\u003e5k\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 30.609%;\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 29.8077%;\\\"\\u003e\\n \\u003cp\\u003eLWT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 17.1474%;\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22.4359%;\\\"\\u003e\\n \\u003cp\\u003e5k\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 30.609%;\\\"\\u003e\\n \\u003cp\\u003e0.0175\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 29.8077%;\\\"\\u003e\\n \\u003cp\\u003eDEF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 17.1474%;\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22.4359%;\\\"\\u003e\\n \\u003cp\\u003e5k\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 30.609%;\\\"\\u003e\\n \\u003cp\\u003e0.0325\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 29.8077%;\\\"\\u003e\\n \\u003cp\\u003eDEF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 17.1474%;\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22.4359%;\\\"\\u003e\\n \\u003cp\\u003e10k\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 30.609%;\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 29.8077%;\\\"\\u003e\\n \\u003cp\\u003eLWT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 17.1474%;\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22.4359%;\\\"\\u003e\\n \\u003cp\\u003e10k\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 30.609%;\\\"\\u003e\\n \\u003cp\\u003e0.0175\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 29.8077%;\\\"\\u003e\\n \\u003cp\\u003eDEF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 17.1474%;\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22.4359%;\\\"\\u003e\\n \\u003cp\\u003e10k\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 30.609%;\\\"\\u003e\\n \\u003cp\\u003e0.0325\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 29.8077%;\\\"\\u003e\\n \\u003cp\\u003eDEF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 17.1474%;\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22.4359%;\\\"\\u003e\\n \\u003cp\\u003e1k\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 30.609%;\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 29.8077%;\\\"\\u003e\\n \\u003cp\\u003eDEF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 17.1474%;\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22.4359%;\\\"\\u003e\\n \\u003cp\\u003e5k\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 30.609%;\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 29.8077%;\\\"\\u003e\\n \\u003cp\\u003eDEF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 17.1474%;\\\"\\u003e\\n \\u003cp\\u003e12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 22.4359%;\\\"\\u003e\\n \\u003cp\\u003e10k\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 30.609%;\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 29.8077%;\\\"\\u003e\\n \\u003cp\\u003eDEF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;(Note: LWT- low waiting time manager; DEF-Default manager)\\u003c/p\\u003e\\n\\u003cp\\u003eFigure 2 shows the distribution of origins and destinations generated during a day according to different population size.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Results and analysis\",\"content\":\"\\u003cp\\u003eIn this section, we analyze the results of simulation model on relation of suppliers with population size of 1K, 5K and 10K. The passenger waiting time is calculated by filtering the \\u0026lsquo;NumberOfPassenger\\u0026rsquo; equals to zero in the \\u0026lsquo;event\\u0026rsquo; file, and calculate the time difference between the ride hail vehicle departure and arrival. The dead heading distance is calculated by the coordinates of the departing and arriving events.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eComparison of user waiting time\\u003c/p\\u003e\\n\\u003cp\\u003eAt initial stage, we set the idealized area of several blocks (n by n) with similar street condition as indicated in the graph above. If it was possible, we could also define each street with different road condition (i.e. capacity, free-floating speed, travel directions) and different intersection cycle time. Next, we define demand and supply. For supply, multi-agent system is adopted, and we will set certain amount of users appearing in one unit time and pop out randomly in the design region. The destination is uniformly distributed inside region and is randomly assigned to each user (more than 1km). For the supply side, drivers are also distributed uniformly in the region at initial setting. In this case, we can analyze the effects of waiting time and en route distance/time by changing demand/ supply levels. When running the simulation model, we set the algorithm as,\\u003c/p\\u003e\\n\\u003cp\\u003eThe distribution of user waiting times are shown in Figure 3 as below:\\u003c/p\\u003e\\n\\u003cp\\u003eThe graphs above show the waiting time of the passengers with complete information from both service supplier. In this case, the low-waiting time manager is assumed to maximize the sharing resources of the two service suppliers.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eIt is realized that the in the shared-information scenario, the average waiting time is shorter than the separated operation scenario. For the population of 1k case, it shows that the ride hailing waiting time (% of longer waiting time) decreased for the combined supplier case. This is a valid statement as the shared information would lead to a higher efficiency of vehicles relocation and reduced resources consumed. Also, it is worth noting that total frequency of using ride hailing increased in the combined case with population of 1K, which is believed to be the result of unsaturated traffic condition of low travel demands (i.e. car, ride hail, transit). However, in the 5k and 10k cases, it follows the same pattern though, where the waiting time decreased (shown by increased green area), the total frequency of ride and hailing did not go up correspondingly. The potential reasons for this may be caused by the increased population, or the congestion problem limited by the traffic capacity. In a short summary, the combined supplied case can reduce the individual waiting time for ride hailing passengers, while the total frequency of using ride hailing may not change significantly, due to the limitations from road capacity and other traffic modes.\\u003c/p\\u003e\\n\\u003cp\\u003eComparison of dead distance\\u003c/p\\u003e\\n\\u003cp\\u003eFrom the graphs shown in Figure 4, with reposition of the cars, the total traveling distance is slightly increased due to extra driving time. Either from Uber of Lyft to the combined supplier case, a decreased proportion in blue area could be clearly observed, which indicates the decrease of en route distance. This reduction is believed to be substituted by the green area, which is the reposition distance by re-allocation, though it is idle. Therefore, it is safe to conclude that by combining the suppliers, the waiting time will decrease thanks to the contribution of reposition. In other words, the wild goose chase is also partially solved in this case. However, it should be noted that for each supplier, the total profit would most likely decrease due to the lower revenue received from customer and cost associated with repositioning.\\u003c/p\\u003e\\n\\u003cp\\u003eAnalysis on Supplier Revenue \\u0026amp; User Welfare\\u003c/p\\u003e\\n\\u003cp\\u003eBased on results from the multi-agent logic choice model, the waiting time in the shared-information scenario is shorter which is beneficial to the system users. Figure 5 shows the distribution of supplier revenue. The pickup distance is also reduced due to better reposition strategy generated by the shared-information optimization. Shorter distance and lower waiting time indicate less cost of users and higher benefits. Therefore, it\\u0026rsquo;s a user beneficial strategy.\\u003c/p\\u003e\\n\\u003cp\\u003eFor the suppliers, with reference to the graphs above, the supplier profit seems to be reduced with the strategy. The reason is that the algorithm of surging price is assumed to be the same. As stated in the previous section, the idling time (reposition time) is increased in the shared-information scenario proposing a higher fuel and operation cost in the system. Therefore, the cost is increased in the strategy. In the independent operation scenario, during peak hours, surging price occurs to manipulate the demand instead of repositioning. When the total number of drivers is a constant, the controlled demand is the same in these two scenarios. The only difference is the surging price which indicates a higher revenue in the independent operation scenario. With reference to the profit equation (Profit = Revenue - Cost), the supplier profit is reduced with the strategy.\\u003c/p\\u003e\\n\\u003cp\\u003eHowever, the defections of the assumption are obvious: When two suppliers corporate and forms monopoly, the price can be taken by the suppliers themselves which means they can raise the price by whatever they want. Accordingly, the surging price algorithm in this system is not applicable. In addition, monopoly is undesirable by the government since it may break the balance of market. One of the alternative solutions is to set up a platform to manipulate the operation.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe primary objective of the platform is to provide users with a shared-information system without forming monopoly in the operation of the system. The price can be kept the same level by the suppliers, and interfered and supervised by the government. After the implementation of the platform, the user welfare is expected to increase which may, at the same time, improve the net social benefits.\\u003c/p\\u003e\\n\\u003cp\\u003eIn all, the user welfare is increased with controlled supplier profit. The net social benefits is not quantified and deterministic in the current stage and might be evaluated in the future works of this study.\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eFrom the perspective of users, it is agreed that the user benefits will be increased when these two suppliers cooperate with each other, by sharing part of the information of operation. However, it is arguable whether the operator revenue would benefit from reposition, as it also leads to extra costs. Furthermore, it is realized that the surging pricing system could change dramatically after the combination as the service suppliers could charge according to the buyers\\u0026rsquo; willingness to pay under monopoly. Therefore, the revenue of the cooperation could even rise up under this new pricing strategy, though it cannot be reflected in this BEAM system. Also, this dilemma also applies to the total benefit, i.e. social surplus.\\u003c/p\\u003e \\u003cp\\u003eAs indicated by the results from simulation model, it shows that by combining the service supplier, the total waiting time and fare would be reduced for customers thanks to the reposition and corresponding increased efficiency. On the other side, it also shows that this cooperation would most likely affect the revenue and surplus of service supplier, as it largely reduces the revenue generated from surging, which accounts for around 20% of the total revenue. However, a defect was pointed out that the previous pricing system would not apply in the monopoly market, which is just the case of cooperation. Hence, i would state that the revenue or profit from the supplier would not decline, at least in this case. Besides, the limitation of simulation was also discussed in the previous section, which may affect the accuracy and reliability of the data. Hence, a corresponding regression analysis was conducted to test the sensitivity of the factors chosen in this model.\\u003c/p\\u003e \\u003cp\\u003eIt is also worth noting the existence of error in this study. For example, the utility values of traffic are believed to be a constant after combination of suppliers, to simplify our calculation. Besides, it is realized that the total population used in this test is far less than the real situation in San Francisco, due to the limitation from processing power. Considering the fixed capacity of road network, the real situation may differ from what has been concluded in this study. In addition, carpooling is one of the modes that we wished to introduce the BEAM system, while involvement of a new mode requires a comprehensive change in software architecture, which is not feasible in this study for the time being.\\u003c/p\\u003e \\u003cp\\u003eThere are several implications and lessons learnt from this result. First of all, the initiative of this study is to investigate the effect of combing suppliers in ride hailing market to enhance its efficiency of information sharing. In other words, it is designed to optimize the welfare on demand side (user/passenger). From this process, it is noted that such change would lead to a decrease in the total revenue on supply side (service supplier), which also explains why there is no reason for themselves to combine automatically. Due to the complexity of the study, the total surplus of the society remains unknown as too many factors are involved. However, it is still suggested to provide the subsidy from government to improve the traffic congestion problem.\\u003c/p\\u003e \\u003cp\\u003eRide-hailing has become more and more popular in US traffic system and a suggestion has been proposed in this study to relieve the congestion problem associated with the ride-hailing. The assumption has been tested by utilizing the simulation software BEAM to prove its feasibility. Corresponding regression model and sensitivity analysis were also conducted to strengthen its reliability. Certain limitations have been introduced in the sections of assumption and discussion, and whether these factors would lead to a deviated result have been investigated. As a conclusion, it shows that the cooperation between ride hailing service suppliers would apparently benefit the customers/passengers as it can hugely reduce the waiting time and potential high price due to surging. For supplier side, it is still beneficial due to a new pricing strategy under monopoly, despite the increase cost raised by repositioning. Furthermore, the total surplus for the society remains unknown as it involves extra important factors, which is not included in the discussion of this study.\\u003c/p\\u003e \\u003cp\\u003eFor future improvement, it is suggested a more comprehensive test should be conducted to eliminate the minor errors introduced by the limitations. For example, the amount of the population should be close the real situation of San Francisco, though it requires powerful processing machines. A car-pooling model is also suggested to be included in the BEAM simulation system, where the results of this study may be referenced for the car-pooling research.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e \\u003ch2\\u003eCompeting interests\\u003c/h2\\u003e \\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eK.L. conceived the experiment(s). K.L. and X.C. conducted the experiment(s). K.L. and X. H. performed statistical analysis. G. J. and C.C. prepared figures and tables. K. L. wrote the draft. X.C. and X. H. edited the manuscript. All authors reviewed the manuscript.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eOlayode, I. O., Severino, A., Alex, F. J., Macioszek, E., \\u0026amp; Tartibu, L. K. (2023). Systematic review on the evaluation of the effects of ride-hailing services on public road transportation. Transportation research interdisciplinary perspectives, 22, 100943.\\u003c/li\\u003e\\n\\u003cli\\u003eStiglic, M., Agatz, N., Savelsbergh, M., \\u0026amp; Gradisar, M. (2015). The benefits of meeting points in ride-sharing systems. \\u003cem\\u003eTransportation Research Part B: Methodological\\u003c/em\\u003e, \\u003cem\\u003e82\\u003c/em\\u003e, 36-53.\\u003c/li\\u003e\\n\\u003cli\\u003eCohen, M. C., Fiszer, M. D., Ratzon, A., \\u0026amp; Sasson, R. (2023). Incentivizing commuters to carpool: A large field experiment with waze. \\u003cem\\u003eManufacturing \\u0026amp; Service Operations Management\\u003c/em\\u003e, \\u003cem\\u003e25\\u003c/em\\u003e(4), 1263-1284.\\u003c/li\\u003e\\n\\u003cli\\u003eCheng, X., Mamalis, T., Bose, S., \\u0026amp; Varshney, L. R. (2024). On Carsharing Platforms With Electric Vehicles as Energy Service Providers. \\u003cem\\u003eIEEE Transactions on Intelligent Transportation Systems\\u003c/em\\u003e.\\u003c/li\\u003e\\n\\u003cli\\u003eRanjbari, A., Luis Machado-Le\\u0026oacute;n, J., Dalla Chiara, G., MacKenzie, D., \\u0026amp; Goodchild, A. (2021). Testing curbside management strategies to mitigate the impacts of ridesourcing services on traffic. \\u003cem\\u003eTransportation Research Record\\u003c/em\\u003e, \\u003cem\\u003e2675\\u003c/em\\u003e(2), 219-232.\\u003c/li\\u003e\\n\\u003cli\\u003eWei, K., Vaze, V., \\u0026amp; Jacquillat, A. (2022). Transit planning optimization under ride-hailing competition and traffic congestion. \\u003cem\\u003eTransportation Science\\u003c/em\\u003e, \\u003cem\\u003e56\\u003c/em\\u003e(3), 725-749.\\u003c/li\\u003e\\n\\u003cli\\u003eSun, L., Teunter, R. H., Babai, M. Z., \\u0026amp; Hua, G. (2019). Optimal pricing for ride-sourcing platforms. \\u003cem\\u003eEuropean Journal of Operational Research\\u003c/em\\u003e, \\u003cem\\u003e278\\u003c/em\\u003e(3), 783-795.\\u003c/li\\u003e\\n\\u003cli\\u003eWang, X., He, F., Yang, H., \\u0026amp; Gao, H. O. (2016). Pricing strategies for a taxi-hailing platform. \\u003cem\\u003eTransportation Research Part E: Logistics and Transportation Review\\u003c/em\\u003e, \\u003cem\\u003e93\\u003c/em\\u003e, 212-231.\\u003c/li\\u003e\\n\\u003cli\\u003eCheng, X., Nie, Y. M., \\u0026amp; Lin, J. (2024). An Autonomous Modular Public Transit service. \\u003cem\\u003eTransportation Research Part C: Emerging Technologies\\u003c/em\\u003e, 104746.\\u003c/li\\u003e\\n\\u003cli\\u003eCohen, A., \\u0026amp; Shaheen, S. (2018). \\u003cem\\u003ePlanning for shared mobility\\u003c/em\\u003e.\\u003c/li\\u003e\\n\\u003cli\\u003eZafar, F., Khattak, H. A., Aloqaily, M., \\u0026amp; Hussain, R. (2022). Carpooling in connected and autonomous vehicles: current solutions and future directions. \\u003cem\\u003eACM Computing Surveys (CSUR)\\u003c/em\\u003e, \\u003cem\\u003e54\\u003c/em\\u003e(10s), 1-36.\\u003c/li\\u003e\\n\\u003cli\\u003eOuyang, Y., \\u0026amp; Yang, H. (2023). Measurement and mitigation of the \\u0026ldquo;wild goose chase\\u0026rdquo; phenomenon in taxi services. \\u003cem\\u003eTransportation Research Part B: Methodological\\u003c/em\\u003e, \\u003cem\\u003e167\\u003c/em\\u003e, 217-234.\\u003c/li\\u003e\\n\\u003cli\\u003eCastillo, J., Knoepfle, D., \\u0026amp; Weyl, E. (2017). Surge Pricing Solves the Wild Goose Chase. SSRN Electronic Journal. doi: 10.2139/ssrn.2890666\\u003c/li\\u003e\\n\\u003cli\\u003eSt\\u0026acute;ephane Galland, Nicolas Gaud, and Ansar-Ul-Haque Yasar. (2013) Simulation Model of Carpooling with the Janus Multiagent Platform. The 2nd International Workshop on Agent-based Mobility, Trac and Transportation Models, Methodologies and Applications (ABMTRANS)\\u003c/li\\u003e\\n\\u003cli\\u003eAnt\\u0026acute;onio Pedro Fraga, Lu\\u0026acute;ıs Oliveira and Pedro Martin. (2014). Simulation of a carpool system in an artificial road traffic network using NetLogo\\u003c/li\\u003e\\n\\u003cli\\u003eGon\\u0026ccedil;alo Homem de Almeida Correia. (2008). A conceptual model for carpooling systems simulation. Article in Journal of Simulation \\u0026middot; March 2009\\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\":\"info@researchsquare.com\",\"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\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4954010/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4954010/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eRide-hailing services, such as Uber and Lyft, have become integral to urban transportation systems, offering convenience and flexibility to local residents. However, the expansion of these services can contribute to congestion and efficiency problems, particularly in densely populated cities like San Francisco. This study investigates the potential of information sharing between ride-hailing service providers to mitigate congestion and enhance operational efficiency. By leveraging the BEAM (Behavior, Energy, Autonomy, and Mobility) simulation platform, we model various scenarios in which Uber and Lyft either cooperate by sharing operational data or operate independently. The simulation examines key metrics such as passenger waiting time, vehicle repositioning, and the impact of surge pricing on user welfare and supplier profit. Our findings suggest that strategic cooperation between service providers can reduce passenger waiting times and improve resource allocation, though it may also lead to a reduction in supplier profits due to the decreased effectiveness of surge pricing. The results have significant implications for urban transportation policy and the optimization of ride-hailing operations, highlighting the trade-offs between user benefits and supplier profitability in a collaborative framework. Future research should explore the long-term societal impacts of such cooperation, including its effects on traffic congestion and overall social welfare.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Strategic Cooperation in Ride-Hailing: Analyzing Operational Efficiency in San Francisco\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-10-04 14:20:19\",\"doi\":\"10.21203/rs.3.rs-4954010/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"bacce073-c6df-45fe-a382-9165f7039494\",\"owner\":[],\"postedDate\":\"October 4th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":38396213,\"name\":\"Physical sciences/Engineering/Aerospace engineering\"},{\"id\":38396214,\"name\":\"Physical sciences/Engineering/Civil engineering\"},{\"id\":38396215,\"name\":\"Physical sciences/Engineering/Electrical and electronic engineering\"},{\"id\":38396216,\"name\":\"Physical sciences/Engineering/Energy infrastructure\"}],\"tags\":[],\"updatedAt\":\"2024-10-23T15:38:18+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-10-04 14:20:19\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4954010\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4954010\",\"identity\":\"rs-4954010\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}