网约共享出行研究综述

摘要/Abstract

摘要： 网约共享出行是智慧城市交通系统的重要组成部分，作为新兴的移动互联出行方式，产生了海量庞杂、异质多源、大尺度时空关联的交通大数据，蕴含能够描述复杂交通系统供需态势的丰富信息。从网约共享出行行为机理、平台管理优化、政府监管政策、系统仿真优化等4个方面，综述了国内外网约共享出行研究的基础理论前沿和交通运输管理实践成果，归纳总结了其中存在的问题。通过移动互联交通大数据，分析网约车乘客和司机的出行行为影响因素、特征辨识及外部性，追踪城市个体和群体的出行行为演变规律，揭示网约共享出行系统供需平衡和网络均衡机理。研究解决网约共享出行供需的时空效应及短时预测问题，优化网约共享出行平台定价策略，提高平台匹配和调度效率，实现供需时空资源的优化配置。利用智能体仿真、基于活动的仿真、数据驱动的仿真等技术手段对理论结果进行模拟推演和优化验证，为政府制定相关监管政策和平台优化运营管理策略提供理论依据和工具支持。并面向复杂动态移动互联环境，展望了亟须开展的若干重点研究方向。

关键词: 城市交通, 网约共享出行, 出行行为, 平台管理, 监管政策, 系统仿真

Abstract: App-based ridesharing mobility is an essential component of intelligent urban transportation systems. As an emerging mobile internet travel mode, it generates massive, complex, heterogeneous, multi-source, large-scale, and spatially-temporally correlated transportation big data, which contain rich information that can describe the supply and demand situations of complex transportation systems. This paper reviews the state- of-the- art and practical results of transportation management from the four aspects of app-based ridesharing mobility travel behavior mechanism, platform management optimization, government regulatory policies, and system simulation optimization, then summarizes the existing problems. Through the mobile internet transportation big data, the paper analyzes the influencing factors, characteristics identification, and the externalities of passengers' and ride- sourcing drivers' travel behaviors, tracks travel behavioral evolutions both on the individual and population levels, and reveals the balancing mechanism of supply and demand, as well as network equilibrium of the app-based ridesharing mobility system. We study to solve the spatial-temporal effect and short-term prediction problem of ridesharing demand and supply, optimize the pricing strategy of the app-based ride-sourcing platform, improve the platform matching and dispatching efficiency, and realize the optimal allocation of the spatial- temporal resources of supply and demand. By using the technologies of agent-based simulation, activity-based simulation, and data-driven simulation, the above results can be simulated and optimized to provide a theoretical basis and supporting tool for the government to formulate relevant regulatory policies and the platform to optimize operation management strategies. Finally, facing the complex dynamic mobile internet environment, we prospect some key research directions.

Key words: urban traffic, App- based ridesharing mobility, travel behavior, platform management, regulation policy, system simulation

中图分类号:

U121

陈喜群. 网约共享出行研究综述[J]. 交通运输系统工程与信息, 2021, 21(5): 77-90.

CHEN Xi-qun. Review of App-based Ridesharing Mobility Research[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 77-90.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract30310.shtml

http://www.tseit.org.cn/CN/Y2021/V21/I5/77

参考文献

[1] 中国互联网络信息中心. 第47次中国互联网络发展状况统计报告[R]. 北京: 中共中央网络安全和信息化委员会办公室, 中华人民共和国国家互联网信息办公室, 2021. [China Internet Network Information Center. The 47th China statistical report on internet development [R]. Beijing: Office of the Central Cyberspace Affairs Commission, Cyberspace Administration of China, 2021.]

[2] FARZAD A, GIOVANNI C, PATRICIA M, et al. What drives the use of ridehailing in California? Ordered probit models of the usage frequency of Uber and Lyft[J]. Transportation Research Part C: Emerging Technologies, 2019, 102: 233-248.

[3] CHEN X, ZAHIRI M, ZHANG S. Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach[J]. Transportation Research Part C: Emerging Technologies, 2017, 76: 51-70.

[4] LAVIERI P S, BHAT C R. Investigating objective and subjective factors influencing the adoption, frequency, and characteristics of ride-hailing trips[J]. Transportation Research Part C: Emerging Technologies, 2019, 105: 100-125.

[5] ACHEAMPONG R A, SIIBA A, OKYERE D K, et al. Mobility- on- demand: An empirical study of internetbased ride-hailing adoption factors, travel characteristics and mode substitution effects[J]. Transportation Research Part C: Emerging Technologies, 2020, 115: 102638.

[6] 姚荣涵, 梁亚林, 刘锴, 等. 考虑合乘的共享自动驾驶汽车选择行为实证分析[J]. 交通运输系统工程与信息, 2020, 20(1): 228-233. [YAO R H, LIANG Y L, LIU K, et al. Empirical analysis of choice behavior for shared autonomous vehicles with concern of ride-sharing [J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(1): 228-233.]

[7] CHEN X, CHEN C, NI L, et al. Spatial visitation prediction of on-demand ride services using the scaling law[J]. Physica A: Statistical Mechanics and Its Applications, 2018, 508: 84-94.

[8] 张政, 陈艳艳, 梁天闻. 基于网约车数据的城市区域出行时空特征识别与预测研究[J]. 交通运输系统工程与信息, 2020, 20(3): 89- 94. [ZHANG Z, CHEN Y Y, LIANG T W. Regional travel demand mining and forecasting using car-hailing order records[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(3): 89-94.]

[9] ZHU Z, SUN L, CHEN X, et al. Integrating probabilistic tensor factorization with Bayesian supervised learning for dynamic ridesharing pattern analysis[J]. Transportation Research Part C: Emerging Technologies, 2021, 124: 102916.

[10] YU J R, STETTLER M, ANGELOUDIS P, et al. Urban network-wide traffic speed estimation with massive ridesourcing GPS traces[J]. Transportation Research Part C: Emerging Technologies, 2020, 112: 136-152.

[11] WANG S, LI L, MA W, et al. Trajectory analysis for ondemand services: A survey focusing on spatio-temporal demand and supply patterns[J]. Transportation Research Part C: Emerging Technologies, 2019, 108: 74-99.

[12] CHEN X, ZHENG H, WANG Z, et al. Exploring impacts of on-demand ridesplitting on mobility via real-world ridesourcing data and questionnaires[J]. Transportation, 2018, DOI: 10.1007/s11116-018-9916-1.

[13] ZHENG H, CHEN X, CHEN X. How does on-demand ridesplitting influence vehicle use and purchase willingness? A case study in Hangzhou, China[J]. IEEE Intelligent Transportation Systems Magazine, 2019, 11 (3): 143-157.

[14] WANG Z, CHEN X, CHEN X. Ridesplitting is shaping young people's travel behavior: Evidence from comparative survey via ride-sourcing platform[J]. Transportation Research Part D: Transport and Environment, 2019, 75: 57-71.

[15] CRAMER J, KRUEGER A B. Disruptive change in the taxi business: The case of Uber[J]. American Economic Review, 2016, 106(5): 177-82.

[16] CHEN M K. Dynamic pricing in a labor market: Surge pricing and flexible work on the Uber platform[C]. In Proceedings of the 2016 ACM Conference on Economics and Computation, Maastricht, The Netherlands, 2016: 455.

[17] YU X, GAO S, HU X, et al. A Markov decision process approach to vacant taxi routing with e-hailing[J]. Transportation Research Part B: Methodological, 2019, 121: 114-134.

[18] YAO F G, ZHU J T, YU J R, et al. Hybrid operations of human-driving vehicles and automated vehicles with data-driven agent-based simulation[J]. Transportation Research Part D: Transport and Environment, 2020, 86: 102469.

[19] ZHA L, YIN Y, DU Y. Surge pricing and labor supply in the ride-sourcing market[J]. Transportation Research Part B: Methodological, 2018, 117: 708-722.

[20] KE J, CEN X, YANG H, et al. Modelling drivers' working and recharging schedules in a ride-sourcing market with electric vehicles and gasoline vehicles[J]. Transportation Research Part E: Logistics and Transportation Review, 2019, 125: 160-180.

[21] SUN H, WANG H, WAN Z. Model and analysis of labor supply for ride-sharing platforms in the presence of sample self-selection and endogeneity[J]. TransportationResearch Part B: Methodological, 2019, 125: 76-93.

[22] QIAN X, UKKUSURI S V. Taxi market equilibrium with third-party hailing service[J]. Transportation Research Part B: Methodological, 2017, 100: 43-63.

[23] 李梦, 黄海军. 考虑共享出行的用户均衡交通分配模型[J]. 系统工程理论与实践, 2019, 39(7): 1771-1780. [LI M, HUANG H J. A user equilibrium assignment model with ridesharing[J]. System Engineering-Theory & Practice, 2019, 39(7): 1771-1780.]

[24] 曹祎, 罗霞. 考虑手机召车软件的城市出租车网络均衡研究[J]. 交通运输系统工程与信息, 2016, 16(2): 70- 76. [CAO Y, LUO X. Equilibrium model of urban taxi service network with the influence of taxi-hailing applications[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(2): 70-76.]

[25] BAN X, DESSOUKY M, PANG J S, et al. A general equilibrium model for transportation systems with ehailing services and flow congestion[J]. Transportation Research Part B: Methodological, 2019, 129: 273-304.

[26] YU X, VAN DEN BERG V A C, VERHOEF E T. Carpooling with heterogeneous users in the bottleneck model[J]. Transportation Research Part B: Methodological, 2019, 127: 178-200.

[27] 田丽君, 刘会楠, 许岩. 共享自动驾驶汽车经营策略优化分析[J]. 交通运输系统工程与信息, 2020, 20(3): 6- 13. [TIAN L J, LIU H N, XU Y. Optimization and analysis on operating strategies of shared autonomous vehicles[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(3): 6- 13.]

[28] WANG H, YANG H. E-hailing ride-sourcing systems: A framework and review[J]. Transportation Research Part B: Methodological, 2019, 129: 122-155.

[29] AFECHE P, MENDELSON H. Pricing and priority auctions in queueing systems with a generalized delay cost structure[J]. Management Science, 2004, 50(7): 869- 882.

[30] ZHOU W, CHAO X, GONG X. Optimal uniform pricing strategy of a service firm when facing two classes of customers[J]. Production and Operations Management, 2014, 23(4): 676-688.

[31] BAI J, SO K C, TANG C, et al. Coordinating supply and demand on an on-demand service platform with impatient customers[J]. Manufacturing & Service Operations Management, 2019, 21(3): 556-570.

[32] KE J, YANG H, LI X, et al. Pricing and equilibrium in on-demand ride-pooling markets[J]. Transportation Research Part B: Methodological, 2020, 139: 411-431.

[33] NOURINEJAD M, RAMEZANI M. Ride-sourcing modeling and pricing in non-equilibrium two-sided markets[J]. Transportation Research Part B: Methodological, 2020, 132: 340-357.

[34] CHEN X, ZHENG H, KE J, et al. Dynamic optimization strategies for on-demand ride services platform: Surge pricing, commission rate, and incentives[J]. Transportation Research Part B: Methodological, 2020, 138: 23-45.

[35] YANG H, SHAO C, WANG H, et al. Integrated reward scheme and surge pricing in a ride-sourcing market[J]. Transportation Research Part B: Methodological, 2020, 134: 126-142.

[36] MA J, XU M, MENG Q, et al. Ridesharing user equilibrium problem under OD-based surge pricing strategy[J]. Transportation Research Part B: Methodological, 2020, 134: 1-24.

[37] MASOUD N, JAYAKRISHNAN R. A real-time algorithm to solve the peer-to-peer ride-matching problem in a flexible ridesharing system[J]. Transportation Research Part B: Methodological, 2017, 106: 218-236.

[38] YANG H, QIN X, KE J, et al. Optimizing matching time interval and matching radius in on-demand ride-sourcing markets[J]. Transportation Research Part B: Methodological, 2020, 131: 84-105.

[39] SUN L, TEUNTER R H, HUA G, et al. Taxi- hailing platforms: Inform or assign drivers?[J]. Transportation Research Part B: Methodological, 2020, 142: 197-212.

[40] SANTI P, RESTA G, SZELL M, et al. Quantifying the benefits of vehicle pooling with shareability networks[J]. Proceedings of the National Academy of Sciences of the USA, 2014, 111: 13290-13294.

[41] VAZIFEH M M, SANTI P, RESTA G, et al. Addressing the minimum fleet problem in on-demand urban mobility [J]. Nature, 2018, 557: 534-538.

[42] KE J, ZHENG H, YANG H, et al. Short-term forecasting of passenger demand under On-demand ride services: A spatio-temporal deep learning approach[J]. Transportation Research Part C: Emerging Technologies, 2017, 85: 591-608.

[43] KE J, YANG H, ZHENG H, et al. Hexagon-based convolutional neural network for supply-demand forecasting of ride-sourcing services[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(11): 4160-4173.

[44] 谷远利, 李萌, 芮小平, 等. 基于深度学习的网约车供需缺口短时预测研究[J]. 交通运输系统工程与信息, 2019, 19(2): 223-230. [GU Y L, LI M, RUI X P, et al. Short-term forecasting of supply-demand gap under online car-hailing services based on deep learning[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(2): 223-230.]

[45] ZHA L, YIN Y, YANG H. Economic analysis of ridesourcing markets[J]. Transportation Research Part C: Emerging Technologies, 2016, 71: 249-266.

[46] BELLEFLAMME P, PEITZ M. Platform competition: Who benefits from multihoming?[J]. International Journal of Industrial Organization, 2019, 64: 1-26.

[47] 卢珂, 周晶, 林小围. 考虑交叉网络外部性的网约车平台市场定价研究[J]. 运筹与管理, 2019, 28(7): 169- 178. [LU K, ZHOU J, LIN X W. Pricing research of ridehailing platform: From the view of inter- group network externality[J]. Operations Research and Management Science, 2019, 28(7): 169-178.]

[48] BRYAN K A, GANS J S. A theory of multihoming in rideshare competition[J]. Journal of Economics & Management Strategy, 2019, 28(1): 89-96.

[49] BERNSTEIN F, DECROIX G A, KESKIN N B. Competition between two-sided platforms under demand and supply congestion effects[J]. Manufacturing & Service Operations Management, 2020, DOI: 10.1287/ msom.2020.0866.

[50] 关伟, 吴建军, 高自友. 交通运输网络系统工程[J]. 交通运输系统工程与信息, 2020, 20(6): 9-21. [GUAN W, WU J J, GAO Z Y. Transportation network systems engineering[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(6): 9- 21.]

[51] ERHARDT G D, ROY S, COOPER D, et al. Do transportation network companies decrease or increase congestion?[J]. Science Advances, 2019, 5(5): eaau2670.

[52] HARDING S, KANDLIKAR M, GULATI S. Taxi apps, regulation, and the market for taxi journeys[J]. Transportation Research Part A: Policy and Practice, 2016, 88: 15-25.

[53] NIE Y M. How can the taxi industry survive the tide of ridesourcing? Evidence from Shenzhen, China[J]. Transportation Research Part C: Emerging Technologies, 2017, 79: 242-256.

[54] LI S, TAVAFOGHI H, POOLLA K, et al. Regulating TNCs: Should Uber and Lyft set their own rules?[J]. Transportation Research Part B: Methodological, 2019, 129: 193-225.

[55] DI X, BAN X J. A unified equilibrium framework of new shared mobility systems[J]. Transportation Research Part B: Methodological, 2019, 129: 50-78.

[56] 赵道致, 杨洁. 考虑不同监管目标的网约车服务价格管制策略研究[J]. 系统工程理论与实践, 2019, 39(10), 2523- 2534. [ZHAO D Z, YANG J. Research on the price regulation strategy of online car-hailing considering different regulation targets[J]. System Engineering: Theory & Practice, 2019, 39(10): 2523-2534.]

[57] 司杨, 关宏志, 严海. 网约车进入市场利益方博弈策略及效果分析[J]. 交通运输系统工程与信息, 2019, 19 (3): 19-26. [SI Y, GUAN H Z, YAN H. Game strategies of beneficiary parties and cost effectiveness analysis of introducing the online car-hailing service to the market [J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(3): 19-26.]

[58] YU J J, TANG C S, SHEN M Z J, et al. A balancing act of regulating on-demand ride services[J]. Management Science, 2020, 66(7): 2975-2992.

[59] MO D, YU J, CHEN X. Modeling and managing heterogeneous ride-sourcing platforms with government subsidies on electric vehicles[J]. Transportation Research Part B: Methodological, 2020, 139: 447-472.

[60] LI B, SZETO W Y. Modeling and analyzing a taxi market with a monopsony taxi owner and multiple rentee-drivers [J]. Transportation Research Part B: Methodological, 2021, 143: 1-22.

[61] 姚晓锐, 王冠, 杨超. 未来城市自动驾驶共享汽车规模研究: 以上海为例[J]. 交通运输系统工程与信息, 2019, 19(6): 85- 91. [YAO X R, WANG G, YANG C. Exploring fleet size of shared autonomous vehicles in future city: A case study in Shanghai[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(6): 85-91.]

[62] ALONSO-MORA J, SAMARANAYAKE S, WALLAR A, et al. On-demand high-capacity ride-sharing via dynamic trip- vehicle assignment[J]. Proceedings of the National Academy of Sciences of the USA, 2017, 114: 462-467.

[63] DING Z, DAI Z, CHEN X, et al. Simulating on-demand ride services in a Manhattan-like urban network considering traffic dynamics[J]. Physica A: Statistical Mechanics and Its Applications, 2020, 545: 123621.

[64] FRANCO P, JOHNSTON R, MCCORMICK E. Demand responsive transport: Generation of activity patterns from mobile phone network data to support the operation of new mobility services[J]. Transportation Research Part A: Policy and Practice, 2020, 131: 244-266.

[65] BECKER H, BALAC M, CIARI F, et al. Assessing the welfare impacts of Shared Mobility and Mobility as a Service (MaaS) [J]. Transportation Research Part A: Policy and Practice, 2020, 131: 228-243.

[66] ZHANG W, GUHATHAKURTA S, FANG J, et al. Exploring the impact of shared autonomous vehicles on urban parking demand: An agent-based simulation approach[J]. Sustainable Cities and Society, 2015, 19: 34-45.

[67] LEVIN M W, KOCKELMAN K M, BOYLES S D, et al. A general framework for modeling shared autonomous vehicles with dynamic network-loading and dynamic ridesharing application[J]. Computers, Environment and Urban Systems, 2017, 64: 373-383.

[68] FAGNANT D J, KOCKELMAN K M. Dynamic ridesharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas[J]. Transportation, 2018, 45(1): 143-158.

[69] LOEB B, KOCKELMAN K M, LIU J. Shared autonomouselectric vehicle (SAEV) operations across the Austin, Texas network with charging infrastructure decisions[J]. Transportation Research Part C: Emerging Technologies, 2018, 89: 222-233.

[70] CHEN T D, KOCKELMAN K M, HANNA J P. Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions[J]. Transportation Research Part A: Policy and Practice, 2016, 94: 243-254.

[71] LOEB B, KOCKELMAN K M. Fleet performance and cost evaluation of a shared autonomous electric vehicle (SAEV) fleet: A case study for Austin, Texas[J]. Transportation Research Part A: Policy and Practice, 2019, 121: 374-385.

[72] VOSOOGHI R, PUCHINGER J, JANKOVIC M, et al. Shared autonomous vehicle simulation and service design [J]. Transportation Research Part C: Emerging Technologies, 2019, 107: 15-33.

[73] VOSOOGHI R, PUCHINGER J, BISCHOFF J, et al. Shared autonomous electric vehicle service performance: Assessing the impact of charging infrastructure[J]. Transportation Research Part D: Transport and Environment, 2020, 81: 102283.

[74] STOGIOS C, KASRAIAN D, ROORDA M J, et al. Simulating impacts of automated driving behavior and traffic conditions on vehicle emissions[J]. Transportation Research Part D: Transport and Environment, 2019, 76: 176-192.

[75] TU R, ALFASEEH L, DJAVADIAN S, et al. Quantifying the impacts of dynamic control in connected and automated vehicles on greenhouse gas emissions and urban NO2 concentrations[J]. Transportation Research Part D: Transport and Environment, 2019, 73: 142-151.

[76] GAWRON J H, KEOLEIAN G A, DE KLEINE R D, et al. Deep decarbonization from electrified autonomous taxi fleets: Life cycle assessment and case study in Austin, TX [J]. Transportation Research Part D: Transport and Environment, 2019, 73: 130-141.

[77] STERN R E, CHEN Y, CHURCHILL M, et al. Quantifying air quality benefits resulting from few autonomous vehicles stabilizing traffic[J]. Transportation Research Part D: Transport and Environment, 2019, 67: 351-365.

[78] FAGNANT D J, KOCKELMAN K M. The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios[J]. Transportation Research Part C: Emerging Technologies, 2014, 40: 1-13.

[79] ZHANG W, GUHATHAKURTA S, KHALIL E B. The impact of private autonomous vehicles on vehicle ownership and unoccupied VMT generation[J]. Transportation Research Part C: Emerging Technologies, 2018, 90: 156-165.