Shared mobility refers to the shared use of a motor vehicle, bicycle, or other low-speed transportation modes, which has significant influences on transportation systems. Among different types of shared mobility, ridesplitting is developing the most rapidly because of the emergence of transportation network companies. In this study, we present exploratory evidence of on-demand ridesplitting's impacts on the vehicle use and purchase willingness using the emerging ridesourcing real-world data (i.e., app-based, on-demand ride services like Uber and DiDi Chuxing) in Hangzhou, China. We explore ridesplitting users' travel habits and their transportation modal shift behavior if the ridesplitting services are unavailable, and how ridesplitting impacts the use of public transit and private cars. In summer 2017, an online survey was conducted with respondents who had completed ridesourcing journeys within one month before the investigation. We compare the survey results with two-week ridesourcing order data provided by DiDi Chuxing, 20% of which are ridesplitting orders (including DiDi Hitch and Express ridesplitting). Considering the modal shift from public transit to ridesplitting, the findings indicate that (I). In the short term, ridesplitting services reduce the number of vehicles on road (decreased by 3,051 veh/day, nearly 2.6 parts per thousand of the vehicle ownership in the urban area of Hangzhou). (II). In the intermediate term with the development of ridesplitting, the total decreased number of vehicles is estimated to be 4,129 veh/day (nearly 3.6 parts per thousand of the vehicle ownership). Au interesting phenomenon is that Hitch's influences on the vehicle usage in the intermediate term is not obvious as the influence in the short term due to travelers' modal shift from public transit. (III). In the long term, ridesplitting will reduce the car purchase willingness and influence people's travel behavior to some extent. This paper shines light on quantitatively exploring the influence of on-demand ridesplitting on the vehicle use and purchase willingness.

This paper proposes a dynamic ride-hailing sharing problem with multiple vehicle types and user classes. Ride-hailing vehicles (RHVs) can be classified into express ride-hailing vehicles (ERHVs) and premier ride-hailing vehicles (PRHVs) according to service levels. PRHVs can provide the high-quality ride-hailing service with upmarket vehicles and ERHVs provide the normal ride hailing service with normal vehicles. The fare of PRHVs is higher. PRHVs can be temporarily used as ERHVs to serve the customers who order ERHVs with or without ride-sharing, which referred to as the substitution of ERHVs with PRHVs. A lexicographic multi-objective function with three-level objectives is proposed to formulate the problem, in which the first-level objective is to maximize the profit of the platform, the second-level objective is to minimize the number of requests of customers who involve ordering ERHVs matched to PRHVs, and the third-level objective is to minimize the total driving distance of all RHVs. The dynamic problem is divided into a set of continuous and small ride-hailing sharing subproblems based on equal time intervals. Each subproblem is formulated as a mixed integer nonlinear program for matching RHVs to the requests collected in the last time interval or unmatched in previous time intervals and re-scheduling the vehicle routes. To solve the subproblems, a new solution method is proposed based on the modified artificial bee colony algorithm developed by Zhan et al. (2021). Numerical examples using real request data from Didi are given to explore the problem properties, and the results gain insights into the ride-hailing market. For example, the profit of the platform and the number of matched requests are higher when the substitution of ERHVs with PRHVs is allowed while the matching percentage of requests of customers who select a mixed choice is higher when there is no substitution. Different ratios of vehicle types and user classes influence the perfor-mance of the ride-hailing sharing market (e.g., the profit of the platform, the number of matched requests, matching percentage, etc.). The value of the fare discount multiplier for the passengers who successfully share RHVs with others can affect the number of shared requests, the number of matched requests, and platform profitability.

Electric vehicles (EVs) are more environmentally friendly than gasoline vehicles (GVs). To reduce environmental pollution caused by ride-hailing gasoline vehicles (RGVs), they have been grad-ually replaced with ride-hailing electric vehicles (REVs). Like RGVs, REVs can allow passengers to share trips with others. However, REVs are plagued by charging needs in daily operations. This study develops a simulation-optimization framework for the dynamic electric ride-hailing sharing problem. This problem integrates a dynamic electric ride-hailing matching problem (with sharing) and a dynamic REV charging problem, both of which aim to match REVs to passengers willing to share their trips with others and schedule the charging events of REVs on temporal and spatial scales, respectively. The dynamic electric ride-hailing matching problem is divided into a set of electric ride-hailing matching subproblems by a rolling horizon approach without a look-ahead period, while the dynamic REV charging problem is divided into a set of REV charging sub -problems by a rolling horizon approach with look-ahead periods. Each REV charging subproblem incorporates a novel charging strategy to determine the charging schedules of REVs and relieve the charging anxiety by considering the information of requests, REVs, and charging stations. Each REV charging subproblem is formulated as a mixed integer linear program (MILP), whereas each electric ride-hailing matching subproblem is formulated as a mixed integer nonlinear pro-gram (MINLP). The MINLP and MILP are solved by the artificial bee colony algorithm and CPLEX, respectively. The proposed simulation-optimization framework includes a simulation model which is used to mimic the operations of REVs and update and track the state of passengers and the charging processes at charging stations over time using the outputs of each MILP and MINLP. The results show that the proposed charging strategy outperforms the benchmarks with a shorter waiting time for charging and a higher matching percentage in the dynamic ride-hailing matching problem. The robustness of the proposed charging strategy is tested under different scenarios with changing the initial state of charge (SOC), the number of REVs, the number of charging piles at each charging station, the time to fully charge, and the distribution of charging piles. The results show that REV drivers can charge their vehicles more flexibly without waiting too long and then pick up more passengers under all test scenarios.

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. Copyright © 2021 by Science Press.

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。

In the era of transportation big data, the analysis of mobility patterns generally involves large quantities of datasets with high-dimensional variables recording individual travelers' activities and socio-economic attributes, bringing new challenges to researchers. Conventional regression-based models commonly require complex structures in depicting random or fixed effects with a considerable number of parameters to estimate, and state-of-the-art machine learning models are regarded as black-boxes that are not clear in interpreting the mechanism in human mobility. To overcome the challenges of capturing complex high-order relationships among variables of interest, this paper proposes a Bayesian supervised learning tensor factorization (BSTF) model for the classification of travel choices in the mobility pattern analysis. The BSTF model induces a hierarchical probabilistic structure between predictor variables and the dependent variable, which offers a nature supervised learning foundation via Bayesian inference. Latent class (LC) variables are considered in the BSTF model to discover hidden preferences/states among travelers associated with their mobility patterns. We apply the BSTF model to analyze passenger-side choice patterns between diverse service options on a ride-sourcing platform, drawing increasing attention during recent years. A case study with a real-world dynamic ridesharing dataset in Hangzhou, China, is conducted. Different cases of training sizes are utilized to fit the proposed BSTF model as well as some other state-of-the-art machine learning models. By identifying significant variables and derive their probabilistic relationship between service types (i.e., ridesharing, non-sharing, and taxi), the proposed BSTF model offers good performance in both classification accuracy and the interpretability of shared mobility.