Research On The Operational Optimization Of Shared Mobility With Autonomous Vehicles

Urban development depends on the efficient operation of the road transportation system.However,the deepening of urbanization and motorization has made the relationship between demand and supply in road transportation increasingly tense,which further causes extreme traffic externalities.With the development of technology and economy,autonomous driving and shared mobility will become the two major directions of the future urban transportation system,leading to a promising solution to cope with the externalities of urban transportation.This study is dedicated to researching the operational optimization of mobility with shared autonomous vehicles(SAVs).This study conducts the assignment optimization of SAVs in the service state and the parking optimization of SAVs in the non-service state.Further,this study quantifies the potential impact of mobility with SAVs on the urban road transportation system.Firstly,this study proposes a vehicle trajectory reconstruction method based on license plate recognition data,converting roadside node-based detection data into trajectory data that contains spatiotemporal information.Furthermore,the trajectory reconstruction method is applied to urban-scale license plate recognition data to extract motorized travel demand information,which provides data support for the operational optimization modeling of mobility with SAVs.Secondly,this study investigates the operation and impact of carpooling with human-driven vehicles.An updated longest common subsequence algorithm is used to evaluate the spatiotemporal similarity of trajectories and retrieve all potential carpooling matching pairs.An integer programming model is constructed to find the optimal pairwise matching,which leads to the maximum reduction in traffic volume.This part provides methodology foundation and prospect description for the investigation on the mobility with SAVs.Furthermore,this study investigates the mobility with SAVs,demonstrating the operation strategy and traffic impact of SAVs in service state.This study focuses on a future mobility scenario,where the individuals no longer purchase or own vehicles,but use the ride-sharing and car-sharing services provided by the SAV fleet.This study designs a travel-time-constrained ride-sharing matching strategy,retrieving all the potential ride-sharing matching pairs.A large-scale integer programming model is formulated to search for the optimal pairwise ride-sharing matching.A vehicle assignment model is constructed to search for the minimum fleet to fulfill the travel demand.To make the vehicle assignment model tractable,the original model is transformed into a minimum path cover problem in the network programming,which can be exactly solved by a graph-based algorithm.This study further conducts multi-scenario analysis to demonstrate the feasibility and impact of SAVs.Specifically,this study investigates mobility with SAVs under cases with different shared mobility participation levels,in case cities with different sizes,and in a scenario considering actual public participation willingness.This part helps reveal potential benefits and possible problems during the promotion of SAVs.Finally,this study carried out parking planning towards SAVs,demonstrating the operational optimization of SAVs in non-service state.Two-stage stochastic parking facility planning models are constructed to fulfill the temporary parking demand of SAVs with the minimum social cost.The first-stage decision aims to determine the location and capacity of the parking facilities.The second stage generates recourse decisions,which return the parking relocation assignments of SAVs to make the first-stage planning scheme adapt to various uncertain demand scenarios.This model is applied to a realworld case to show its effectiveness.