Analysis And Modeling Of Travel Mode Choice For Autonomous Vehicles Under Managed Lanes

Autonomous Vehicle(AV)has significant features such as improved road capacity,reduced traffic emissions,and vehicle-road cooperation,while managed lanes,with access control and relatively rapid provision of advanced infrastructure,provide a good platform for the gradual promotion of AV from low penetration to high penetration.The mixed traffic flow under the coexistence of human-driven vehicles and AVs will persist for a long time.The use of managed lanes is an important measure to fully utilize the benefit of AVs in the mixed traffic environment.With the integration of AV and managed lanes,the travel behavior will change.Therefore,it is essential to analyze the travel mode preference and mode choice of urban travelers in the context of AV and managed lane integration.In this thesis,a questionnaire first has been designed to conduct a survey on the heterogeneous travel mode choice.The Stated Preference(SP)with Revealed Preference(RP)survey is combined to obtain social characteristics,AV attitude willingness,and mode choice of respondents.The commuting population in Beijing is surveyed in this thesis.Secondly,the Multiple Indicator Multiple Cause Model(MIMIC model)is constructed to explore the behavioral intention to use AV.The latent class model and latent class affiliation influence model are constructed based on attitude questions in the survey.Thirdly,in view of the managed lane type,a random parameter choice model considering panel effects for different groups is constructed to explore the influence of the managed lane type on the mode shift of different traveler groups.Fourthly,from the perspective of managed lane service level improvement(travel time and travel-time fluctuations),the mode choice model has been built for commuting and leisure purposes to compare the influencing factors of the mode choice.The elasticity analysis of the mode attributes has been carried out to identify the sensitive factors.Finally,the changes in preference for autonomous bus(AB)for different groups under different service levels of managed lanes for commuting are analyzed.The main findings are as follows.(1)The MIMIC model shows that social influence has the most significant impact on behavioral intention to use AV.The AV perceived usefulness and perceived risk affected the behavioral intention to use AV as both direct and mediating variables.According to the latent class modeling,the sample can be identified into three groups,including AV leading group(21%),AV positive group(43%),and AV hesitant group(36%).The model indicates that age,car ownership,attitudes toward driving,and advanced driver assistance system experience can help predict the group identification.(2)The analysis of the impact of managed lane types on mode choice of different travel groups shows differences in the impact of bus priority lanes and AV dedicated lanes on different travel groups.Compared with the general lane,managed lanes greatly impact metro groups shifting to AV.The bus priority lane is most beneficial in attracting travelers to shift to AB,while AV dedicated lane stimulates metro travelers to shift to the private autonomous car(PAC).(3)The results of the mode choice model for different levels of service for the managed lanes show that there are differences in the impacts under commuting and recreational purposes.The mode share of AB for commuting is most sensitive to travel time,while autonomous taxis and PAC are more sensitive to travel cost.When the travel time of AB is reduced by 25% and travel time fluctuation is reduced by 30%,travelers’ preference for AB exceeds that of PAC.When the behavioral intention to use AV increases by 6%,the choice preference for PAC exceed that of the conventional mode currently used by travelers.