Analysis Of Choice Behavior For Shared Autonomous Vehicles With The Concern Of Ride-Sharing

With the acceleration of urban motorization,the number of private vehicles has increased day by day.Road congestions,parking problems,traffic accidents,and environment pollution have become increasingly serious.At the same time,advances in technology have made the autonomous vehicles(AV)a reality,and the autonomous vehicle technology is expected to improve road safety while reducing traffic congestions and pollutant emissions.The autonomous vehicle technology may also provide commercial services,such as the shared autonomous vehicle(SAV),for people to travel.If SAV,which combines the advantages of private vehicles and public transit,enters the transportation system,private vehicles travel and public transportation will inevitably be affected.The reason is that people have more mode choices in their daily travel.In addition,SAV can be combined with the dynamic ride-sharing system,so SAV with ride-sharing will be a positive attempt to achieve green transportation and sustainable development.Therefore,it is practically significant to conduct a comprehensive intention survey and analysis before this travel mode introduced to the market,and to help scientifically and reasonably formulate relevant transportation policies.Firstly,the SP questionnaire was designed by analyzing travel mode choice behavior on SAV with ride-sharing.In February 2019,a total of 488 questionnaires were collected,including 393 valid questionnaires.Secondly,based on K-Means cluster analysis,the historical travel modes of respondents are classified into different patterns.Factor analysis is used to integrate the related characteristics of personality attitude into a kind,so as to obtain a number of personality attitude characteristics variables.Then,on the basis of processing and classifying the effective data,using the utility maximization theory to establish random parameters for the crowd without private vehicles and the crowd with private vehicles,to obey the differently distributed Mixed Logit models,use R language for the parameter estimation.The detailed parameter estimation results of the ML-I model with normal distribution and the ML-II model with the highest fitness were selected.Finally,the ML-II model with the highest fitness was used to analyze the significance of various influencing factors.The results show that the positive and negative of the various influencing factors calibrated by ML-I model and ML-II model are consistent and significant.It is obvious that both models can well explain the choice behavior on SAV with ride-sharing.As for the crowd without private vehicles and the crowd with private vehicles,travel costs,in-vehicle travel time,historical travel mode,daily travel distance,average travel time per trip,weekly travel costs,travel purposes,personality attributes characteristics,occupations,and the familiarity of SAV with ride-sharing will have a significant impact on choice behaviors.However,in addition to the above factors,the waiting time,arrival time flexibility,driver's license,and housing situation are also significant factors in the choice of travel modes for the crowd without private vehicles;the parking fee and monthly income also have a significant impact on SAV with ride-sharing for the crowd with private vehicles.