A Study On A Hybrid Choice Model Of Travel Mode Based On Bayesian Approach

Since the reform and opening-up,Chinese economy has developed rapidly while the level of urbanization has improved,which have brought traffic congestion and serious environmental pollution problems,leading to low efficiency of people travel,hindering urban and economic development.Aiming at studying the personal choice of travel mode,a hybrid choice model combining structural equation model and measurement equation model is constructed by introducing traveler's psychology as potential variables based on cognitive psychology and behavioral theory,and Bayesian methods are used for estimating unknown parameters in the model to discuss theoretical and applicable aspects of traveling behaviors.This paper is carried out from the following aspects: the first chapter introduces the research background and current research status of the choice of travel mode.The second chapter introduces the theory,including Bayes' theorem,Markov Chain Monte Carlo method(Metropolis-Hasting and Gibbs sampling method),the basic idea of approximate Bayes computation,and the rejection algorithm.The third chapter presents an introduction and construction of the model,including the questionnaire collection of samples designed by likert5-level scale;the hybrid choice model constructed by introducing the latent variable of environmental preferences based on the data collected for Qianshan city;and the Markov Chain Monte Carlo algorithm and approximate Bayesian algorithm applied to the model.In chapter 4,the unknown parameters of the model are estimated based on the proposed algorithm with data collected,the results are analyzed and the two methods are compared.The last chapter summarizes the work and provides future directions.The empirical results show that the psychological potential variables of environmental preference have an impact on the residents' travel intention.Female,18-40 years old,and those people who has a bachelor degree or above will pay more attention to environmental factors,and are more likely to choose WB travel mode.The carbon emission and convenience of the travel mode are the main factors affecting the residents' travel consideration.By comparing the two algorithms,the Markov Chain Monte Carlo method which is dependent on the likelihood of kernel function,is found to be more sensitive to priors' hyper-parameter values and requires controlling sample's correlation,however,it can estimate parameters highly efficiently.The approximate Bayesian computation which can avoid solving the likelihood function,is more robust to the values of the prior hyper-parameters and can estimate the parameters effectively.