Optimization Research On Road Passenger Transport Route Based On Passenger Travel Behavior Analysis

With the speed up of China's high-speed rail construction, passenger transport pattern changes a lot, which contribute influences to other modes of transportation. As an important form of passenger transport, road transport plays an important role in the transportation of passengers due to its flexibility and convenience. However, with competition between high-speed rail and other modes of transportation, the unreasonable phenomenon on organizing and planning of road passenger transport is highlighted, which directly restricts the development of road passenger transport. Therefore, in order to reduce cost and improve service levels to gain a competitive advantage, rational organizing and planning of road passenger transport are urgently to be solved in study of road passenger transport.Based on the analysis of passenger travel behavior, the paper studies the problem of road passenger transport line optimization. Analysis of passenger travel behavior by the use of psychology and utility theory, this paper refining and analysis of factors affecting passenger travel behavior, introduces the SEM to establish the sharing-rate prediction model. Then, on the basis of the prediction of road passenger transport demand, the paper established a road class line optimization model and departure schedule optimization model to provide a reference for the rational planning and organizing of transportation of the road passenger transport enterprises. Specific content is organized as follows:The second chapter studies the factors affecting passenger travel behavior and passenger travel choice behavior in direct and transfer mode. Firstly, this chapter studies travel behavior choice theory base and analyzes the development situation and trend of road passenger transport; Secondly, designing the questionnaire based on SP and RP survey to investigate and analyze the survey results; Finally, summarized and analyzed the factors affecting passenger travel mode choice behavior in direct and transfer mode.The third chapter studies the problem of transportation sharing-rate prediction model in direct and transfer traffic mode. Firstly, on the basis of analysis of the status quo of the transportation sharing-rate prediction model, paper selected disaggregate logit model as the basic model; Due to the deficiencies of the logit model, SEM model is introduced to analyze the relationship among each factor that influences passenger travel behavior. Secondly, a new utility function is builded based on SEM model; Finally, based on questionnaire data processing, paper established the sharing-rate prediction model for direct and transfer mode of passenger transport, and calculate the road passenger transport sharing-rate.The fourth chapter studies the problem of road class line optimization. Firstly, this chapter outlines the problem of road transport class line setting and qualitative analysis of road transport passenger flow characteristics; Secondly, according to the forecasts idea of to predict overall demand and then share to the various modes of transportation by the sharing-rate, paper predict the total demand for passenger transport by combination forecasting method based on RBF neural network and then predict the road passenger transport demand and demand structure; Finally, paper established the road transport class line adjustment and optimization model, and gives the solution method.The fifth chapter studies the problem of adjustment and optimization of road passenger transport departure schedules. Firstly, this chapter analyzes the principle of the passenger terminal preparation departure schedules; Secondly, paper established the departure schedules basic optimization model based on the basic assumption; According to the passenger arrival and the preparation process of the departure schedules, paper presents a departure schedules preparation methods which facilitate the actual operation. Finally, based on survey data, case studies, to prove the feasibility of the model.