Research On Theory And Application Of Travel Behavior And Urban Public Transit Pricing

Urban public transportation, a critical infrastructure of public service, which is closely related to citizen production and daily life, economic development and social stability, affects directly the overall function of the city, citizen life and urban sustainable development. Travel behavior is the key factor in urban public transit pricing, and transit pricing will play a significant role to the result of travel behavior, and it will determine public transport share rate and then will affect the survival of operating enterprises and the share of social welfare when service quality is unchanged. At present, bus fare is still a static pricing model based by policy regulation, corporate costs and profit, without considering the latent variable arising from travel mode choice behavior of urban residents into the construction of the dynamic pricing model. Moreover, temporal differentiatioan fare is hardly researched, delaying the requirement of bus priority development.Based on this, transit pricing model based on travel mode choice behavior was constructed by researching conventional bus transportation action on bus travel behavior analysis and pricing model establishment to improve the existing model and study the interaction between them. The main contents of this paper include:1. The definition of urban public transit system was extended, which included system participants of government departments, public transport enterprises and passengers, based on studying a category of urban transport system. The impact of public transit pricing on the structure of urban transport systems was analyzed, and then a concept of "model-route" graph was proposed to describe the whole process of travel according to traditional traffic network assignment model. At last, trip assignment model for urban traffic system based on network equilibrium was built and a numerical example was designed to verify the validity of the model.2. Travel behavior analysis model involved latent variables such as travelers’attitude, subjective norms and perceived behavioral control was constructed using the theory of planned behavior, and measurement items for each latent variable was design to reveal mechanism of the latent variable in the travel selection process. Example results show that travelers’perceived behavioral control have a significant impact on travel behavior intentions and have a positive correlation, followed by travelers’attitude on the impact of behavioral intentions, then the subjective norms have the weakest effect on intention of travel behavior.3. The concept of influence factor categories of travel mode choice behavior by analysis of travel mode choice behavior of decision-making process on the theory of consumer behavior patterns in the angle of EMB (Engel Miniard Blackwell model), which made the factors divided into the latent variables and variables and proved the existence of the "black box" idea between elements and given solution. And a structural equation modeling of public transportation mode selection behavior was built and to be used in the analysis of Chengdu bus travel survey examples, and then questionnaire designing, test on reliability and validity and solving steps required for model validation were discussed. The results show that perceived value on the effect of behavioral intention is quantified to be0.84, service quality on the effect of perceived value is quantified to be0.65, greater than0.26of the price rationality, which illustrate that travelers think level of service are valued higher than the price, service environment variables is the most important one in service qualities whose path coefficient is0.68. Finally, taking self-awareness level of travelers personality traits, occupation, income as a grouping criteria, a quantitative comparative was analyzed for different groups of public transport mode selection behavior characteristics.4. For traditional utility functions in Logit mode choice model considered only the observable program properties and individual socio-economic attributes, but did not consider psychological factors which affect travel choices, SEM-Logit integration model of bus mode choice behavior was built combined with structural equation modeling (SEM) and Logit model to solve the problem that current studies didn’t consider latent variable in travel mode selection model. And two-phase algorithm of integrated model was designed, whose solution was proved by both theory and application that the ability to explain was promoted more when considering satisfaction of service quality in integration model than traditional Logit model.5. Bi-level programming model of public transit pricing based on Multi-mode network with elastic demand was constructed from interaction mechanism between travel behavior and public transport pricing, which effectively described that public transport network fare changes have an impact on the total travel passengers, mode split and route choice. The objective function of the upper model is to maximize their profits, to minimize the cost of passenger and to maximize social welfare, the lower was a random user assignment model based on Multi-mode network with elastic demand. Numerical results showed that dynamic bus pricing using bi-level programming model can obtain higher yields for government, enterprises and travelers than the traditional static pricing, and the upper model took social welfare maximization as the objective function can represent the interests of the majority social groups and made the effect of optimize most satisfactory.6. The concept of temporal differentiation fare was proposed according to characteristics of unbalanced nature urban transport travel time and the feasibility was analyzed. Then the temporal differentiation fare mode was constructed, the upper model is a model with optimal social welfare and the lower model described Multi-mode and Different Times network with elastic demand by unnecessary travel coefficient. The overall model was solved using improved genetic algorithm and the lower model was solved by diagonalization algorithm combined with the MSA algorithm. Numerical results show that the social welfare objective function of temporal differentiation fare program was37higher, and corporate earnings was36.5higher than the ones of the same flat rate pricing program in the peak level, the ratio was reduced from1.75:1in bus passenger flow peak periods to1.16:1in the flat peak periods. Another conclusion was that the program of increasing fare in the peak level while reducing fare in the flat peak had a more obvious effect than a program of only increasing fare in the peak level or a program of only reducing fare in the flat peak, and that passengers have a greater elasticity when reducing fare in the flat peak than increasing fare in the peak level.In summary, the paper fully involved in the exploration and research on basic theory of urban public transport, TPB-based theory of travel behavior, structural equation modeling of travel mode choice behavior, SEM-Logit model integration of transportation mode selection, bi-level programming model of travel demand and transit pricing, which can provide the theory and methods for analysis of urban public transport demand and public transit pricing.