Forecast Of Urban Car Ownership And Economic Effect Analysis Of Road Traffic

This study established the forecast model of urban car ownership in terms of transportation demand and environment capacity, analyzed the economy effect of road traffic based on economy and diseconomy aspects induced by car industry and car use respectively, to provide theory and practical base for sustainable transportation system.Firstly, a macro-level forecast model of urban car ownership based on artificial neural network technique is developed. To simulate the non-linear relationship between car ownership and its effect factors, the main factors are analyzed, and seven input vectors, such as per-capita GDP, transit network density, road network density, car price/income, patrol price and external disturbing factors, are taken as independent variables and car ownership is taken as output variable to establish the forecast model based on BP neural network technique. Here, external variable, including some affecting factors which could not be measured, is input to the model to simulate the sudden effect induced by them. Furthermore, to verify the model, I got the calculation formula of car ownership taking Dalian data as a case.Secondly, taking traffic environment load into account, the maximum urban car ownership model constrained by transportation environment capacity is developed. The purpose is to optimize the traffic distribution and get the maximum car ownership constrained by environment capacity. A bi-level programming problem has been established. The upper level is a car ownership model, in which objective function is to maximize the total of zonal car ownerships and the constraint is traffic environment load on a link should not exceed the pre-decided capacity. The lower level is a fixed demand user equilibrium assignment model, which simulates the travelers' path choice behavior and calculates distributions and running characteristics of traffic demand on road network. To realize the feedback between the two levels and solve the optimization problems simultaneously, we developed an optimal algorithm based on sensitivity analysis, namely acquire derivative function of link volume and traffic demand with respect to zonal car ownership, and feedback the function into upper level program. With iterated calculation, the maximum car ownership in upper optimization could be obtained. In the last, the bi-level model and the algorithm are verified with a case study.Next, the economy and diseconomy values induced by car industry and car use are integrated to analyze the economic effect of road traffic. To calculate the economic effectbrought by car industry with the growth of car numbers, input-output theory and industry association theory are used to analyze the upper and lower industries related to it. Then the immediate effects, backward multiply effect, forward multiply effect and consume multiply effect are calculated and summed to get the contribution to GDP of per-capita car production; the externalities include both transportation environment pollution and traffic accidents. The other part of this study is the external diseconomy of traffic environment pollution and traffic incidents. Based on the relation between car ownership and transportation environment load, and also the relation between car ownership and the number of traffic incidents, the macro forecast models are established respectively, and all results are conversed to monetary values.Finally, taking car numbers as a base, various monetary factors are integrated to analyze the economic effect caused by road traffic.