Research On Driver Travel Behavior In The Satellite-City Area Of Urban Agglomeration

The reseaech on travel behavior is an important element for establishing the traffic management and control strategy. It is one of the important content of city Transportation Planning and also is one of the main reference content for establishing the urban agglomeration traffic development strategy. The existing methods are simple and lack of systemic. The existing models are mainly used for studying the behavior of traveler travel choice of big cities. Study on the travel behavior of satellite cities has been not appeared. The satellite city has been appeared along with the rise of urban agglomeration. The traffic travel behavior between the satellite city and the center city is different. So study on the behavior of travel choice in satellite city is great significance for reduceing the traffic congestion of center city.Based on formation and development of urban agglomeration, the important function of satellite city driver travel behavior in the urban agglomeration transportation planning was expatiated. It is need to research the travel choice behavior of satellite city driver because of the difference travel choice behavior between satellite city driver and center city from analyzing a lot of literatures. Many mentality factors affect the satellite city drivers, so the hybrid utility model was established including average utility, self anticipation regret utility, relative anticipation regret utility, self risk avoid utility and relative risk avoid utility. The model was validated by experiment route network.In order to depict the real travel choice behavior of satellite city drivers, this paper introduced systematization analyzes travel choice behavior method relation to route generate and route choice. Considered the infection of information to drivers learn, the route generate method based on information to update anticipate regret was introduced. Contrasting to tradition method, the method which was introduced in this paper can exactly reflect the real process of route generate of drivers.For the advantage of Bayesian network reasoning method in the field of uncertainty, this paper used it to depict the link attribute update process by drivers self learning. Updated link attribute was obtained by Bayesian network structure and parameter learning.Because of the travel information and depart period of time affect the drivers, a multi-period route choice hybrid utility random model was established. The information function expression was firstly introduced into the model. The driver's anticipate regret threshold and expression were introduced. The paper established the framework of route choice in multi-period and multi-travel. The choice behavior of drivers was modeled through experiment network. By contrast the choice behavior under without information and information obtain, the paper believed that the multi-period route choice hybrid utility random model and systematization analyzes travel choice behavior method were capable of explaining the travel choice behavior of satellite city drivers.