Study On The Method Of Dynamic Route Choice On Urban Road

ITS has been made the trend of current traffic transportation, while dynamic route choice is a very important aspect of ITS research. The essence of dynamic route choice is that the condition according to which the driver chooses the route is changeable with time. Three aspects have been made the aims of this article. First, to help the driver avoid traffic jam and accident; second, to help the driver avoid lost for the unfamiliarity with urban traffic environment; third, to reduce vehicles' driving time on the road thus make traffic run smoothly.Firstly three standers for route choice are set. They are the physically shortest route, the least time cost and the largest traffic volume. Then pros and cons of the common current ways used in dynamic route choice are analyzed. At last ant society arithmetic is adopted to make route choice.The basic principle of ant society arithmetic is that when ants are seeking a route they release information element on the route they pass. The route with the easiest detected information element is made the physically shortest route. It is unpractical that we duplicate ants' route-seeking method when we make dynamic route choice. Thus in order to solve the problem of vehicles' dynamic route choice we must optimize the arithmetic.For the sake of limitations existing in basic ant society arithmetic, combined with some present optimization methods optimized dynamic ant society arithmetic is put forward. In this optimized method, a dynamic adjustive factor is introduced. Information elements' concentration of each road is confined to between a minimum and a maximum. Any value that is beyond this range is forced to be made the maximum. In this way limitless accumulation of information elements and zero information elements are avoided. At the same time, the phenomenon of stopping to find other routes after finding one single route is avoided. The route chosen is the one that meets several confined conditions at the same time rather than the one that only meets physically shortest condition. The math model, steps, simulated codes and framework of the program are put forward firstly. Secondly the basic principle and steps of the optimized dynamic ant society arithmetic are expounded. Each parameter is decided by Matlab emulation. At last the effectiveness of this arithmetic is validated by using VB and Matlab softwear and applying this arithmetic to a simulated area and certain actual area in Xi'an. Reliability and practicability of this arithmetic are validated.