| The dissertation studies on route choice,traffic guidance information and game equilibrium. First,this paper proposed the driver’s k+1 times path-selection depends on his own income of the k times path-selection on appling operant conditioning to the driver’s route choice behavior: self-learning mechanism. Secondly, the paper consider the feeling of driver’s travel time as his route choice income and the time feeling has been divided into three fuzzy sets, and then gives the membership function of each fuzzy sets. Finally, the paper in the bounded rationality and under the premise of time feeling as fuzzy sets, the corresponding model is established, then gives the algorithm of solving the model and simulation validation.Specifically, the main contents and innovations are as follows:①The theory is applied to the operating conditions of the vehicle path reflection driver’s choice behavior, the establishment of a rational choice based on limited game without inducing fuzzy vehicle routing information under the conditions of the model; proven in the nine initial state model, the game will eventually strike a balance, and gives the game a balanced outcome in nine initial state.②The establishment of a fuzzy game based on the finite rational path of a vehicle under guidance Information selection model, the model gives the algorithm, and using specific examples of the model simulation.③Comparative analysis of the information has induced and non-induced conditions under Information Game balanced outcome, the results show: inducing the release of information is not always effective, when the total number of vehicles involved in the game less than the total road network capacity, inducing release Information can achieve good results; when the total number of vehicles involved in the game close to the road network of the total capacity, the availability of information release induced no significant difference; when the total number of vehicles involved in the game is much larger than the total road network capacity, without the right path under guidance Information higher utilization of network conditions have induced the information network utilization.④The establishment of a self-learning mechanism of accumulation of vehicle routing model, the model gives the algorithm, and using specific examples conducted simulation, simulation results show that: the accumulation in self learning mechanism, no information of vehicle routing induced game results induced release of information and vehicle routing showed no significant difference in the game.⑤The dissertation have analyzed the impact of changes induced by game model parameters ζ under Information on the outcome of the game balance. Affect paper uses simulation method to analyze the changes in the game balance ζ results, simulation results show that: when the total number of vehicles involved in the game less than the total road network capacity, the impact of changes in ζ balanced outcome of the game significantly; when the total number of vehicles involved in the game When the network is much larger than the total capacity, changes in ζ results without affecting the balance of the game; in the total number of vehicles involved in certain game situations, the greater path L1 initial allocation, the smaller the impact of changes in ζ balanced outcome of the game.⑥Finally, the paper analyzes and discusses the impact of changes on the game model parameters ζ induced effect. The results showed that: ζ can affect the players overall total travel time T, the total impact on the total number of initial T notable Bureau of flow distribution and Game and Game of human-related; it is less than the total number of human(or close to) the total road network tra ffic When the ability to influence the overall T ζ on the main road and the ratio of the initial allocation of the relevant network traffic; when the authorities total road network is much larger than the total number of human capacity, ζ T total impact is not significant. |
PDF Full Text Request