Method Research Of Stochastic Path Planning On Navigation

With the development of economy, people’s living standard has improved, and urban traffic congestion is also growing. As the important research content in intelligent transportation system, vehicle navigation system becomes one of the hotspots in the field of the current international traffic. It incorporates the related technology in the field of traffic, vehicles, electronic communication, network and so on. According to different travel demands, it can provide real-time route guidance for the drivers to reduce vehicle’s retention time in the traffic network, improve the efficiency of road network, and, to some extent, relieve urban traffic congestion.Path planning is in the key position in vehicle navigation system, and the traditional research on the shortest path problem always assume that the weights on the transportation network is certain, but these assumptions is not fully comply with the reality of the traffic environment. As the shortest path problem in the random environment can simulate the randomness of the real traffic network parameters better, the research has more practical significance than the traditional one.Starting with the random variables (the travel time of a section) in the reality network traffic, this paper statistics and analysis the history data of the variables in specific time, then gets the probability distribution function of the random variable. According to the travel demand which is related with travel time in vehicle navigation, Stochastic expected value model, stochastic chance-constrained programming model and stochastic dependent-chance programming model are constructed for path planning. These models can provide the path of the shortest expected time, the shortest distance path with time constraint and the largest probability path witch arrived destination in the scheduled time for travelers. Because the model with random variables, this article designs genetic algorithms based on stochastic simulation for the corresponding model, using stochastic simulation to calculate the individual fitness value, considering actual of problem to solve, using genetic algorithms based on priority coding to make the generated code more effective, and improve the performance of the initial population.Finally, a transportation network in Tianhe district of Guangzhou City is taken as an example, through system simulation, the experiment proves the genetic algorithm based on stochastic simulation can solve the vehicle navigation path planning problem in random environment.