基于改进遗传算法的出租车共乘线路规划研究

常规出租车运行模式效率低，增加了城市的拥堵. 针对兰州市出租车搭载率低和行驶路线规划不合理等问题，设计一种以最高搭载率、最短行驶距离为目标的函数，考虑出租车的容量限制、车辆行驶距离限制及上下车人数约束等问题的共乘模式. 建立基于改进遗传算法的出租车共乘线路规划模型. 通过采用锦标赛选择策略、站点片段交叉设计和站点监督式变异等操作对模型进行求解. 最后用兰州市出租车历史轨迹数据进行实验分析，结果表明，运用设计的出租车线路规划模型及改进的遗传算法，能够快速地得到优化路径，实现多辆出租车的规划路径满足最高搭载率及最短距离的要求.

Taxi Sharing Route Planning Based on Improved Genetic Algorithm

Common taxis working mode is inefficiently and increases congestion in the city. Aiming at the current problem of low taxi carrying rate and unreasonable route planning in Lanzhou, this paper designs a taxi sharing route planning model based on improved genetic algorithm, which takes the maximum carrying rate and the shortest driving distance as the objective function and considers the problems of taxi capacity limitation, the driving distance limitation and the number of people getting on and off. The model was solved by means of championship selection strategy, station fragments cross design and station supervised mutation. Finally, the taxi data of Lanzhou were simulated. The research result was shown that the designed taxi sharing route planning model and algorithm could quickly obtain the optimized paths of the multiple taxis, and the planning routes of multiple taxis were meet the requirements of the highest carrying rate and the shortest distance at the same time.