摘要
通过研究制约公交系统运行效率的问题,提出了一种旨在快速响应乘客出行需求的"小粒度"频繁调度方法,构建了以"小粒度"时段为调度周期的多目标优化模型.针对模型特征设计了实现在线调度的改进遗传算法,结合启发信息控制变异位置、增加修复算子,解决了染色体变长和跨维搜索难题.利用仿真试验验证了方法的有效性,结果表明,算法能在较短时间产生调度方案,满足在线调度要求;与传统的基于统计数据的调度方案相比,模型能有效优化出行成本和运营成本.
In order to address variable passengers＇ needs quickly, a multi-objective optimal model was established, which would produce timetables for buses frequently in a short period of time on the assumption that passengers＇ needs have been obtained through ITS. And an advanced genetic algorithm was designed to solve the program on-line. The simulation shows that a dispatching plan can be worked out in a short time with the proposed model and algorithm, the effects are better compared to those took by traditional statistical approach.
出处
《武汉理工大学学报（交通科学与工程版）》
2009年第3期430-433,490,共5页
Journal of Wuhan University of Technology（Transportation Science & Engineering）
基金
国家自然科学基金项目资助(批准号:50478088)
关键词
APTS
快速响应
优化模型
公交智能调度
改进遗传算法
APTS
quick response
optimal model
intelligent scheduling for public transportation
advanced genetic algorithm
