针对城市综合公共交通工具数量日益增多,为了推进公共交通一体化发展和提升公交车路网服务水平,提出一种基于GIS(Geographical Information System)路径分析和多目标规划的公交车路网优化方法.以汉中市公交车路网为例,借助在线地图应用...针对城市综合公共交通工具数量日益增多,为了推进公共交通一体化发展和提升公交车路网服务水平,提出一种基于GIS(Geographical Information System)路径分析和多目标规划的公交车路网优化方法.以汉中市公交车路网为例,借助在线地图应用程序接口(Application Programming Interface,API)获取公交线路、乘客出行、站点设施等数据,结合GIS路径分析和多目标规划模型,考虑交通状况、服务人口及现有公交路网等约束条件,对汉中市城市公交路线服务和运营进行最短路径优化.结果表明:在发车频率、客运量和城镇人口总数不变时,公交车站点覆盖率从优化前的40.77%提高到优化后的53.77%,路线覆盖率从优化前的49.10%提高到优化后的62.86%.城市公交车路网的优化,既可提高公交车路网的服务水平,增强公交线路规划的科学性,也可实现城市公共交通资源使用率的最大化.展开更多
针对无人公交场站通行效率低、调度不规律等问题,提出无人场站虚拟场景的构建方法,建立车-路-云一体化环境下的无人公交调度仿真平台。根据高精卫星地图和场站内部场景要素尺寸、位置等信息,利用PreScan软件构建无人场站场景模型,利用Tr...针对无人公交场站通行效率低、调度不规律等问题,提出无人场站虚拟场景的构建方法,建立车-路-云一体化环境下的无人公交调度仿真平台。根据高精卫星地图和场站内部场景要素尺寸、位置等信息,利用PreScan软件构建无人场站场景模型,利用Trucksim软件构建车辆整车模型和横纵向动力学模型;考虑无人公交电池荷电状态(state of charge,SOC)、场站内部充电位和停车位的分布情况,设计了无人公交场站全局调度策略,确定无人公交停车、充电方案和充电标准,基于改进的Dijkstra算法规划无人公交行驶路径。为验证该场景模型及调度规划策略的有效性,基于PreScan、Trucksim和Matlab/Simulink构建无人场站综合仿真平台,结合实车传感器采集的行驶信息,验证了该平台的可靠性。通过综合仿真平台数据和实车测试数据对比可得,无人场站整体通行效率提高了14.30%。展开更多
Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of d...Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.展开更多
文摘针对城市综合公共交通工具数量日益增多,为了推进公共交通一体化发展和提升公交车路网服务水平,提出一种基于GIS(Geographical Information System)路径分析和多目标规划的公交车路网优化方法.以汉中市公交车路网为例,借助在线地图应用程序接口(Application Programming Interface,API)获取公交线路、乘客出行、站点设施等数据,结合GIS路径分析和多目标规划模型,考虑交通状况、服务人口及现有公交路网等约束条件,对汉中市城市公交路线服务和运营进行最短路径优化.结果表明:在发车频率、客运量和城镇人口总数不变时,公交车站点覆盖率从优化前的40.77%提高到优化后的53.77%,路线覆盖率从优化前的49.10%提高到优化后的62.86%.城市公交车路网的优化,既可提高公交车路网的服务水平,增强公交线路规划的科学性,也可实现城市公共交通资源使用率的最大化.
文摘针对无人公交场站通行效率低、调度不规律等问题,提出无人场站虚拟场景的构建方法,建立车-路-云一体化环境下的无人公交调度仿真平台。根据高精卫星地图和场站内部场景要素尺寸、位置等信息,利用PreScan软件构建无人场站场景模型,利用Trucksim软件构建车辆整车模型和横纵向动力学模型;考虑无人公交电池荷电状态(state of charge,SOC)、场站内部充电位和停车位的分布情况,设计了无人公交场站全局调度策略,确定无人公交停车、充电方案和充电标准,基于改进的Dijkstra算法规划无人公交行驶路径。为验证该场景模型及调度规划策略的有效性,基于PreScan、Trucksim和Matlab/Simulink构建无人场站综合仿真平台,结合实车传感器采集的行驶信息,验证了该平台的可靠性。通过综合仿真平台数据和实车测试数据对比可得,无人场站整体通行效率提高了14.30%。
文摘Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.
