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
针对建筑机器人在施工现场获取地图信息时间长且需要规划出一条全局的、能实时避障的路径等问题,该文提出了一种应用建筑信息模型(building information model,BIM)技术建立导航地图并进行路径规划的算法。根据BIM模型中的信息对传统RR...针对建筑机器人在施工现场获取地图信息时间长且需要规划出一条全局的、能实时避障的路径等问题,该文提出了一种应用建筑信息模型(building information model,BIM)技术建立导航地图并进行路径规划的算法。根据BIM模型中的信息对传统RRT算法进行优化改进,提出了IRRT(improved rapid-exploration random tree)算法。首先将原有的固定步长改为动态步长,通过判断与目标点的远近界定步长大小,避免了节点的盲目扩张;其次,对随机采样点的生成范围进行了约束,并设置一个同时考虑目标点和随机点的权重来解决传统RRT算法中新生成点仅由随机采样点单一决定的问题;算法陷入最小值时选取随机扰动策略进行逃脱;最后在全局路径的相邻节点间使用动态窗口法进行局部避障。实验仿真结果表明IRRT算法比传统RRT算法在搜索速度上快了3倍多,平均路径比改进前减少25.56%,平均节点减少8.92%,加入动态窗口法后有效提高了机器人实时避障能力,更适合多变的室内环境使用。展开更多
移动自组织网络是一种移动通信和计算机网络相结合的网络,用户节点可以在网络内随意移动并且保持通信,因此移动自组织网络中节点移动的随意性给研究网络中的路由带来了困难;另一方面,移动自组织网络中节点之间的社会关系对路由过程有直...移动自组织网络是一种移动通信和计算机网络相结合的网络,用户节点可以在网络内随意移动并且保持通信,因此移动自组织网络中节点移动的随意性给研究网络中的路由带来了困难;另一方面,移动自组织网络中节点之间的社会关系对路由过程有直接影响.本文采用最可靠路径的原则来计算移动自组织网络中的最优路径进行路由,该方法通过网络节点之间的邻接可能性来表示最优路径,通过节点的移动方案和与其他节点之间的社会关系强度对邻接可能性进行计算.最后,通过仿真实验,将本文提出的路由方法与常用的最优链路状态路由协议OLSR(Optimized Link State Routing)进行比较分析,结果显示本文提出的方法具有较好的性能.展开更多
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.
文摘针对建筑机器人在施工现场获取地图信息时间长且需要规划出一条全局的、能实时避障的路径等问题,该文提出了一种应用建筑信息模型(building information model,BIM)技术建立导航地图并进行路径规划的算法。根据BIM模型中的信息对传统RRT算法进行优化改进,提出了IRRT(improved rapid-exploration random tree)算法。首先将原有的固定步长改为动态步长,通过判断与目标点的远近界定步长大小,避免了节点的盲目扩张;其次,对随机采样点的生成范围进行了约束,并设置一个同时考虑目标点和随机点的权重来解决传统RRT算法中新生成点仅由随机采样点单一决定的问题;算法陷入最小值时选取随机扰动策略进行逃脱;最后在全局路径的相邻节点间使用动态窗口法进行局部避障。实验仿真结果表明IRRT算法比传统RRT算法在搜索速度上快了3倍多,平均路径比改进前减少25.56%,平均节点减少8.92%,加入动态窗口法后有效提高了机器人实时避障能力,更适合多变的室内环境使用。
文摘移动自组织网络是一种移动通信和计算机网络相结合的网络,用户节点可以在网络内随意移动并且保持通信,因此移动自组织网络中节点移动的随意性给研究网络中的路由带来了困难;另一方面,移动自组织网络中节点之间的社会关系对路由过程有直接影响.本文采用最可靠路径的原则来计算移动自组织网络中的最优路径进行路由,该方法通过网络节点之间的邻接可能性来表示最优路径,通过节点的移动方案和与其他节点之间的社会关系强度对邻接可能性进行计算.最后,通过仿真实验,将本文提出的路由方法与常用的最优链路状态路由协议OLSR(Optimized Link State Routing)进行比较分析,结果显示本文提出的方法具有较好的性能.
