...
首页> 外文期刊>Simulation modelling practice and theory: International journal of the Federation of European Simulation Societies >Calibrating a social force based model for simulating personal mobility vehicles and pedestrian mixed traffic
【24h】

Calibrating a social force based model for simulating personal mobility vehicles and pedestrian mixed traffic

机译:基于社会力量模拟个人移动性车辆和行人混合交通校准基于社会的模型

获取原文
获取原文并翻译 | 示例
           

摘要

Personal mobility vehicles (PMVs), such as the Segway, have recently gained remarkable popularity as an alternative transport mode for short-distance trips in indoor and outdoor settings. Before allowing them on shared sidewalks, where the pedestrian and cyclist demand is high, interactions between PMV riders and other shared space users should be properly understood. Further, the designs of shared sidewalks and implementation policies should also be evaluated. Calibrated microscopic simulation tools could facilitate such purposes. This study aims to explore the applicability of a social force based microscopic simulation model, which was originally used to simulate pedestrian movements and interactions, for Segway and pedestrian mixed traffic. The parameters of the model are calibrated with data collected through controlled experiments under different Segway–pedestrian interaction scenarios. Lateral and longitudinal avoidance distances measured from trajectory data collected in a different controlled experiment was used to validate the model for a Segway rider avoiding a pedestrian. The findings of this study suggest that, with proper calibration, the social force model can potentially be used to simulate Segway-like PMVs and pedestrian mixed traffic.
机译:个人移动性车辆(PMV)(如SEGWAY)最近在室内和室外设定中的短程旅行中获得了显着的流行性。在允许他们在共享人行道上之前,行人和骑自行车者需求很高,PMV骑手与其他共享空间用户之间的相互作用应得到正确理解。此外,还应评估共享人行道和实施策略的设计。校准的显微仿真工具可以促进这种目的。本研究旨在探讨基于社会力量的微观模拟模型的适用性,最初用于模拟行人运动和互动,为SEGWAY和行人混合交通。通过在不同的SEGWAY - 行人交互场景下通过受控实验收集的数据进行校准模型的参数。从不同受控实验中收集的轨迹数据测量的横向和纵向避免距离用于验证Segway车手的模型,避免行人。该研究的结果表明,通过适当的校准,社会力量模型可能用于模拟类似SEGWAY的PMV和行人混合交通。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号