This paper proposes an adaptive agent model with a hybrid routing selection strategy for studying the road-network congestion problem. We focus on improving those severely congested links. Firstly,a multi-agent system...This paper proposes an adaptive agent model with a hybrid routing selection strategy for studying the road-network congestion problem. We focus on improving those severely congested links. Firstly,a multi-agent system is built,where each agent stands for a vehicle,and it makes its routing selection by considering the shortest path and the minimum congested degree of the target link simultaneously. The agent-based model captures the nonlinear feedback between vehicle routing behaviors and road-network congestion status.Secondly,a hybrid routing selection strategy is provided,which guides the vehicle routes adapting to the realtime road-network congestion status. On this basis, we execute simulation experiments and compare the simulation results of network congestion distribution,by Floyd agent with shortest path strategy and our proposed adaptive agent with hybrid strategy. The simulation results show that our proposed model has reduced the congestion degree of those seriously congested links of road-network. Finally,we execute our model on a real road map. The results finds that those seriously congested roads have some common features such as located at the road junction or near the unique road connecting two areas. And,the results also show an effectiveness of our model on reduction of those seriously congested links in this actual road network. Such a bottom-up congestion control approach with a hybrid congestion optimization perspective will have its significance for actual traffic congestion control.展开更多
【目的】随志愿者地理信息系统的快速发展,高现势性众源路网已成为智慧城市建设的重要数据来源,其选取的效率与效果成为影响多尺度数据服务的关键因素。已有的路网选取方法大多基于数据属性信息判断道路重要性,十分合理且有效,但是,实...【目的】随志愿者地理信息系统的快速发展,高现势性众源路网已成为智慧城市建设的重要数据来源,其选取的效率与效果成为影响多尺度数据服务的关键因素。已有的路网选取方法大多基于数据属性信息判断道路重要性,十分合理且有效,但是,实际数据往往存在属性缺失问题,一定程度上限制了方法的适用性。【方法】针对此问题,本文提出一种属性信息缺失条件下的众源路网空间句法自动建模与选取方法。首先,基于开放街道地图(Open Street Map)中心线数据,开发程序自动执行几何化简、拓扑修正与伪节点处理,批量生成整个城市的空间句法线段模型,并基于模型计算整合度、选择度等空间句法指标;随后构建Stroke,并提取几何特征;进一步,创新性地提出2项复合指标:基于路径单元的标准化角度整合度(SNAIN)与基于路径单元的标准化角度选择度(SNACH),以联合刻画道路的拓扑可达性与几何连续性。在此基础上,应用结合熵权法与层次分析法(EW-AHP)的主客观集成赋权方法,确定综合指标的权重,实现道路的重要性排序。最后,通过断头路识别与网格密度修补,进一步提高路网的连通性和完整性。【结果】以兰州(带状道路网)和成都(环形放射状道路网)为案例验证,结果表明:在道路属性信息缺失的条件下,本文方法能够有效识别城市主干路网,其与OSM道路等级匹配准确率分别达到兰州0.9421、成都0.9711;修补后兰州市路网连通率由1.0582提升至1.0864,成都市路网连通率由1.1086提升至1.1198(成都在所选尺度内的断头路完全消除)。消融实验表明,SNAIN更有利于提升全局连通性,SNACH有助于增强几何连续性,二者并用能在连通性与空间覆盖间取得平衡。【结论】本文方法为属性信息不完整情形下的大规模城市路网快速建模与选取提供了新的理论支持和技术路径。展开更多
基金Sponsored by the Natural Science Foundation of Hunan ProvinceChina(Grant No.13JJ3049)the Fundamental Research Funds for the Central Universities(Grant No.2012AA01A301-1)
文摘This paper proposes an adaptive agent model with a hybrid routing selection strategy for studying the road-network congestion problem. We focus on improving those severely congested links. Firstly,a multi-agent system is built,where each agent stands for a vehicle,and it makes its routing selection by considering the shortest path and the minimum congested degree of the target link simultaneously. The agent-based model captures the nonlinear feedback between vehicle routing behaviors and road-network congestion status.Secondly,a hybrid routing selection strategy is provided,which guides the vehicle routes adapting to the realtime road-network congestion status. On this basis, we execute simulation experiments and compare the simulation results of network congestion distribution,by Floyd agent with shortest path strategy and our proposed adaptive agent with hybrid strategy. The simulation results show that our proposed model has reduced the congestion degree of those seriously congested links of road-network. Finally,we execute our model on a real road map. The results finds that those seriously congested roads have some common features such as located at the road junction or near the unique road connecting two areas. And,the results also show an effectiveness of our model on reduction of those seriously congested links in this actual road network. Such a bottom-up congestion control approach with a hybrid congestion optimization perspective will have its significance for actual traffic congestion control.
文摘【目的】随志愿者地理信息系统的快速发展,高现势性众源路网已成为智慧城市建设的重要数据来源,其选取的效率与效果成为影响多尺度数据服务的关键因素。已有的路网选取方法大多基于数据属性信息判断道路重要性,十分合理且有效,但是,实际数据往往存在属性缺失问题,一定程度上限制了方法的适用性。【方法】针对此问题,本文提出一种属性信息缺失条件下的众源路网空间句法自动建模与选取方法。首先,基于开放街道地图(Open Street Map)中心线数据,开发程序自动执行几何化简、拓扑修正与伪节点处理,批量生成整个城市的空间句法线段模型,并基于模型计算整合度、选择度等空间句法指标;随后构建Stroke,并提取几何特征;进一步,创新性地提出2项复合指标:基于路径单元的标准化角度整合度(SNAIN)与基于路径单元的标准化角度选择度(SNACH),以联合刻画道路的拓扑可达性与几何连续性。在此基础上,应用结合熵权法与层次分析法(EW-AHP)的主客观集成赋权方法,确定综合指标的权重,实现道路的重要性排序。最后,通过断头路识别与网格密度修补,进一步提高路网的连通性和完整性。【结果】以兰州(带状道路网)和成都(环形放射状道路网)为案例验证,结果表明:在道路属性信息缺失的条件下,本文方法能够有效识别城市主干路网,其与OSM道路等级匹配准确率分别达到兰州0.9421、成都0.9711;修补后兰州市路网连通率由1.0582提升至1.0864,成都市路网连通率由1.1086提升至1.1198(成都在所选尺度内的断头路完全消除)。消融实验表明,SNAIN更有利于提升全局连通性,SNACH有助于增强几何连续性,二者并用能在连通性与空间覆盖间取得平衡。【结论】本文方法为属性信息不完整情形下的大规模城市路网快速建模与选取提供了新的理论支持和技术路径。
