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
Traffic congestion is associated with increased environmental pollutions, as well as reduced socio-economic productivity due to significant delays in travel times. The consequences are worse in least developed countri...Traffic congestion is associated with increased environmental pollutions, as well as reduced socio-economic productivity due to significant delays in travel times. The consequences are worse in least developed countries where motorized road transport networks are often inefficiently managed in addition to being largely underdeveloped. Recent research on traffic congestion has mostly focused on infrastructural aspects of road networks, with little or no emphasis at all on motorists’ on-the-road behavior (MB). The current study thus aimed to bridge this knowledge gap by characterizing traffic jam incidents (TJI) observed over a period of 80 days in Uganda’s Capital City, Kampala. MB as well as road network infrastructural factors such as road blockage (RB), were captured for each of the observed TJI. A total of 483 peak-time TJI were recorded, and exploratory data analysis (EDA) subsequently performed on the TJI dataset. EDA involved Hierarchical clustering analysis (HCA) and K-means clustering of the TJI dataset, as well as a detailed descriptive statistical analysis of both the entire dataset and the emerging TJI clusters. A highlight finding of this study is that 48.2% of the observed TJIs were as a result of on-the-road motorist behavior. Furthermore, the intervention of traffic police officers in a bid to regulate traffic flow was equally responsible for 25.9% of the TJIs observed in this study. Overall, these results indicate that whereas road infrastructural improvement is warranted in order to improve traffic flow, introducing interventions to address inappropriate on-the-road motorists’ behavior could alone improve traffic flow in Kampala, by over 48%. Additionally, in-order to effectively regulate traffic flow in Kampala and other least developed cities with similar traffic congestion management practices, motorists’ on-the-road behavior ought to be factored into any data-driven mechanisms deployed to regulate traffic flow and thus potentially significantly curbing traffic congestion.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Traffic congestion is associated with increased environmental pollutions, as well as reduced socio-economic productivity due to significant delays in travel times. The consequences are worse in least developed countries where motorized road transport networks are often inefficiently managed in addition to being largely underdeveloped. Recent research on traffic congestion has mostly focused on infrastructural aspects of road networks, with little or no emphasis at all on motorists’ on-the-road behavior (MB). The current study thus aimed to bridge this knowledge gap by characterizing traffic jam incidents (TJI) observed over a period of 80 days in Uganda’s Capital City, Kampala. MB as well as road network infrastructural factors such as road blockage (RB), were captured for each of the observed TJI. A total of 483 peak-time TJI were recorded, and exploratory data analysis (EDA) subsequently performed on the TJI dataset. EDA involved Hierarchical clustering analysis (HCA) and K-means clustering of the TJI dataset, as well as a detailed descriptive statistical analysis of both the entire dataset and the emerging TJI clusters. A highlight finding of this study is that 48.2% of the observed TJIs were as a result of on-the-road motorist behavior. Furthermore, the intervention of traffic police officers in a bid to regulate traffic flow was equally responsible for 25.9% of the TJIs observed in this study. Overall, these results indicate that whereas road infrastructural improvement is warranted in order to improve traffic flow, introducing interventions to address inappropriate on-the-road motorists’ behavior could alone improve traffic flow in Kampala, by over 48%. Additionally, in-order to effectively regulate traffic flow in Kampala and other least developed cities with similar traffic congestion management practices, motorists’ on-the-road behavior ought to be factored into any data-driven mechanisms deployed to regulate traffic flow and thus potentially significantly curbing traffic congestion.
