Prediction Model Of Congestion Propagation And Dissipation For Dynamic Route Guidance Under Incidents

With the continued expansion of the urban size and rapid growth of automobile ownership,the congestion in urban areas has been rapidly worsened.At present,the congestion mitigation has become a most pressing task that directly affects the national sustainable development progress for urban areas.The Dynamic Route Guidance(DRG)is one of the most efficient methods to mitigate congestions.It attempts to provide travelers travelling information as well as mitigate congestions through enabling a balanced distribution of vehicles over the road network.The core components of a DRG system include the road weight prediction and the optimal route identification.However,due to the inability to capture congestion characteristics,especially congestion propagation and dissipation characteristics over the network under an incident,the existing DRG approach always identifies the optimal route that is often not the real optimal one.In addition,existing congestion propagation and dissipation models always have a high requirement to the input data and an over-detailed road network,which thus can hardly be supported by any existing data collection systems.Thus,such models cannot be integrated with a real DRG system.In this context,the research in the dissertation is intended to study the characteristics of the congestion propagation and dissipation over the expressway as well as the network under incidents,aiming at serving the traffic management and travelers' information system.By an incorporation of the algorithm for the congestion propagation over the network under incidents within the DRG system,a DRG approach was developed with the prediction of congestion propagation and dissipation characteristics over the network,thus assisting the improvement of the travelling service to travelers and the mitigation of congestions.The research includes the following contents:(1)A method for estimating traffic flows in the whole network with various road classes was developed based on multi-source data.The traffic flow is the basis for analyzing the congestion propagation over the network.However,the coverage of the traffic flow data provided by the Remote Traffic Microwave System(RTMS)and Screen Line Survey is very limited.Thus,they can hardly satisfy the requirement of traffic flows for establishing the congestion propagation and dissipation model in the network.Therefore,this research developed an approach that based on multi-source data to estimate the traffic flow for the whole network with various road classes.Based on the field data in Beijing,six fundamental diagrams were established by applying the Floating Car Data(FCD)speeds,RTMS flows,and Screen Line Survey flows,with a consideration of different road classes,speed limits,and number of lanes.In the method of the traffic flow estimation for the entire network,the condition that the speed and traffic flow were not highly correlated when the speed approached the free-flow-speed was considered.Thus,the historical traffic flow data of the corresponding road class were adopted to adjust the estimated traffic flow.(2)An impact model of incidents on expressways was developed by considering the diverging flow of ramps.An urban expressway exhibits different congestion propagation characteristics from a freeway,due to its high density of ramps and existence of available detour routes.This research analyzed the impact factors to the upstream flow and downstream flow of the wave front based on the shockwave model.The impact factors were categorized into three aspects:the capacity at the bottleneck,background flow,and diverging flow of ramps.Furthermore,by quantifying the influence of the bus proportion,guidance information,queue length,ramp spacing,speeds on expressways and frontage roads,and flow on frontage roads on diverging flows,the impact model of incidents on expressways was developed by considering the diverging flows of ramps.(3)An algorithm for the congestion propagation over the network under incidents was developed.In light of the fact that existing congestion propagation and dissipation models can hardly be supported by existing data collection systems,a practical application oriented model for the congestion propagation in the network under incidents was developed by considering the difference of traffic flow characteristics and physical properties possessed by various road classes.The congestion propagation characteristics at the diverging-node,merging-node,and intersection-node were analyzed by a combination of the shockwave model and Link Transmission Model(LTM).At the intersection-node,the congestion propagation characteristics due to the existence of the shared-lane and conflict points were considered.By a consideration of the physical property and topological structure of the real network,an algorithm for identifying the node type was developed.Furthermore,an algorithm for the congestion propagation over the road network under incidents was designed by considering road classes,including expressways,major arterials,minor arterials,and collectors.(4)The DRG was achieved by predicting the congestion propagation and dissipation characteristics in the network under incidents.In light of the problem that the DRG system and congestion propagation and dissipation models under incidents cannot be integrated effectively,an integration approach was developed on the GIS platform.Based on the prediction model of congestion propagation and dissipation characteristics under incidents,the dynamic travel time of the segment was estimated.During the estimation of the delay time at intersections,the spill-back of the congestion from the downstream was considered.A "multi-optimization" algorithm was developed to obtain the dynamic shortest path under the dynamic weight of the segment based on the Dijkstra's algorithm.The key segment was defined to solve the invalid shortest path problem in existing algorithms.Finally,the traffic control on the western 4th Ring Road in Beijing was selected as a case study in which the congestion propagation over the entire road network and the optimal route identification under the impact of the incident were developed.