Research On Traffic Flow Modeling And Real-time Guidance Strategy Based On Cellular Automaton

With the improvement of the level of social productive forces, transportationindustry is more and more important in human society because it influents thecivilization degree of society and the sustainable development of economy. However,the rapid development of transportation industry brings great convenience to our life,as well as several social problems, such as traffic congestion, traffic accidents andenvironmental pollution. Efforts on the transportation infrastructure are not sufficientto solve the transportation problems. The systematic and scientific research of trafficsystem is also very important for the reveal of transportation system for the laws andcharacteristics and the guidance of development and management of transportationsystem. Traffic flow theory provides theoretical basis to explain the trafficphenomena, analyze the traffic problems and guide the traffic management, whichcan help to solve the social problems, such as traffic congestion. In this dissertation,traffic flow modeling, quantitative analysis of traffic flow complexity and trafficflow guidance strategy based on information feedback are focused. The research inthe dissertation has academic significance and promising application.(1) Traffic flow cellular automaton model based on brake light rulesTo investigate the interaction of vehicles from micro aspect and the influence ofthe interaction on traffic flow evolution from macro aspect, an improved brake lightmodel is proposed based on Tian model considering the influence of deterministicdeceleration on randomization and improving of modeling rules for drivingbehaviors, with which the generation mechanism of synchronized flow inthree-phase traffic theory and the mechanism of phase transitions betweensynchronized flow and wide moving jam are explored. The improved brake lightmodel modifies the acceleration and randomization rules. The modifications makethe interaction between vehicles more realistic and avoid the phenomenon of over-deceleration. The qualitative analyses of the fundamental diagram andspatial-temporal diagrams show that the new model can reproduce the three trafficphases: free flow, synchronized flow and wide moving jam. In addition, the newmodel can well describe the complexity of traffic flow evolution.(2) Quantitative complexity analysis of traffic flow based on the multi-scaleentropy methodThe spatial-temporal diagrams can describe the complexity of traffic flowevolution qualitatively. However, qualitative analysis is not enough for thecomplexity analysis of traffic flow. To describe the complexity of traffic flowquantitatively for further investigation, such as the comparison of complexity oftraffic flow between different phases and the influence of parameters of traffic flowmodel on the complexity of traffic flow, multi-scale entropy method is used toquantitatively analyze the complexity of time headway at different time scales. Thetime series of time headway are generated based on NS model and our improvedbrake light model, respectively. Analysis results show that for NS model, the vehicledensity has larger influence on the complexity of time headway than randomizationprobability does; for our improved brake light model, the emergence of synchronizedflow will increase the complexity of time headway greatly.(3) Traffic flow guidance strategy based on information feedbackTo fulfill successful traffic flow guidance, reasonable information feedbackstrategies based on real-time traffic information are needed. In addition, based on thetraffic flow automaton model, traffic flow simulation and evolution with differentscenarios can be achieved to provide basis for the validation of traffic informationfeedback strategies. In this dissertation, the application of Intelligent TransportationSystem (ITS) is discussed and the influence of the coverage of Internet of Vehicleson the performance of typical information feedback strategies is analyzed, which canprovide a reference for the selection of information feedback strategies with differentcoverage of Internet of Vehicles. And a new information feedback strategy is proposed, which is the Weighted Mean Velocity Feedback Strategy (WMVFS).Based on the NS model and our improved brake light model, the performances ofWMVFS and typical information feedback strategies are compared and thecharacters of WMVFS are analyzed. Results show that our new strategy has betterperformance and better robustness, which means WMVFS has better practicabilityand applicability.The main contributions are as follows:(1) The improved brake light model isproposed, which can reveal the random character of phase transitions betweensynchronized flow and wide moving jam.(2) The multi-scale entropy method is usedin the analysis of traffic flow, which can quantitatively measure the complexityanalysis of traffic flow.(3) The influence of the proportion of float cars on theperformance of typical information feedback strategies is analyzed, which canprovide a reference for the selection of information feedback strategies in scenarioswith Internet of Vehicles.(4) The Weighted Mean Velocity Feedback Strategy isproposed, which has good performance for traffic flow guidance in differentapplication scenarios.