The Research On The Cooperative Optimization And Traffic Guidance Technologies Of Traffic Flow For Urban Intelligent Transportation System

With the accelerated urbanization and the development of the automobile industry, there is the increasing requirement for the traffic throughout of the urban in contrast to the current urban road capacity. The traffic congestion problem has become serious gradually for many metropolises. Thus it is an important and extensive method in many countries to use the intelligent transport system to control traffic and to induce vehicle flows so that the road congestion may be mitigated and the traffic may be more efficiency. Taking the large-scale urban complex traffic net as research background, the paper adapts the theories and methods such as cooperative control and optimization, fuzzy control and dynamic re-planning to research the critical problems in intelligent transport system such as the signal phase switch, region traffic balance, global traffic optimization, the data acquisition and the data process for vehicle sensor network, the optimal path planning and task decomposition of the traffic guidance and so on. By this research, the paper tries to construct a completed and efficient traffic management and passing mechanism. The main work is as follows:The paper firstly constructs the intelligent transport model to analyze the traffic flow of the transport network including continuous vehicle flows and discrete traffic signal. Then the difficulty and problem of the control and guidance in urban intelligent transport system is analyzed based on the characteristics and the application background of urban transport network. To solve these problems, a design framework of cooperative control and guidance is presented. In the design, the vehicle wireless nodes collect the transport information of sensor nodes along the urban roads. Then a new confidence level based adaptive particle filter algorithm is proposed to predict the short-term flow of traffic using the collected data. After it is transferred or relayed by vehicle wireless nodes, which can be used for cooperative traffic signal control and traffic guidance.In order to provide the real-time, fully reliable information about dynamic traffic report and vehicle flows for the traffic control, a data collection algorithm based on the concentric trees by using vehicle wireless nodes considering the mobility and the power efficiency. This algorithm can balance the networks load, reduce the power consuming of the nodes and increase the reliability for data transmitting. Then a confidence-level-based new adaptive particle filter (CAPF) algorithm is proposed in this paper to increase the prediction accuracy of traffic flow. In this algorithm the idea of confidence interval is utilized. The least number of particles for the next time instant is estimated according to the confidence level and the variance of the estimated state. CAPF can effectively reduce the computation while ensuring accuracy of online real-time and accurate estimates of traffic flow and improving the accuracy of the traffic flow forecasting.The cooperative control strategy with the intelligent learning ability was proposed to induce the urban traffic load and optimize global traffic property. Considering the discontinuity of the vehicle flows density, the paper proposes the affine traffic optimization index for the subsection of the urban traffic road and made the signal phase switch by using fuzzy rules, then obtain the subsection linear parameter. Secondly the traffic information of the adjacent Crossroads was used to construct the cooperative control items and balance the area load. Finally the global optimal traffic index is obtained by introducing the fuzzy Q-learning mechanism to adjust the gains of the cooperative control and the local feedback control in order to achieve optimism of traffic. In this paper, the learning reward is derived from the estimated value of traffic flow. The optimal cooperative control strategy with learning can obtain optimal traffic flow through the feedbacks of the traffic control nodes and the cooperative control acting of the neighbor nodes.Apart from research about controlling the urban vehicle flows by using traffic signal, this paper also analyzed an intelligent Traffic stream guidance, discussed the key function of traffic distribution path planning and finally proposed a way to address the traffic guidance decomposition and path selection issue. First, the large-scale traffic stream guidance is described by introducing WAndOrTree. Then the WAndOrTree is turned into AOE network. On the basis of AOE network, the traffic stream guidance is decomposed, considering the timing constraints of the guidance task, which can ensure the consistency of the task execution and meet dynamic real-time requirement for task planning. Furthermore, a group of optimal path set was obtained by using reverse multi-objective search. Then the Traffic stream guidance reused the remaining unchanged planning information with incremental way. By this way, the move path between the current position and destination location can be adjusted faster. The proposed traffic guidance is more scalable for large-scale city with real-time requirement.