Frameworks of Data-Based Dynamic Optimality for Complex Urban Traffic Flow = 基于数据的复杂城市交通流动态优化框架研究

Larger scale, constraint complexity and more uncertainty of urban traffic system are posing great challenges on dynamic traffic flow optimality problem. On the one hand, it becomes more and more difficult to build accurate mathematical optimal models; on the other hand, the basic demand data needed by dynamic traffic flow optimality is very difficult to get entirely and accurately. While in the actual traffic management system, there are abundant historical and real-time traffic flow data from kinds of detectors. The traffic demand information and the traffic flow state information are hidden in them and can be extracted by methods with rational traffic flow detector layout. So, it is necessary to explore innovative theories and methodologies of data-based dynamic optimality problem. In this paper, based on a brief survey of the usual dynamic traffic flow optimality methods, a data-based optimal framework is proposed. Related theories for this optimal framework are discussed as well.