BibSonomy publications for /author/ZhangTue Feb 14 04:45:14 CET 2012Ieee Intelligent Systems#nov#619--23DynaCAS: Computational Experiments and Decision Support for ITS232008Accurate, reliable, and timely traffic information is critical for deployment and operation of intelligent transportation systems (ITSs). Traffic forecasting for travelers and traffic operators should become at least as useful and convenient as weather reports. In the US, the Federal Highway Administration (FHWA) has envisioned a real-time traffic estimation and prediction system (TrEPS) as an ITS Support platform that resides at traffic management centers (TMCs) for dynamic route assignment (DRA) and other transportation operations. To enable ITS deployment for urban traffic control and management in China, in 1999 the Chinese Academy of Sciences Outlined a research agenda to develop related intelligent systems and technology.(1) A central component of this agenda was a REPS called DynaCAS (dynamic traffic assignment based on complex adaptive systems). Here, we briefly introduce DynaCAS and its open source counterpart DynaChina, emphasizing how they differ from other TrEPS projects.Tue Feb 14 04:44:29 CET 2012Advanced Traffic Management Systems For Freeways and Traffic Signal Systems 2002: Highway Operations, Capacity, and Traffic Control1811139--147Optimizing traffic network signals around railroad crossings - Model validations2002A model entitled SOURCAO (signal optimization under rail crossing safety constraints) is discussed. There are two objectives in SOURCAO design: highway rail grade crossing (HRGC) safety consideration and highway traffic delay reduction around the crossings. The first step in SOURCAO is to choose a proper preemption phase sequence to promote HRGC safety. This is done by the inference engine in an intelligent agent. The second step in SOURCAO is to find the optimized signal phase length to reduce highway traffic delay. The optimization process uses an objective function of the network traffic delay. The delay is approximated and represented by a multilayer perceptron neural network. The system inputs are online surveillance detector data and HRGC closure information. The system architecture of SOURCAO is briefly discussed. Various types of surveillance schemes in the proposed delay model are introduced. Emphasis is on validating the various models applied in SOURCAO. First, the proposed delay model is compared with queue delays obtained from the TSIS/CORSIM traffic simulation package. Second, the neural network delay forecast is verified through a cross-validation procedure. Finally, the objectives of SOURCAO are evaluated by TSIS/CORSIM. The average network delay for 20 runs of simulation is reduced over 8% (P = 0.05; t-test), and the unsafe queue time of the highway vehicle on HRGC in a test case is reduced.Mon Feb 13 15:23:25 CET 2012Proc. 33rd Int. Conf. Current Trends Theory \& Practice Comput. Sci. 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