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Implementing a Kalman Filtering Dynamic O-D Algorithm within Paramics- Analysing Quadstone Won Efforts for the Dynamic O-D Estimation Problem

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  • Garcia, Reinaldo C.

Abstract

This Project moves forward from the accumulated knowledge garnered from MOU 4121. Over the last year a good deal of insights was obtained about the implementation of dynamic Origin-and-Destination (O-D) estimation. Under MOU 4121, the Kalman Filtering (KF) algorithm described in Hu et al. (2000) for dynamic O-D estimation was implemented. In this research, the implemented algorithm is incorporated to the microscopic traffic simulator Paramics. Paramics offers important and unprecedented features, such as high performance and scalability, to handle realistic real world traffic networks under ITS (Intelligent Transportation Systems). Nevertheless, Paramics has its own limitations, particularly relating to the model stability to interface with dynamic routing protocols, and dynamic O-D estimation. Hu et al. (2000) implemented their KF algorithm for dynmic O-D estimation and tested it for a freeway system. Needing the KF algorithm inputs such as link traffic counts and assignment matrices to be applied, Hu et al. (2000) used the DYNASMART mesoscopic traffic simulator to obtain the necessary input data for the KF algorithm. In this research, being Caltrans (The California Transportation Department) the main sponsor of this project, the implementation of the algorithm was made applying solely the Paramics traffic simulator (not using therefore the DYNASMART simulator). Doing so, it was intended to avoid future training costs to Caltrans, as it is already applying mainly Paramics through its own districts. To implement the KF algorithm described in Hu et al. (2000) within Paramics, the following APIs (Advanced Programming Interfaces) were developed: net_action, vehicle_action, vehicle_link_action and net_post_action. These APIs will be explained in detail in this report. Furthermore, this research will discuss the own proposed development of Quadstone (the developer of Paramics) for the dynamic O-D estimation algorithm.

Suggested Citation

  • Garcia, Reinaldo C., 2003. "Implementing a Kalman Filtering Dynamic O-D Algorithm within Paramics- Analysing Quadstone Won Efforts for the Dynamic O-D Estimation Problem," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6vf61301, Institute of Transportation Studies, UC Berkeley.
    • Handle: RePEc:cdl:itsrrp:qt6vf61301
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    References listed on IDEAS

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