Highlights Develop an analytical model to evaluate the data collection efficiency (DCE) of instrumented vehicles (IVs), according to stochastic geometry theory. Develop a simulation approach for IV data acquisition, by imitating the vehicle-detection mechanism of an IV based on a dataset of full sample vehicle trajectories. Conduct numerical analyses to validate the proposed DCE analysis model for IVs under various traffic scenarios.

    Abstract Emerging instrumented vehicles (IVs), when equipped with high-precision positioning devices (e.g., DGPS) and ranging sensors (e.g., radar, LIDAR, cameras), are capable of generating high-quality traffic data. This study evaluates the efficiency of such data-collection procedures; this remains largely unknown because variable penetration rates of IVs rarely arise in the real world. We propose an analytical model that establishes a quantitative relationship between the ratio of collected trajectory points to total traffic trajectory points (RCT) and the IV penetration rate, according to stochastic geometry theory. With this, the data-collection efficiency (DCE) of IVs can be effectively evaluated. A simulation approach is developed to generate IV data and thereby validate the proposed analytical model, using a comprehensive set of traffic scenarios; these data consist of eight micro-trajectory datasets from the next-generation simulation (NGSIM) program and four typical sensor IV deployments. The numerical analysis demonstrates that the model perfectly reflects the simulated IV data for all traffic scenarios. In addition, an analytical comparison of the DCEs for fixed sensors, probe vehicles, and IVs reveals that IVs are the most efficient method for collecting traffic data in road networks. This study proposes a theory to predict the percentage of trajectory points that can be collected by a certain percentage of IVs within the entire traffic flow.


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    Title :

    An analytical model for quantifying the efficiency of traffic-data collection using instrumented vehicles


    Contributors:
    Cao, Peng (author) / Xiong, Zhiqiang (author) / Liu, Xiaobo (author)


    Publication date :

    2022-01-03




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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