In a connected-vehicle environment, wireless subsecond data exchange connects vehicles, the infrastructure, and travelers’ mobile devices. These data have the promise to transform the geographic scope, precision, and latency of transportation system control; fulfillment of that promise could result in significant safety, mobility, and environmental benefits. However, the new data influx also has the potential to overburden legacy computational and communication systems. Although connected-vehicle technology can facilitate ubiquitous system coverage, the existing prediction methods, computational platforms, and data management methods are insufficient to process the data within a reasonable time frame for real-time predictions. An investigation of the ways in which advanced (big-data) analytics might be applied to realize the full potential of connected-vehicle technology is particularly relevant now as this technology evolves from research to deployment. This paper presents an approach combining big-data graph analytics with high-performance computing to predict traffic congestion by analyzing nearly 4 billion basic safety messages generated by the safety pilot model deployment conducted in 2012–2013. This paper provides an alternative approach for predicting congestion in 30.5-m segments anywhere on the network at 1-min intervals 30 to 60 min before actual congestion over a time window of 1 h. Despite sparseness of data, the proposed framework predicted highly congested locations 40% of the time. Severity of congestion was predicted with an accuracy of 77%. This combination of rapid computation and predictive accuracy may provide significant value in future real-time decision support systems that leverage connected-vehicle data.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Predicting Congestion States from Basic Safety Messages by Using Big-Data Graph Analytics


    Additional title:

    Transportation Research Record


    Contributors:


    Publication date :

    2019-04-04




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Vehicular radar interference management using basic safety messages

    VA VUTHA / SHIMIZU TAKAYUKI / BANSAL GAURAV | European Patent Office | 2020

    Free access

    Congestion predicting system and congestion predicting method

    European Patent Office | 2018

    Free access

    CONGESTION PREDICTING SYSTEM AND CONGESTION PREDICTING METHOD

    NING RUI / KATOU MANABU / FUJIWARA MASAYASU | European Patent Office | 2017

    Free access

    Risk Quantification for Automated Driving using Information from V2V Basic Safety Messages

    Cowlagi, Raghvendra V. / Debski, Rebecca C. / Wyglinski, Alexander M. | IEEE | 2021


    Conflict Identification Using Speed Distance Profile on Basic Safety Messages

    Wu, Di / Zhang, Li / Whalin, Robert et al. | ASCE | 2022