The primary objective of this paper was to develop a control strategy of variable speed limits (VSLs) to reduce rear-end collision risks near freeway recurrent bottlenecks. The risks of rear-end collisions were estimated using a crash risk prediction model that is specifically developed for rear-end collisions in freeway bottleneck areas. The effects of the VSL control strategy were evaluated using a cell transmission model. Several control factors were tested, including the start-up threshold of the collision likelihood, the target speed limit, the speed change rate, and the speed difference between adjacent links. A genetic algorithm was used to optimize critical control factors. For the high demand scenario, the proposed control strategy used 25% of the maximum collision likelihood for the start-up threshold, 35 mi/h for the target speed limit, 10 mi/h per 30 s for the speed change rate, and 10 mi/h for the speed difference between different links. For the moderate demand scenario, the strategy used 20% of the maximum collision likelihood for the start-up threshold, 40 mi/h for the target speed limit, 15 mi/h per 30 s for the speed change rate, and 10 mi/h for the speed difference between different links. The results of comparative analyses suggested that the proposed control strategy outperformed other strategies in reducing the rear-end collision risks near freeway recurrent bottlenecks. With the proposed control strategy, the VSL control reduced the rear-end crash potential by 69.84% for the high demand scenario and by 81.81% for the moderate demand scenario.


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    Titel :

    Development of a Control Strategy of Variable Speed Limits to Reduce Rear-End Collision Risks Near Freeway Recurrent Bottlenecks


    Beteiligte:
    Li, Zhibin (Autor:in) / Liu, Pan (Autor:in) / Wang, Wei (Autor:in) / Xu, Chengcheng (Autor:in)


    Erscheinungsdatum :

    01.04.2014


    Format / Umfang :

    1309520 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch