This paper compares the performance of a traffic responsive intersection controller which combines vehicle Global Positioning System (GPS) data and inductive loop information, to fixed-time, inductive loop, and GPS based controllers. The INRIX Global Traffic Scorecard reports that vehicles spent up to 42% of their travel time in congested traffic in 2016. Inefficient signal timing choices by isolated intersection controllers contribute to traffic delays, causing severe negative impacts on the economy and environment. Signal timings can be improved using vehicles’ GPS information combined with vehicle flow information from inductive loops to overcome the control action deficit at isolated intersections. This proposed new signal control algorithm is beneficial for traffic engineers and governmental agencies, as optimised traffic flow can reduce fuel consumption and emissions. The proposed traffic responsive Hybrid Vehicle Actuation (HVA) algorithm uses position and heading data from vehicle status broadcasts, and inferred velocity information to determine vehicle queue lengths and detect vehicles passing through the intersection to actuate intersection signal timings. When vehicle broadcast data are unavailable, HVA uses inductive loop data. Microscopic simulations comparing HVA to fixed-time control, inductive Loop Based Vehicle Actuation (Loop-VA) and GPS Based Vehicle Actuation (GPS-VA) on four urban road networks were carried out to see how the proposed HVA algorithm performs compared to existing control strategies. The results show that HVA is an effective alternative to traditional intersection control strategies, offering delay reductions of up to 32% over Loop-VA, for networks with 0−100% connected vehicle presence.


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    Title :

    A Hybrid Traffic Responsive Intersection Control Algorithm Using Global Positioning System and Inductive Loop Data


    Contributors:
    Rafter, C (author) / Anvari, B (author) / Box, S (author)

    Publication date :

    2018-01-15


    Remarks:

    In: Proceedings of the Transportation Research Board 97th Annual Meeting. The National Academies Press: Washington DC, United States. (2018)


    Type of media :

    Paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



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