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Road Risk Modeling and Cloud-Aided Safety-Based Route Planning.

Zhaojian Li, Ilya Kolmanovsky, Ella Atkins

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    This summary is machine-generated.

    This study introduces a safety-based route planner using vehicle-to-cloud-to-vehicle (V2C2V) connectivity. It balances travel time and road risk index (RRI) for personalized navigation, enhancing road safety.

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    Area of Science:

    • Intelligent Transportation Systems
    • Traffic Safety Engineering
    • Computer Science

    Background:

    • Traditional route planning often prioritizes travel time over safety.
    • Assessing real-time road risk is challenging due to dynamic factors.
    • Vehicle-to-everything (V2X) communication offers potential for enhanced navigation.

    Purpose of the Study:

    • To develop a safety-based route planner integrating vehicle-to-cloud-to-vehicle (V2C2V) connectivity.
    • To balance user-defined preferences between travel time and road risk index (RRI).
    • To predict and incorporate real-time road segment risk into route planning.

    Main Methods:

    • Utilized a hybrid neural network to mine accident data for predicting static RRI.
    • Incorporated real-time factors (time, day, weather) to dynamically adjust RRI.
    • Formulated route planning as a multiobjective network flow problem, reduced to mixed-integer programming.
    • Proposed a V2C2V implementation for real-time data access and computation.

    Main Results:

    • Demonstrated a functional safety-based route planner using V2C2V communication.
    • Successfully balanced travel time and RRI based on user preferences in a case study.
    • Showcased dynamic route adjustments favoring either time or safety.

    Conclusions:

    • V2C2V connectivity enables effective safety-based route planning.
    • The proposed method allows for personalized route selection balancing time and safety.
    • Real-time risk assessment significantly improves the relevance of route planning.