A crash severity prediction tool for use with a vehicle. The vehicle is equipped with a native crash severity prediction application and includes a user interface configured to receive a user destination, a GPS unit configured to generate location coordinates of the vehicle and a display configured to show a road map depicting roadways between a user start location and the user destination. The native crash severity computer application is communicably connected to a cloud based crash severity prediction computer application configured to receive the location coordinates and the road map. The cloud based application includes a trained artificial neural network (ANN) configured to predict a crash severity level based on real time weather conditions, light conditions, road surface conditions, and day of the week. A crash severity index is transmitted to the native crash severity prediction application and is rendered on a vehicle display.


    Access

    Download


    Export, share and cite



    Title :

    Real time traffic crash severity prediction tool


    Contributors:
    RATROUT NEDAL (author) / MANSOOR UMER (author) / ALAM GULZAR (author)

    Publication date :

    2022-09-13


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G01C Messen von Entfernungen, Höhen, Neigungen oder Richtungen , MEASURING DISTANCES, LEVELS OR BEARINGS / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



    Real-Time Framework to Predict Crash Likelihood and Cluster Crash Severity

    Islam, Md Rakibul / Abdel-Aty, Mohamed / Islam, Zubayer et al. | Transportation Research Record | 2023



    Real-Time Crash Prediction Model for Application to Crash Prevention in Freeway Traffic

    Lee, Chris / Hellinga, Bruce / Saccomanno, Frank | Transportation Research Record | 2003


    Utilizing Ensemble Learning Methods in Real-Time Traffic Crash Prediction

    Xue, Mengdi / Huang, Jie / Gao, Zhen et al. | ASCE | 2018


    TSDCN: Traffic safety state deep clustering network for real‐time traffic crash‐prediction

    Li, Haitao / Bai, Qiaowen / Zhao, Yonghua et al. | Wiley | 2021

    Free access