Macro Prediction Model of Road Traffic Accident Based on NARX Neural Network
ID:1819 View Protection:ATTENDEE Updated Time:2021-12-09 10:29:13 Hits:256 Poster Presentation

Start Time:2021-12-17 08:10(Asia/Shanghai)

Duration:1min

Session:P2 Poster2021 » P2T4Track 4 Transportation Behavior, Safety and Security

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Abstract
In order to predict the development trend of traffic accidents and improve the prediction accuracy of macro indicators of traffic accidents, three direct indicators of road traffic accidents were taken as output variables and six macro indicators were taken as input variables to establish the NARX model. The model was trained and fitted based on China's data from 2001 to 2016, the modeling results were used to predict three direct indicators of traffic accidents in 2017 and 2018. To validate the performance of the NARX model, MLR model, BPNN model and GRNN model were also used as comparative benchmarks. The models were compared by selecting mean square error(MSE), Theil IC(TIC), mean absolute error(MAE) and mean absolute percentage error(MAPE) as the error analysis indexes. The results show that the accuracy of NARX model is better than the other three contrast models in the aspect of traffic accident macro index prediction.
Keywords
CICTP
Speaker
Lu Cai
Chang'an University

Submission Author
Lu Cai Chang'an University
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  • Conference Date

    Dec 17

    2021

    to

    Dec 20

    2021

  • Dec 16 2021

    Contribution Submission Deadline

  • Dec 24 2021

    Registration deadline

Sponsored By
Chinese Overseas Transportation Association
Chang'an University
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