Macro Prediction Model of Road Traffic Accident Based on NARX Neural Network
编号:1819 访问权限:仅限参会人 更新:2021-12-09 10:29:13 浏览:133次 张贴报告

报告开始:2021年12月17日 08:10(Asia/Shanghai)

报告时间:1min

所在会场:[P2] Poster2021 [P2T4] Track 4 Transportation Behavior, Safety and Security

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摘要
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.
关键词
CICTP
报告人
Lu Cai
Chang'an University

稿件作者
Lu Cai Chang'an University
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重要日期
  • 会议日期

    12月17日

    2021

    12月20日

    2021

  • 12月16日 2021

    报告提交截止日期

  • 12月24日 2021

    注册截止日期

主办单位
Chinese Overseas Transportation Association
Chang'an University
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