Prediction of the Duration of Freeway Traffic Incidents Based on an ATT-LSTM Model

JIA Xingli; LI Shuangqing; YANG Hongzhi; CHEN Xingpeng

doi:10.3963/j.jssn.1674-4861.2022.05.007

Volume 40 Issue 5

Nov. 2022

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Article Navigation > Journal of Transport Information and Safety > 2022 > 40(5): 61-69

JIA Xingli, LI Shuangqing, YANG Hongzhi, CHEN Xingpeng. Prediction of the Duration of Freeway Traffic Incidents Based on an ATT-LSTM Model[J]. Journal of Transport Information and Safety, 2022, 40(5): 61-69. doi: 10.3963/j.jssn.1674-4861.2022.05.007

Citation:

JIA Xingli, LI Shuangqing, YANG Hongzhi, CHEN Xingpeng. Prediction of the Duration of Freeway Traffic Incidents Based on an ATT-LSTM Model[J]. Journal of Transport Information and Safety, 2022, 40(5): 61-69. doi: 10.3963/j.jssn.1674-4861.2022.05.007

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Prediction of the Duration of Freeway Traffic Incidents Based on an ATT-LSTM Model

doi: 10.3963/j.jssn.1674-4861.2022.05.007

1.
School of Highway, Chang'an University, Xi'an 710064, China
2.
Xianyang Planning and Design Institute, Xianyang 712000, Shaanxi, China

Received Date: 2022-01-14
Available Online: 2022-12-05

Abstract

Abstract

In order to study the impacts of traffic incidents on freeway operation, a method for predicting the duration of freeway traffic incidents is studied. Time-dependent characteristics of traffic incidents on freeways are extracted from time series data based on the recurrent neural network (RNN) theory. The feature and the temporal attention layer of a long short-term memory (LSTM) network are combined to study the correlation between historical and current moment data. Based on attention (ATT) mechanism and the LSTM, a model for predicting the duration of traffic incidentson freeways is developed. Validation of the model is carried out based on traffic monitoring dataset collected in 2018 along the Xi'an Ring Freeway. The prediction accuracy of the proposed model is compared with the following models: back propagation neural network (BP), random forest (RF), support vector machine (SVM), and long short-term memory (LSTM). The impacts of different factors, including the types of events, weather conditions, types of vehicles, and traffic volume, on the duration is also analyzed. Study results indicate that under the condition of the same dataset, the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE) of the ATT-LSTM model is 24.43, 25.24%, and 21.17, respectively, which is better than that of other models. The "type of events" has the maximum weight of 0.375 among all of factors considered within the model, followed by the "number of lanes" "vehicle type" and "weather". By using the hourly traffic volume at the entrances and exits of interchanges as the correction parameter, the prediction accuracy is improved, and the MAE, MAPE, and RMSE of the model is decreased by 21.3%, 7.5%, and 16.9%, respectively. This study improves the prediction accuracy of the duration of traffic incidents on freeways and provides technical support for their safe and efficient operation.
- traffic safety,
- freeway,
- traffic incidents,
- duration,
- attention mechanism,
- long short-term memory network

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References(25)

References

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