基于ATT-LSTM模型的高速公路交通事件持续时长预测

贾兴利; 李双庆; 杨宏志; 陈星澎

doi:10.3963/j.jssn.1674-4861.2022.05.007

基于ATT-LSTM模型的高速公路交通事件持续时长预测

doi: 10.3963/j.jssn.1674-4861.2022.05.007

1.
长安大学公路学院西安 710064
2.
咸阳市规划设计研究院陕西咸阳 712000

基金项目:

国家重点研发计划项目 2020YFC1512003

陕西省自然科学基础研究计划项目 2020JM-260

详细信息

通讯作者:
贾兴利(1986—)，博士，副教授. 研究方向：道路交通安全与应急. E-mail：jiaxingli@chd.edu.cn

中图分类号: U491.1
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出版历程
- 收稿日期: 2022-01-14
- 网络出版日期: 2022-12-05

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

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

摘要

摘要: 为揭示交通事件对高速公路运行状态持续时间的影响规律，研究了高速公路交通事件持续时长预测方法。考虑高速公路交通事件时间序列特性，基于循环神经网络理论，从时间序列数据中提取交通事件时间依赖关系；通过引入长短时记忆网络，结合特征、时序注意力层挖掘历史时刻信息和当前时刻数据间的相关性，构建基于注意力机制-长短时记忆网络的高速公路交通事件持续时长预测模型。以2018年西安绕城高速公路交通监测数据集为例，开展了高速公路交通事件持续时长预测模型验证，对比了所提模型与反向传播神经网络、随机森林、支持向量机、长短时记忆网络模型这4种典型算法的预测精度，并分析了事件类型、天气条件、车辆类型、交通量等不同影响因素对持续时长的影响程度。结果表明：使用同一数据集，注意力机制-长短时记忆网络预测模型的预测结果平均绝对误差为24.43，平均绝对百分比误差为25.24%，均方根误差为21.17，预测精度优于其他4种预测方法。在模型的各影响因素权重中，事件类型所占权重最大为0.375，其次分别为车道数、车辆类型、天气等；采用立交出入口小时交通量作为修正参数，可以进一步提升预测精度，预测结果的绝对误差、平均绝对百分比误差和均方根误差可分别降低21.3%、7.5%和16.9%。研究结果能进一步提高高速公路交通事件持续时长预测的精度，为公路安全高效运行提供技术支持。
- 交通安全 /
- 高速公路 /
- 交通事件 /
- 持续时长 /
- 注意力机制 /
- 长短时记忆网络
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

HTML全文

图 1 LSTM模型单元结构

Figure 1. Unit structure of LSTM model

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图 2 ATT-LSTM模型权重计算图

Figure 2. Weight calculation diagram of ATT-LSTM model

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图 3 不同超参数的测试集MAE值

Figure 3. MAE values of test sets with different super parameters

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图 4 交通事件持续时长的实际值与预测值

Figure 4. Theactual and forecastduration of traffic incidents

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图 5 各影响因素所占权重

Figure 5. Weight of each influencing factor

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图 6 立交出入口分布图

Figure 6. Layout of interchange entrance and exit

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图 7 预测模型测试对比

Figure 7. Comparison of prediction model tests

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表 1 交通事件主要特征及分类

Table 1. Main characteristics and classification of traffic events

特征	数据类型	种类
事件发生时间	连续变量
事件发生地点	连续变量
天气状况	分类变量	4
路面状况	分类变量	3
上下行	分类变量	2
涉及车辆类型	分类变量	3
涉及车辆数量	连续变量
影响范围	分类变量	2
影响车道数量	连续变量
是否位于主线	分类变量	2
事件类型	分类变量	5

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表 2 原始数据

Table 2. Raw data

月份	日期	时间	桩号	桩号类型	上下行	天气	路面环境	事件描述	影响范围	影响车道	处理结束时间
1月	1日	08：10	57	A	下行	晴	A	1辆6轴半挂货车撞护栏	A	占用1条行车道	10:00

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表 3 格式转换后数据

Table 3. Data after format conversion

发生时间	发生地点	涉及车辆			上下行		天气				事件类型					涉及车辆数	影响范围		影响车道	持续时长/min
发生时间	发生地点	小客车	大客车	货车	上行	下行	雨	雪	晴	阴	追尾	侧翻	碰撞	剐蹭	故障	涉及车辆数	主线	非主线	影响车道	持续时长/min
1514764800	-0.233 -0.972	0	0	1	0	1	0	0	1	0	0	0	1	0	0	1	1	0	1	110

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表 4 不同模型预测精度评价指标值

Table 4. Prediction accuracy evaluation index value of different models

模型	MAE	MAPE/%	RMSE
ATT-LSTM	24.43	25.24	21.17
GA-BP	26.09	37.98	43.41
RF	36.34	29.60	34.72
SVM	30.29	35.47	36.37
LSTM	27.03	30.15	29.86

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表 5 预测模型误差指标对比

Table 5. Comparison of prediction model error indicators

模型	MAE	MAPE/%	RMSE
未优化模型	24.43	25.24	21.17
优化后模型	19.27	23.35	17.59

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