Travel Time Probability Prediction Based on Constrained LSTM Quantile Regression
Travel time reliability assessment has been widely used in recent years to evaluate the performance of transportation networks and measure the operation level of transportation systems. Weather, as one of the important factors influencing travel time reliability, affects the relationship between the...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/9910142 |
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| _version_ | 1849471172044914688 |
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| author | Hao Li Zijian Wang Xiantong Li Hua Wang Yongxing Man Jinyu Shi |
| author_facet | Hao Li Zijian Wang Xiantong Li Hua Wang Yongxing Man Jinyu Shi |
| author_sort | Hao Li |
| collection | DOAJ |
| description | Travel time reliability assessment has been widely used in recent years to evaluate the performance of transportation networks and measure the operation level of transportation systems. Weather, as one of the important factors influencing travel time reliability, affects the relationship between the supply and requirement of urban road networks. Considering the traffic characteristics under different traffic conditions, a study on the influence of weather on travel time reliability under different conditions is proposed to predict the probability of travelers completing their trips within the expected time under different weather conditions. Based on the urban road network data and cab trajectory data of Harbin city, this paper correlates the floating vehicle location with the road network information through a hidden Markov model to reduce the influence of vehicle trajectory errors on the calculation results of path travel time. To analyze the entire distribution of extreme travel time and its impact on the reliability of travel time under various traffic situations, it captures the tail features of the travel time distribution based on extreme value theory. Then, to increase the predictability of each quantile, it combines a deep-learning LSTM model and a quantile regression model to create a probabilistic travel time prediction model utilizing combined layers. The proposed model is compared with the linear quantile regression and neural network quantile regression models, and the model is evaluated in terms of point prediction results and probabilistic prediction results, respectively, to ensure the accuracy of predictions from the model. As a result, the prediction accuracy of the model in this paper is greatly improved, and the degree of violating quantile constraints is greatly reduced. |
| format | Article |
| id | doaj-art-cfa0bdf0d95f42e38f38722fd57316b1 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-cfa0bdf0d95f42e38f38722fd57316b12025-08-20T03:24:55ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/9910142Travel Time Probability Prediction Based on Constrained LSTM Quantile RegressionHao Li0Zijian Wang1Xiantong Li2Hua Wang3Yongxing Man4Jinyu Shi5Broadvision Engineering ConsultantsShandong Key Laboratory of Smart Transportation (Preparation)School of Transportation Science and EngineeringSchool of Transportation Science and EngineeringSchool of Transportation Science and EngineeringSchool of Transportation Science and EngineeringTravel time reliability assessment has been widely used in recent years to evaluate the performance of transportation networks and measure the operation level of transportation systems. Weather, as one of the important factors influencing travel time reliability, affects the relationship between the supply and requirement of urban road networks. Considering the traffic characteristics under different traffic conditions, a study on the influence of weather on travel time reliability under different conditions is proposed to predict the probability of travelers completing their trips within the expected time under different weather conditions. Based on the urban road network data and cab trajectory data of Harbin city, this paper correlates the floating vehicle location with the road network information through a hidden Markov model to reduce the influence of vehicle trajectory errors on the calculation results of path travel time. To analyze the entire distribution of extreme travel time and its impact on the reliability of travel time under various traffic situations, it captures the tail features of the travel time distribution based on extreme value theory. Then, to increase the predictability of each quantile, it combines a deep-learning LSTM model and a quantile regression model to create a probabilistic travel time prediction model utilizing combined layers. The proposed model is compared with the linear quantile regression and neural network quantile regression models, and the model is evaluated in terms of point prediction results and probabilistic prediction results, respectively, to ensure the accuracy of predictions from the model. As a result, the prediction accuracy of the model in this paper is greatly improved, and the degree of violating quantile constraints is greatly reduced.http://dx.doi.org/10.1155/2023/9910142 |
| spellingShingle | Hao Li Zijian Wang Xiantong Li Hua Wang Yongxing Man Jinyu Shi Travel Time Probability Prediction Based on Constrained LSTM Quantile Regression Journal of Advanced Transportation |
| title | Travel Time Probability Prediction Based on Constrained LSTM Quantile Regression |
| title_full | Travel Time Probability Prediction Based on Constrained LSTM Quantile Regression |
| title_fullStr | Travel Time Probability Prediction Based on Constrained LSTM Quantile Regression |
| title_full_unstemmed | Travel Time Probability Prediction Based on Constrained LSTM Quantile Regression |
| title_short | Travel Time Probability Prediction Based on Constrained LSTM Quantile Regression |
| title_sort | travel time probability prediction based on constrained lstm quantile regression |
| url | http://dx.doi.org/10.1155/2023/9910142 |
| work_keys_str_mv | AT haoli traveltimeprobabilitypredictionbasedonconstrainedlstmquantileregression AT zijianwang traveltimeprobabilitypredictionbasedonconstrainedlstmquantileregression AT xiantongli traveltimeprobabilitypredictionbasedonconstrainedlstmquantileregression AT huawang traveltimeprobabilitypredictionbasedonconstrainedlstmquantileregression AT yongxingman traveltimeprobabilitypredictionbasedonconstrainedlstmquantileregression AT jinyushi traveltimeprobabilitypredictionbasedonconstrainedlstmquantileregression |