Travel Time Estimation by Learning Driving Habits and Traffic Conditions
Travel time estimation (TTE) is widely applied for ride dispatching, ride-hailing, and route navigation. There are many factors affecting the travel time of a driver on a given trajectory, including the distance, road type, driving habits, traffic congestion, etc. Existing works fail to model the co...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/1308488 |
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| author | Ling Yang Shouxu Jiang Fusheng Zhang Ming Zhao |
| author_facet | Ling Yang Shouxu Jiang Fusheng Zhang Ming Zhao |
| author_sort | Ling Yang |
| collection | DOAJ |
| description | Travel time estimation (TTE) is widely applied for ride dispatching, ride-hailing, and route navigation. There are many factors affecting the travel time of a driver on a given trajectory, including the distance, road type, driving habits, traffic congestion, etc. Existing works fail to model the complex relationships of these factors for TTE. To fill this gap, in this paper, we first analyze how these factors work together in determining the travel time. In particular, the travel time depends on the distance and driving speed on each road segment of the trajectory, where the driving speed depends on the driving habits and the environment, including the static factors like the road type (highway or byway) and speed limit and the dynamic factor like the time of the day and congestion. Among these factors, driving habits and traffic conditions (e.g., jam) are the most difficult ones to model. Second, we propose to learn the driving habits of each driver via meta-learning and estimate the conditions based on the current and historical traffic conditions (via recurrent neural networks) of this road and its connected road segments (via graph convolutional neural network). The experimental results on two real taxi trajectory datasets show that our approach outperforms three state-of-the-art methods significantly. |
| format | Article |
| id | doaj-art-45376ccdbbcc4e408a4bce7b3eab044c |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-45376ccdbbcc4e408a4bce7b3eab044c2025-02-03T01:20:08ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/1308488Travel Time Estimation by Learning Driving Habits and Traffic ConditionsLing Yang0Shouxu Jiang1Fusheng Zhang2Ming Zhao3Faculty of ComputingFaculty of ComputingKey Laboratory of Elevator Intelligent Safety in Jiangsu ProvinceSchool of InformaticsTravel time estimation (TTE) is widely applied for ride dispatching, ride-hailing, and route navigation. There are many factors affecting the travel time of a driver on a given trajectory, including the distance, road type, driving habits, traffic congestion, etc. Existing works fail to model the complex relationships of these factors for TTE. To fill this gap, in this paper, we first analyze how these factors work together in determining the travel time. In particular, the travel time depends on the distance and driving speed on each road segment of the trajectory, where the driving speed depends on the driving habits and the environment, including the static factors like the road type (highway or byway) and speed limit and the dynamic factor like the time of the day and congestion. Among these factors, driving habits and traffic conditions (e.g., jam) are the most difficult ones to model. Second, we propose to learn the driving habits of each driver via meta-learning and estimate the conditions based on the current and historical traffic conditions (via recurrent neural networks) of this road and its connected road segments (via graph convolutional neural network). The experimental results on two real taxi trajectory datasets show that our approach outperforms three state-of-the-art methods significantly.http://dx.doi.org/10.1155/2022/1308488 |
| spellingShingle | Ling Yang Shouxu Jiang Fusheng Zhang Ming Zhao Travel Time Estimation by Learning Driving Habits and Traffic Conditions Journal of Advanced Transportation |
| title | Travel Time Estimation by Learning Driving Habits and Traffic Conditions |
| title_full | Travel Time Estimation by Learning Driving Habits and Traffic Conditions |
| title_fullStr | Travel Time Estimation by Learning Driving Habits and Traffic Conditions |
| title_full_unstemmed | Travel Time Estimation by Learning Driving Habits and Traffic Conditions |
| title_short | Travel Time Estimation by Learning Driving Habits and Traffic Conditions |
| title_sort | travel time estimation by learning driving habits and traffic conditions |
| url | http://dx.doi.org/10.1155/2022/1308488 |
| work_keys_str_mv | AT lingyang traveltimeestimationbylearningdrivinghabitsandtrafficconditions AT shouxujiang traveltimeestimationbylearningdrivinghabitsandtrafficconditions AT fushengzhang traveltimeestimationbylearningdrivinghabitsandtrafficconditions AT mingzhao traveltimeestimationbylearningdrivinghabitsandtrafficconditions |