A recurrent neural network model for predicting two-leader car-following behavior
Unlike lane-based traffic where each driver has a distinct leader, the subject driver in disorderly traffic may interact with multiple vehicles in-front. The existence of lateral interactions among the vehicles in-front adds even more complexity to modeling the human-driving process. Utilizing traje...
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| Published in | Transportation letters Vol. ahead-of-print; no. ahead-of-print; pp. 1 - 15 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Taylor & Francis
27.05.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1942-7867 1942-7875 |
| DOI | 10.1080/19427867.2023.2205190 |
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| Abstract | Unlike lane-based traffic where each driver has a distinct leader, the subject driver in disorderly traffic may interact with multiple vehicles in-front. The existence of lateral interactions among the vehicles in-front adds even more complexity to modeling the human-driving process. Utilizing trajectory data extracted from an instrumented vehicle study, this research attempts to propose a gated recurrent unit neural network model to predict responses of vehicles interacting with two leading vehicles simultaneously. The recurrent neural network model can illustrate realistic human-like following behavior of drivers, much better than the classical optimal velocity-based models in terms of trajectory reproducing accuracy. The model can also explain the closing-in, shying-away behavior and local stability properties. Results of the study provide insights into the driving behavioral phenomena of disorderly traffic flows and can contribute to the development of a realistic microsimulation model, smarter autonomous systems, and in-traffic safety evaluation as well. |
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| AbstractList | Unlike lane-based traffic where each driver has a distinct leader, the subject driver in disorderly traffic may interact with multiple vehicles in-front. The existence of lateral interactions among the vehicles in-front adds even more complexity to modeling the human-driving process. Utilizing trajectory data extracted from an instrumented vehicle study, this research attempts to propose a gated recurrent unit neural network model to predict responses of vehicles interacting with two leading vehicles simultaneously. The recurrent neural network model can illustrate realistic human-like following behavior of drivers, much better than the classical optimal velocity-based models in terms of trajectory reproducing accuracy. The model can also explain the closing-in, shying-away behavior and local stability properties. Results of the study provide insights into the driving behavioral phenomena of disorderly traffic flows and can contribute to the development of a realistic microsimulation model, smarter autonomous systems, and in-traffic safety evaluation as well. |
| Author | Das, Sanhita Maurya, Akhilesh Kumar Dey, Arka |
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| SubjectTerms | Car-following disordered traffic mixed traffic neural network two-leader |
| Title | A recurrent neural network model for predicting two-leader car-following behavior |
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