Abstract
In this work, we use reinforcement learning (RL) to train a car following model for vehicle jerk. The learned model is specifically trained for car following in low-speed oscillatory driving conditions such as stop-and-go traffic typical in congested urban centers. This driving is of particular interest since it is difficult to model and substantially contributes to urban air pollution. The proposed model is calibrated using experimental data and the model performance is compared to a baseline calibrated intelligent driver model (IDM). The proposed RL model is able to outperform the IDM in some metrics, while the IDM has lower error in others. This indicates that the proposed RL model is able to capture the general car following behavior in low-speed oscillatory driving conditions without overfitting to the training data and represents a first step toward realistic car following models that capture the full range of driver behavior.
Original language | English (US) |
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Title of host publication | 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2021 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728189956 |
DOIs | |
State | Published - Jun 16 2021 |
Event | 7th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2021 - Heraklion, Greece Duration: Jun 16 2021 → Jun 17 2021 |
Publication series
Name | 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2021 |
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Conference
Conference | 7th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2021 |
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Country/Territory | Greece |
City | Heraklion |
Period | 6/16/21 → 6/17/21 |
Bibliographical note
Funding Information:This material is based upon work supported by the Center for Transportation Studies at the University of Minnesota under the Transportation Scholars Program.
Funding Information:
This work is supported by the University of Minnesota Center for Transportation Studies through the Transportation Scholar’s Program.
Publisher Copyright:
© 2021 IEEE.
Keywords
- Car following
- Reinforcement learning
- Traffic modeling