Twenty-Five Years of the Intelligent Driver Model: Foundations, Extensions, Applications, and Future Directions
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917099143692288 |
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| author | Zhou, Shirui Zheng, Shiteng Tian, Junfang Jiang, Rui Zhang, and H. M. |
| author_facet | Zhou, Shirui Zheng, Shiteng Tian, Junfang Jiang, Rui Zhang, and H. M. |
| contents | The Intelligent Driver Model (IDM), proposed in 2000, has become a foundational tool in traffic flow modeling, renowned for its simplicity, computational efficiency, and ability to capture diverse traffic dynamics. Over the past 25 years, IDM has significantly advanced car-following theory and found extensive application in intelligent transportation systems, including driver assistance systems and autonomous vehicle control. However, IDM's deterministic framework and simplified assumptions face limitations in addressing real-world complexities such as stochastic variability, driver heterogeneity, and mixed traffic conditions. This paper provides a systematic review and critical reflection on IDM's theoretical foundations, academic influence, practical applications, and model extensions. While highlighting IDM's contributions, we emphasize the need to extend the model into a modular and extensible framework. Future directions include integrating stochastic elements, human behavioral insights, and hybrid modeling approaches that combine physics-based structures with data-driven methodologies. By reimagining IDM as a flexible modeling basis, this paper aims to inspire its continued development to meet the demands of intelligent, connected, and increasingly complex traffic systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_05909 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Twenty-Five Years of the Intelligent Driver Model: Foundations, Extensions, Applications, and Future Directions Zhou, Shirui Zheng, Shiteng Tian, Junfang Jiang, Rui Zhang, and H. M. Physics and Society The Intelligent Driver Model (IDM), proposed in 2000, has become a foundational tool in traffic flow modeling, renowned for its simplicity, computational efficiency, and ability to capture diverse traffic dynamics. Over the past 25 years, IDM has significantly advanced car-following theory and found extensive application in intelligent transportation systems, including driver assistance systems and autonomous vehicle control. However, IDM's deterministic framework and simplified assumptions face limitations in addressing real-world complexities such as stochastic variability, driver heterogeneity, and mixed traffic conditions. This paper provides a systematic review and critical reflection on IDM's theoretical foundations, academic influence, practical applications, and model extensions. While highlighting IDM's contributions, we emphasize the need to extend the model into a modular and extensible framework. Future directions include integrating stochastic elements, human behavioral insights, and hybrid modeling approaches that combine physics-based structures with data-driven methodologies. By reimagining IDM as a flexible modeling basis, this paper aims to inspire its continued development to meet the demands of intelligent, connected, and increasingly complex traffic systems. |
| title | Twenty-Five Years of the Intelligent Driver Model: Foundations, Extensions, Applications, and Future Directions |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2506.05909 |