The Intelligent Driver Model (IDM) is one of the widely used car-following models to represent human drivers in mixed traffic simulations. However, the standard IDM performs too well in energy efficiency and comfort (acceleration) compared with real-world human drivers. In addition, many studies assessed the performance of automated vehicles interacting with human-driven vehicles (HVs) in mixed traffic where IDM serves as HVs based on the assumption that the IDM represents an intelligent human driver that performs not better than automated vehicles (AVs). When a commercially available control system of AVs, Adaptive Cruise Control (ACC), is compared with the standard IDM, it is found that the standard IDM generally outperforms ACC in fuel efficiency and comfort, which is not logical in an evaluation of any advanced control logic with mixed traffic. To ensure the IDM reasonably mimics human drivers, a dynamic safe time headway concept is proposed and evaluated. A real-world NGSIM data set is utilized as the human drivers for simulation-based comparisons. The results indicate that the performance of the IDM with dynamic time headway is much closer to human drivers and worse than the ACC system as expected.
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This content will become publicly available on January 1, 2025
A Dynamic Transformation Car-Following Model for the Prediction of the Traffic Flow Oscillation
Car-following (CF) behavior is a fundamental of traffic flow modeling; it can be used for the virtual testing of connected and automated vehicles and the simulation of various types of traffic flow, such as free flow and traffic oscillation. Although existing CF models can replicate the free flow well, they are incapable of simulating complicated traffic oscillation, and it is difficult to strike a balance between accuracy and efficiency. This article investigates the error variation when the traffic oscillation is simulated by the intelligent driver model (IDM). Then, it divides the traffic oscillation into four phases (coasting, deceleration, acceleration, and stationary) by using the space headway of multiple steps. To simulate traffic oscillation between multiple human-driven vehicles, a dynamic transformation CF model is proposed, which includes the long-time prediction submodel [modified sequence-to-sequence (Seq2seq)] model, short-time prediction submodel (Transformer), and their dynamic transformation strategy]. The first submodel is utilized to simulate the coasting and stationary phases, while the second submodel is utilized to simulate the acceleration and deceleration phases. The results of experiments indicated that compared to K -nearest neighbors, IDM, and Seq2seq CF models, the dynamic transformation CF model reduces the trajectory error by 60.79–66.69% in microscopic traffic flow simulations, 7.71–29.91% in mesoscopic traffic flow simulations, and 1.59–18.26% in macroscopic traffic flow simulations. Moreover, the runtime of the dynamic transformation CF model (Inference) decreased by 14.43–66.17% when simulating the large-scale traffic flow.
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- Award ID(s):
- 2152258
- NSF-PAR ID:
- 10510984
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Intelligent Transportation Systems Magazine
- Volume:
- 16
- Issue:
- 1
- ISSN:
- 1939-1390
- Page Range / eLocation ID:
- 174 to 198
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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