Modeling car-following behavior in heterogeneous traffic mixing human-driven, automated and connected vehicles: considering multitype vehicle interactions
DOI: 10.21203/rs.3.rs-2096084/v1
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Summary
This study addresses the challenge of modeling car-following (CF) behavior in heterogeneous traffic environments comprising human-driven vehicles (HDVs), autonomous vehicles (AVs), and connected and automated vehicles (CAVs). The research is motivated by the transition toward mixed traffic scenarios where vehicle interactions are complex and dependent on the specific types of surrounding vehicles. Previous models often failed to adequately account for the influence of multitype vehicle interactions, the degradation of CAV capabilities when following non-connected vehicles, and the impact of surrounding vehicles beyond the immediate leader. To resolve these gaps, the authors propose a unified CF model that integrates the Intelligent Driver Model (IDM) with molecular dynamics theory to quantify the attraction and repulsion forces between vehicles based on their velocities and headways. The methodology involves developing distinct sub-models for HDVs, AVs, and CAVs that reflect their specific sensing and communication capabilities. The HDV model considers only the nearest front vehicle, incorporating driver sensitivity to deceleration. The AV model utilizes sensor data from the nearest front and rear vehicles. The CAV model is the most complex, distinguishing between four scenarios based on the types of nearest front and rear vehicles (HDV, AV, or CAV). This approach accounts for "degradation," where a CAV’s behavior shifts toward AV-like characteristics when surrounded by non-connected vehicles, while still leveraging V2V communication to access data from other CAVs in the platoon. The influence of surrounding vehicles is calculated using molecular dynamics principles, treating vehicles as molecules with interaction energies derived from velocity and distance. Model parameters were calibrated using real-world road test data involving mixed fleets of HDVs, AVs, and CAVs, capturing acceleration, deceleration, and constant-speed driving behaviors. The results demonstrate that the proposed model provides more accurate simulations of CF behavior for all three vehicle types compared to traditional IDM, Adaptive Cruise Control (ACC), and Cooperative Adaptive Cruise Control (CACC) models. The validation, supported by numerical simulations and stability analysis, confirms that the model effectively captures the nuances of mixed traffic interactions. Specifically, the inclusion of molecular dynamics theory allows for a more precise representation of how surrounding vehicles affect the host vehicle’s acceleration and deceleration strategies. The study finds that considering the specific types of nearest vehicles and the potential degradation of CAV connectivity significantly improves the realism of the simulation. The significance of this work lies in its contribution to the understanding of mixed traffic stability and safety. By providing a robust framework that accounts for multitype vehicle interactions and connectivity variations, the model offers valuable guidance for designing control strategies for AVs and CAVs. It highlights the importance of considering the heterogeneous nature of traffic flows to improve the stability of car-following behavior and enhance overall traffic efficiency. The findings suggest that effective integration of AVs and CAVs into existing traffic systems requires models that dynamically adjust to the capabilities and types of surrounding vehicles.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | pdftotext | — | — | 4 | 2026-08-10 |
| clean | success | clean | — | — | 2 | 2026-08-10 |
| chunk | success | chunk | — | — | 2 | 2026-08-10 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 2 | 2026-08-10 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 17 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
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- Theoretical Contribution: computational model