What Do Autonomous Vehicles Mean to Traffic Congestion and Crash? Network Traffic Flow Modeling and Simulation for Autonomous Vehicles

Qian, Sean; Yang, Shuguan · 2016 · ROSA P / University Transportation Centers Program (U.S.)

archive: archived pipeline: cataloged verified

Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)

Summary

This research addresses the impact of autonomous vehicles (AVs) on traffic congestion and crash patterns within roadway networks. As AV technology approaches commercial implementation, understanding how vehicle automation and communication alter traffic flow is critical for infrastructure design. The study specifically investigates how different sensing and control specifications affect vehicle speed, headway, and roadway capacity under varying AV penetration rates. To answer these questions, the authors developed a multi-class traffic flow modeling framework that captures the interactions between regular human-driven vehicles and autonomous connected vehicles. The methodology employs a generic, data-driven multi-class model that approximates mixed traffic flow by considering interactions between distinct classes of traffic streams, rather than using an aggregated flow-density relation like the traditional LWR model. Each class possesses homogeneous car-following behavior and vehicle attributes, encapsulated by a unique fundamental diagram. The model introduces the concept of "perceived equivalent density," where each class perceives the effect of other classes differently, accounting for both lateral and longitudinal cross-class interactions. This approach allows for realistic, class-specific travel time computation and efficient modeling of heterogeneous travelers in large-scale networks. The model was validated through numerical experiments and the NGSIM I-80 dataset, testing scenarios involving flow perturbations and incident-induced bottlenecks. The findings demonstrate that the multi-class model is consistent with single-class Cell Transmission Model (CTM) results while successfully capturing shockwaves for multiple vehicle classes. The model produced realistic congestion propagation and time-varying travel times for each class, capabilities not available in conventional single-class models. The data-driven framework allows for the calibration of road space allocations and fundamental diagrams using refined traffic data, such as vehicle trajectories, to produce spatiotemporal flow estimations that closely match real-world observations. The significance of this work lies in providing a robust modeling framework to assess the mobility and safety impacts of AVs on transportation networks. By enabling the estimation of effective road capacity and operating efficiency under different AV penetration rates, the research offers insights for designing vehicle control strategies and informing transportation planning policies. Future work aims to generalize the classification to include factors like vehicle size and driver behavior, extend the model to merge and diverge junctions, and apply it to general network models for route and mode choice analysis.

Key finding

The multi-class flow model successfully captures shockwave formation and produces realistic, time-varying travel times for each vehicle class, which single-class models cannot achieve.

Methodology

modeling

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• traffic density
• situational awareness
• mental model of traffic

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Theoretical Contribution: computational model