Federal Highway Administration (FHWA) Connected and Automated Vehicles (CAV) Analysis, Modeling, and Simulation (AMS) Program: Connected, Automated, and Electric: Modeling Traffic and Traveler Choice Considering the Three Mega-Trends
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Summary
This report, produced by Noblis for the Federal Highway Administration (FHWA), addresses the challenge of accurately modeling travel behavior in an era defined by three converging "mega-trends": automated vehicles (AVs), traveler connectivity, and electric vehicles (EVs). The rapid deployment of these technologies is disrupting traditional transportation systems, creating complex decision-making processes and new mobility service models that current Analysis, Modeling, and Simulation (AMS) tools struggle to capture. The study aims to identify how these technologies individually and collectively impact travel choices—such as trip generation, route selection, and destination choice—and to determine the necessary updates to AMS frameworks to reflect these shifts. The authors conducted a comprehensive literature review guided by three key research questions: the individual impacts of automation, connectivity, and electrification on travel behavior; the synergies and conflicts arising from their combination; and the gaps in current AMS tools. The analysis distinguishes between micro-level modeling (individual decisions) and macro-level modeling (aggregate regional outcomes), focusing on how the convergence of these technologies alters the standard sequence of travel choices, including mode, route, departure time, and parking decisions. The findings reveal distinct behavioral shifts for each technology. Traveler connectivity, driven by smartphones and real-time navigation apps, has fostered selfish routing behaviors and the growth of shared mobility services like ride-hailing, which introduce fleet-based decision-making and deadheading. AVs are expected to induce travel by lowering the value of travel time, allowing for multi-tasking, and enabling zero-occupancy trips where empty vehicles seek cheaper parking or charging, thereby increasing vehicle miles traveled (VMT). EVs introduce complex decision-making constraints related to state of charge, battery range, and charging infrastructure availability, leading to behaviors such as eco-routing and risk-averse choices. When combined, these technologies present both complementary and conflicting dynamics. For instance, connectivity and AVs can improve network efficiency through system-optimal routing but may degrade performance if programmed for individual utility maximization. Similarly, AVs can expand EV charging access by allowing autonomous charging trips, yet limited EV range may conflict with the convenience of AV-enabled regional travel. The report identifies four critical gaps in current AMS tools: the inability to model shared service fleet behaviors, the lack of representation for zero-occupancy trips and their associated infrastructure interactions, insufficient modeling of network utilization impacts from real-time app recommendations, and inadequate handling of EV charging and routing complexities. The authors conclude that future research must integrate these behavioral nuances into AMS tools, specifically focusing on risk attitudes, charger supply constraints, human responses to real-time recommendations, and the operational differences between human-driven and autonomous fleets. These updates are essential for accurately forecasting network impacts and designing strategic interventions in the mega-trend era.
Key finding
The convergence of automation, electrification, and connectivity creates complex, often conflicting travel behaviors that current analysis, modeling, and simulation tools are ill-equipped to capture accurately.
Methodology
review
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
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| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Theoretical Contribution: computational model