Research

We study public perceptions and adoption of autonomous mobility — on the ground (autonomous vehicles or AVs) and in the air (advanced air mobility or AAM) — through combined empirical and experimental studies. Empirical analyses draw on travel-diary, stated-preference, and longitudinal panel data; experimental work leverages high-fidelity driving simulation. Our modeling frameworks — including latent-class and hybrid choice (ICLV) models — integrate behavioral economics, psychology, and statistics to quantify how safety concerns, trust, risk perception, equity, costs, and service attributes shape willingness to adopt AVs and AAM, and how these perceptions interact with current travel patterns and infrastructure availability. This research identifies heterogeneous cohorts, estimates policy elasticities, and produces scenario-based adoption forecasts that inform targeted incentives, infrastructure planning, and risk-communication strategies for agencies and industry.

Read more at:

• Nazari, F., Noruzoliaee, M., Mohammadian, A. (2025) Autonomous vehicle adoption behavior and safety concern: A study of public perception. Multimodal Transportation, 100252. https://doi.org/10.1016/j.multra.2025.100252

• Soto, Y., Nazari, F., Noruzoliaee, M. (2025) Urban mobility in the age of automation: Analyzing public attitudes toward privately-owned versus shared automated vehicles. https://doi.org/10.48550/arXiv.2309.03283

• Nazari, F., Mohammadian, A. (2023) Modeling vehicle-miles of travel accounting for latent heterogeneity. Transport Policy 133, 45-53. https://doi.org/10.1016/j.tranpol.2023.01.005

• Nazari, F., Noruzoliaee, M., Mohammadian, A. (2018) Shared versus private mobility: Modeling public interest in autonomous vehicles accounting for latent attitudes. Transportation Research Part C: Emerging Technologies 97, 456-477. https://doi.org/10.1016/j.trc.2018.11.005

Focusing on implications of electric vehicles (EVs) for energy security, climate, and public health, we design household vehicle surveys and model the dynamics of vehicle ownership — namely, transaction timing, fuel-type choice, use (VMT), and fleet size — mapped to demographics, travel patterns, and attitudes. Using the same integrated modeling, including latent-class and hybrid choice (ICLV) models, we evaluate how charging infrastructure and policy incentives (e.g., tax credits, parking benefits, and access to exclusive lanes for high occupancy vehicles) affect market penetration and distributional equity. Outputs include regional adoption forecasts, concise policy briefs, and interactive scenario dashboards with sensitivity analyses, providing clear guidance on cost-effective, equitable pathways for accelerating EV adoption.

Read more at:

• Nazari, F., Mohammadian, A. (2025) Exploring the role of perceived range anxiety in adoption behavior of plug-in electric vehicles. https://doi.org/10.48550/arXiv.2308.10313

• Nazari, F., Noruzoliaee, M., Mohammadian, A. (2024) Electric vehicle adoption behavior and vehicle transaction decision: Estimating an integrated choice model with latent variables on a retrospective vehicle survey. Transportation Research Record: Journal of the Transportation Research Board, 1-20. https://doi.org/10.1177/03611981231184875

• Nazari, F., Rahimi, E., Mohammadian, A. (2019) Simultaneous estimation of battery electric vehicle adoption with endogenous willingness to pay. eTransportation, 100008. https://doi.org/10.1016/j.etran.2019.100008

• Nazari, F., Mohammadian, A., Stephens, T. (2019) Modeling electric vehicle adoption considering a latent travel pattern construct and charging infrastructure. Transportation Research Part D: Transport and Environment 72C, 65-82. https://doi.org/10.1016/j.trd.2019.04.010

• Nazari, F., Mohammadian, A., Stephens, T. (2018) Dynamic household vehicle decision modeling with consideration of electric vehicles. Transportation Research Record: Journal of the Transportation Research Board, 1-10. https://doi.org/10.1177%2F0361198118796925

We develop robust, explainable, and uncertainty-aware AI for safety-critical transportation on the ground and in the air. In our work on trustworthy AI for transportation cyber-physical systems, we are building verifiable sensing-prediction-decision pipelines for infrastructure operations. In aviation, our research integrates airspace representations with probabilistic risk modeling to deliver short-horizon, real-time safety predictions for mixed traffic and air mobility. On the ground, our modeling frameworks moves beyond associative forecasting to enable counterfactual policy evaluation at scale. Complementing these efforts, we further aim at quantifying unobserved segments in travel pattern to provide empirical priors and validation targets for trustworthy, policy-relevant AI. Our research in this line advances reliability, transparency, and human-in-the-loop validation, with direct pathways to safer operations, targeted incentives, and equitable deployment for all.

Read more at:

• Nazari, F., Noruzoliaee, M. (2025) Planning and policy for safer roads with autonomous vehicles: Moral decision making behavior in dilemma-inducing situations, U.S. Department of Transportation, Traffic21 Institute, Safety21 University Transportation Center (UTC). https://rosap.ntl.bts.gov/view/dot/86173

• Nazari, F., Mohammadian, A. (2023) Modeling vehicle-miles of travel accounting for latent heterogeneity. Transport Policy 133, 45-53. https://doi.org/10.1016/j.tranpol.2023.01.005