Professor
Richard P. Braun CTS Chair in Transportation Engineering
Contact
136 Civil Engineering Building
500 Pillsbury Drive SE
Minneapolis, MN 55455
Affiliations
Professor
Richard P. Braun CTS Chair in Transportation Engineering
Transit Lab Website | Experts@Minnesota | Google Scholar
My research focuses on modeling the impacts of emerging technologies on public
transit and freight systems and optimizing these systems for more sustainable,
equitable, and reliable service. I leverage advanced network modeling, optimization
techniques, and AI algorithms to modernize transit with next-generation solutions, such
as Autonomous Mobility-on-Demand (AMoD), Mobility-as-a-Service (MaaS), and
Freight-on-Transit (FoT) logistics.
My work aims to provide transportation agencies with a deeper understanding of how
these technologies reshape both system performance and community well-being. A
significant portion of my current research involves developing decision-support tools for
planners and operators, specifically designed to align transportation infrastructure and
policies with inclusive and low-carbon goals.
Transit and Freight Electrification: Optimizing the transition to electric fleets,
addressing charging infrastructure siting, fleet conversion, and operational scheduling
under energy uncertainty.
Transit Last-Mile Technologies: Designing and controlling AMoD and integrating
MaaS to solve the last-mile problem in low-density and rural areas.
Innovative Last-Mile Delivery: Developing solutions that bridge freight logistics and
passenger transport, such as utilizing underutilized transit services and active
transportation for zero-emission last-mile delivery.
AI-Driven Travel Behavior Modeling: Integrating AI and machine learning with
econometric modeling to analyze transit demand, ridership trends, user choices, and
equity.
Network Modeling and Optimization: Developing open-source tools and algorithms to
enhance statewide and regional transportation planning, with a focus on reducing
vehicle-miles-traveled (VMT) and greenhouse gas emissions.
Tork, N., Emami, B. D., Baek, K., Khani, A., Bhandari, S., & Ryan, A. (2026). Charger deployment and fleet sizing for heterogeneous fleets under budget-constrained multi-stage electrification. Transport Policy, 104285.
Emami, B. D., Zhang, Y., Khani, A., & Song, Y. (2025). Towards an efficient electric bus system: Multi-phase optimization model for incremental electrification of bus network with uncertain energy consumption. Transportation Research Part C: Emerging Technologies, 173, 105011.
Davazdah Emami, B., Khani, A., & Zhang, Y. (2025). Fleet Sizing and Scheduling of Battery Electric Buses with Limited Overnight Charging: A Spatial-Temporal-Soc Network
Approach. Available at SSRN 5357459.
Li, J., Khani, A., &Baek, K. (2026). Dynamic Evolution of Mobility-as-a-Service Subscriptions: A Game-Theoretic Day-to-Day Framework for Multimodal Transport. In International Conference on Transportation and Development 2026 (pp. 491-502).
Aalipour, A., & Khani, A. (2024). Modeling, analysis, and control of autonomous mobility-on-
demand systems: a discrete-time linear dynamical system and a model predictive control
approach. IEEE Transactions on Intelligent Transportation Systems, 25(8), 8615-8628.
Aalipour, A., & Khani, A. (2023). Data-driven h-infinity control with a real-time and efficient
reinforcement learning algorithm: An application to autonomous mobility-on-demand
systems. arXiv preprint arXiv:2309.08880.
Tork, N., Baek, K., & Khani, A. (2025). Efficient urban transit systems: bridging freight logistics and passenger transport. Transportation Research Record, 2679(11), 782-804.
Tork, N., & Khani, A. (2025). Incentive-Compatible Mechanism for Integrated Bike Rebalancing and Local Store Deliveries. In 2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC) (pp. 4604-4610). IEEE.
Baek, K., & Khani, A. (2026). Navigating machine learning for travel behavior analysis: A
comprehensive guide to inferring trip purposes using transit survey and automatic fare collection
data fusion. Transportation Research Part C: Emerging Technologies, 190, 105726.
Zeng, X., Song, Y., Khani, A., Reid, J., & Sok, S. (2026). Assessing the Impact of Transit Right-of-Way on Service Reliability via Segment-Level Data Integration and Ensemble
Learning. Transportation Research Record, 03611981251401579.
Baek, K., & Khani, A. (2025). A Generalized Discrete Choice Model with Segmentation and
Embedding via AI-Learning (DCM-SEAL). Available at SSRN 5765940.
DeBruin, H., & Khani, A. (2024). Geographic information system-based comprehensive shared micromobility station siting optimization for small urban areas. Transportation Research
Record, 2678(10), 85-101.
Kumar, P., & Khani, A. (2023). Schedule-based transit assignment with online bus arrival
information. Transportation Research Part C: Emerging Technologies, 155, 104282.
Yu, X., Khani, A., Chen, J., Xu, H., & Mao, H. (2022). Real-time holding control for transfer
synchronization via robust multiagent reinforcement learning. IEEE Transactions on Intelligent
Transportation Systems, 23(12), 23993-24007.
Kumar, P., & Khani, A. (2022). Planning of integrated mobility-on-demand and urban transit
networks. Transportation Research Part A: Policy and Practice, 166, 499-521.