Research

Presentation

• GUT Forum 70th Meeting (October 12, 2025, online)

Funded Project

• Waymo | 2024–2025

Shared automated vehicle (SAV) ridehailing services are now operating in several metropolitan regions in the United States. While providing benefits, SAV services may exacerbate issues related to curb usage and vehicle kilometers traveled (VKT) in urban areas. The objective of this study is to provide guidance to cities by evaluating the impacts of SAVs' short-term curb usage for staging between serving ride requests, under different curb restrictions and SAV operational strategies. We focus on the following performance metrics: VKT, curb productivity, customer wait time, and customer matching rate. To perform the analysis, we use a high-fidelity commercial simulation tool that models the dynamics of SAV fleet operations. We use high-quality, high-resolution data, including synthetic ridehailing trip data and forecasts of curb availability at each curb front in San Francisco, California. Our baseline scenario assumes a hypothetical fleet of 1,700 vehicles serving 68,000 daily trips. We construct scenarios that vary in whether, where, and when SAVs can stage at curbs, as well as whether they strategically reposition to high-demand areas. We also vary the day of the week. According to our simulation results, excluding SAVs from staging at the curb would increase daily VKT by more than 200,000 kilometers, a nearly 60 percent increase compared to scenarios in which SAVs can stage at the curb. In a separate analysis, we find that prohibiting SAV curb staging in residential areas and on curbsides with metered parking would increase empty VKT by 5.4%. We also present key performance metrics that are both temporally and spatially resolved, providing additional policy-relevant information. Simulation modeling is supplemented by expert interviews (n=14) with practitioners, regulators, and policymakers in curbside management and innovative mobility to gain additional insight into policy considerations related to SAV curb access, staging, and parking.

Presentation

• 7th Bridging Transportation Researchers (BTR7) Conference (August 6, 2025, online)

• INFORMS TALENT Workshop 2025 (May 30, 2025, Atlanta, USA)

• 2024 Future Shaping ACE Congress​ (October 28, 2024, online)

• IATBR 17th International Conference on Travel Behaviour Research (July 18, 2024, Vienna, Austria)

• GUT Forum 55th Meeting (June 8, 2024, online)

• 4th IEEE Forum for Innovative Sustainable Transportation Systems (February 27, 2024, Riverside, USA)

• TRB 103rd Annual Meeting (January 10, 2024, Washington DC, USA)

Funded Project

• National Science Foundation Smart and Connected Communities (SCC) Planning Grant (NSF: CMMI-2125560) | 2021–2025

Award

• Yongtaek Lim Award

Driverless or fully automated vehicles (AVs) are expected to fundamentally change how individuals and households travel and how vehicles use roadway infrastructure. The first goal of this study is to develop a modeling framework of activity-constrained household travel in a future multi-modal network with private AVs, shared-use AVs, transit, and intermodal AV-transit travel options. The second goal is to analyze the potential impacts of AVs—including intermodal AV-transit travel—on (a) household-level travel behavior, (b) household travel costs, (c) demand for transport modes, including transit, and (d) vehicle kilometers traveled or VKT. To meet the first goal, we propose and formulate the Household Activity Pattern Problem with AV-enabled Intermodal Trips (HAPP-AV-IT) that incorporates AV deadheading and intermodal AV-transit trips. The modeling framework extends prior HAPP-based formulations that model household-level travel decisions as vehicle (and person) routing and scheduling problems, similar to the pickup and delivery problem with time-windows. To meet the second goal, we apply the HAPP-AV-IT to two case studies and conduct many computational experiments. We use synthetic activity location data for synthetic households and a fictitious medium-size network with a road network, transit network, residential locations, activity locations, and parking locations. The computational results illustrate (a) the critical role that household AV ownership plays in terms of household travel decisions, modal demand, and VKT, (b) that with AVs, deadheading accounts for 30–40% of vehicle operating distances, (c) that around 10% of households in the study region benefit from AV-based intermodal trips, and (d) that those 10 % of households see 5% reductions in household travel costs and 25% reductions in VKT on average in the most transit friendly scenario. This last finding suggests that intermodal AV-transit trips may exist in a driverless vehicle future, and therefore, transit agencies and transportation planners should consider how to serve this market. We also propose and test a simple heuristic algorithm that quickly solves HAPP-AV-IT problem instances.

Presentation

• 94th Conference of the Korean Society of Transportation (March 12, 2026, Gwangju, Korea)

• University of Florida Transportation Institute Seminar (November 14, 2024, online)​

• 4th IEEE Forum for Innovative Sustainable Transportation Systems (February 27, 2024, Riverside, USA)

• ​TRB 103rd Annual Meeting (January 10, 2024, Washington DC, USA)

• 5th Bridging Transportation Researchers (BTR5) Conference (August 10, 2023, online)

• TRB ITAP Conference 2023 (June 5, 2023, Indianapolis, USA)

• Northwestern University Transportation Center Seminar (May 4, 2023, Evanston, USA)

• GUT Forum 36th Meeting (November 5, 2022, online)

Award

• Yongtaek Lim Award

• 2024 KOTAA Travel Grant Award

Term Project

• Smart City Transportation Systems (Fall 2021, Advisor: Dr. M. Hyland)

Cities implemented park-and-ride (PNR) systems to decrease congestion in dense urban areas while providing transit options to travelers who live in a city's low- to medium-density regions. The success of PNR systems is mixed, as they suffer from several disadvantages, namely, the uncertainty of parking locations and infrequent and/or unreliable transit services, and the fact that travelers still need to walk to their destination. Motivated by the premise of PNR systems and the potential of automated vehicles (AVs), to address each of the shortcomings of PNR systems, this study proposes a future system with near-ubiquitous AVs where travelers transfer from privately owned AVs (PAVs) to shared-use, shared-ride AVs (SAVs), called a PAV-SAV transfer system. This study proposes a modeling framework to assess the potential market share of the PAV-SAV transfer system and the network impacts (e.g., congestion, vehicle miles traveled) of the proposed system. Finally, the study identifies good designs for the PAV-SAV transfer system using scenario analysis. The critical design variables are the location of transfer stations, the capacity of SAVs, and the transfer station connector links. For the Greater Los Angeles area, the computational results show a market share for PAV-SAV of almost 18% for trips terminating in downtown Los Angeles. In all scenarios, the proposed PAV-SAV system decreases vehicle hours traveled (VHT) across the whole network with significant decreases in the urban core. For the best system designs, the PAV-SAV system only decreases vehicle miles traveled (VMT) slightly. Locating transfer stations closer to the urban core, increasing vehicle capacities, and connecting transfer stations to both arterial links and highway links improves network performance (i.e., VHT and VMT) and increases the market share of the PAV-SAV system.

Presentation

• 4th Bridging Transportation Researchers (BTR4) Conference (August 4, 2022, online)​

• TRB 101st Annual Meeting (January 11, 2022, Washington DC, USA)

• 2022 KOTAA Annual Meeting (January 9, 2022, Washington DC, USA)

• GUT Forum 22nd Meeting (September 4, 2021, online)

Funded Project

• National Science Foundation Smart and Connected Communities (SCC) Planning Grant (NSF: CMMI-2125560) | 2021–2025

Award

• Yongtaek Lim Award

• 2022 KOTAA Travel Grant Award

The goal of this study is to analyze the impact of private autonomous vehicles (PAVs), specifically their near-activity location travel patterns, on vehicle miles traveled (VMT). The study proposes an integrated mode choice and simulation-based parking assignment model and an iterative solution approach to analyze the impacts of PAVs on VMT, mode choice, parking lot usage, and other system performance measures. The dynamic simulation-based parking assignment model determines the parking location choice of each traveler as a function of the spatial-temporal demand for parking from the mode choice model, while the multinomial logit mode choice model determines mode splits based on the costs and service quality of each travel mode determined partially by the parking assignment model. The paper presents a case study to illustrate the power of the modeling framework. The case study varies the percentage of persons with a private vehicle (PV) who own a PAV vs. own a private conventional vehicle (PCV). The results show that PAV owners travel an extra 0.11 to 1.51 miles compared with PCV owners on average. Hence, as the PCVs are converted into PAVs, total VMT in the network increases substantially. The results further indicate that VMT can be reduced by adjusting parking fees and redistributing parking lot capacities. The significant increase in VMT from PAVs implies that planners should develop policies to reduce PAV deadheading miles near activity locations, as the automated era comes closer.

Publication

• Under review in Transportation Research Part C: Emerging Technologies

Presentation

• ​GUT Forum 69th Meeting (September 14, 2025, online)

• 7th Bridging Transportation Researchers (BTR7) Conference (August 6, 2025, online)

• TRB 102nd Annual Meeting (January 9, 2023, Washington DC, USA)

Peer-to-peer (P2P) ridesharing systems—where independently traveling drivers and riders are matched to share trips—present a scalable strategy for reducing vehicle miles traveled (VMT), improving travel efficiency, and supporting transportation sustainability goals. This study presents a graph-based optimization framework designed to address multi-scale ridematching problems in P2P ridesharing. The framework employs a transshipment network representation to model ridematching scenarios ranging from simple one-to-one matches to more complex one-to-many (multi-hop transfer) and many-to-many (multi-hop with pooling) arrangement. A preprocessing phase first identifies all feasible rider–driver match combinations and trip segments based on spatiotemporal constraints, thereby constructing a tractable matching graph that encodes the viable grouping options. The ridematching problem is then formulated as a linear programming (LP) model that compute system-optimal match assignments to maximize total cost savings relative to a baseline of individual, unshared trips. The proposed framework is demonstrated through simulation experiments using synthetic travel demand from the California Statewide Travel Demand Model (CSTDM) for a morning peak-hour scenario on a large urban road network. We evaluate multiple matching scenarios–including one-to-one, many-to-one, and many-to-many–to quantify the benefits of increasing match flexibility. Results show that relaxing matching constraints by allowing transfers and multi-rider assignments substantially improves system performance. In our experiments, the most flexible many-to-many scenario achieves a match rate of approximately 70–80%, compared to about 50% under one-to-one matching, along with significantly higher total cost savings. The findings highlight that the proposed graph-based framework can greatly enhance operational efficiency in P2P ridesharing systems by increasing the utilization of shared trips. Furthermore, the proposed framework can serve as a practical tool for strategic planning and policy design, allowing transportation planners and service operators to evaluate and implement shared mobility strategies that balance efficiency and user convenience.

Funded Project​​

• National Research Foundation of Korea (NRF) 신진연구 유형 B (RS-2026-25499075) | 2026–2031

Presentation

• WCTR 2026 (July 6, 2026, Toulouse, France)

• TRB 105th Annual Meeting (January 13, 2026, Washington DC, USA)

• KOTI Seminar (January 8, 2026, Sejong, Korea)

• Studio G Small Talk #23 (December 23, 2025, Yongin, Korea)

Funded Project

• National Research Foundation of Korea (NRF) 신진연구 유형 B (RS-2026-25499075) | 2026–2031

• National Science Foundation Smart and Connected Communities (SCC) Planning Grant (NSF: CMMI-2125560) | 2021–2025

Driverless or automated vehicles (AVs) are operating within commercial mobility services (e.g., Waymo) in several US cities, and could be available for personal ownership in the coming decade. With the ability to deadhead empty between activity and parking locations, personal AVs have the potential to dramatically change how households travel and persons and vehicles utilize transportation infrastructure. As such, the goal of this study is to determine how the inclusion of AVs in household vehicle fleets changes travel patterns and infrastructure usage. To address this goal, we propose the Mixed-Fleet Activity-based Household Routing Problem (MAHRP), where the mixed fleet includes AVs and conventional (i.e., non-automated) vehicles (CVs). MAHRP extends existing mathematical programming models of activity-constrained household travel decisions by incorporating both CVs and AVs. We use San Diego, California, as our case study region, and utilize the regional planning agency’s activity-based travel demand model to obtain synthetic households from a realistic distribution of household attributes and activity locations. Pivoting from a baseline scenario where the San Diego households only own CVs, we define several scenarios that vary the total number and mix of household AVs and CVs. The case study results, with 115 households, indicate that AVs (i) decrease generalized household travel costs by 25–27%, (ii) increase auto mode shares by 5–6 percentage points to 94–95% of all trips, (iv) increase vehicle miles traveled by 19–27%, and (iii) reduce demand for non-home parking spaces from 9 to 5–7 hours per day.

Funded Project​​

• National Research Foundation of Korea (NRF) 신진연구 유형 B (RS-2026-25499075) | 2026–2031