Our research focuses on network optimization and demand modeling for shared mobility systems (e.g., carsharing, parking-sharing and ridesharing) and multimodal mobility service (e.g., Mobility-as-a-Service). We also investigated the impact of risk perception and risk attitude in travel behavior using experimental economics approaches and the application of machine learning methods in transport modeling.
Our techniques combine ideas from various research areas, including linear programming, nonlinear optimization, bi-level optimization, game theory, mechanism design, statistics, discrete choice modeling, experimental economics, machine learning and reinforcement learning.
Parking is one of the main concerns for carsharing users, since a user must park the car somewhere after each trip. If users are allowed to park the cars at paid parking spaces, there would be a parking fee incurred between…Keep reading
The day-to-day evolution of travel choices (e.g., departure time choice and mode choice) are impacted by various factors. Reliability of transport services is one of these factors, which significantly affects their attractiveness and further influences the travel demand. This study develops…Keep reading
An Integrated Supply-Demand Approach to Solving Optimal Relocations in Station-Based Carsharing Systems
The dominant challenge in one-way carsharing systems is the vehicle stock imbalance. Previous studies have proposed relocation approaches to handle it using optimization and simulation models. However, these models do not consider the interdependence between supply and demand in carsharing systems.…Keep reading
To determine the most efficient allocation of resources within a carsharing program, it is critical to understand what factors affect the users’ behavior when selecting vehicles. This study attempts to investigate the importance of users’ attributes and fleet characteristics on choice…Keep reading
Facing the growing demand for carsharing services, it is critical for operators to accurately predict users’ preferences on different vehicle types and their vehicle usage. This vehicle choice behavior involves choosing multiple vehicle types simultaneously and allocating continuous amounts of budget…Keep reading
Collecting effective data is a fundamental step in developing transport networks and related research. Social media have become an emerging source of data for traffic analyses. In this study, we demonstrate that the function of a city influences the utility of…Keep reading
Automated driving has been predicted to be transformational in improving safety and productivity on roads. However, there are several unknowns with regards to how drivers’ will interact with automated vehicles, especially at moments requiring manual resumption of vehicle control. This is…Keep reading
The design of transit networks is a challenging task for transportation planners that span numerous often counter-intuitive considerations. Broadly, the Network Design Problem (NDP) can be approached through cost-benefit models that are capable of representing multiple aspects of system-wide behavior, such…Keep reading