A Tier 1 University Transportation Center taking on today’s mobility challenges

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An image of a car underneath a wifi symbol, indicating mobility options with smart technology.
The image of a person, a vehicle, and a shield, meant to evoke safety.
The image of a smart phone with people on it, meant to be evocative of shared transportation modes.
A computer with graphs and charts, meant to represent big data.
The image of a bridge, in good condition.
Three people standing in a halo, meant to indicate equity.

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What makes a parking facility “smart?” Professor Kelvin Cheu developed a Level of Service (LOS—used by transportation engineers to communicate service quality) analysis procedure for parking lots and garages to address a research gap in the current Highway Capacity Manual. He also developed a Concept of Operations for a Performance Management System (PMS) used to estimate benefits, in terms of search time and LOS, by converting a conventional garage into a smart garage, and by converting on-street parking into Smart On-Street Parking (SOSP), using on-street parking data from Los Angeles, California, and El Paso, Texas. His work will inform new guidelines for transportation engineers and ITS system architects for designing parking PMS and opens a new approach for the analysis of on-street parking performance that could be used to link to dynamic parking pricing, saving time, improving safety, and reducing congestion and emissions.
Parking for Smart Cities
YouTube Video UC-JzRVBnWGJ3_Jv5iYmpo6A_NFU2rWbedck
In the fall of 2020, C2SMART launched the “C2SMART Student Learning Hub”, free for all consortium member students. Students were able to access learning from a variety of course domains, including data science, computer science, and traffic simulation. The Hub is designed to offer students hands-on experience to learn the tools and skills they will need as they advance their careers, whether in academia, industry, or within government agencies.
C2SMART Student Learning Hub
YouTube Video UC-JzRVBnWGJ3_Jv5iYmpo6A_h3lim861i_s
Traffic congestion has become a serious issue around the globe, partly owing to single-occupancy commuter trips. Ridesharing can present a suitable alternative for serving commuter trips. However, there are several important obstacles that impede ridesharing systems from becoming a viable mode of transportation, including the lack of a guarantee for a ride back home as well as the difficulty of obtaining a critical mass of participants. This study addresses these obstacles by introducing a Traveler Incentive Program (TIP) to promote community-based ridesharing with a ride-back home guarantee among commuters. The TIP program allocates incentives to (1) directly subsidize a select set of ridesharing rides, and (2) encourage a few, carefully selected set of travelers to change their travel behavior. We formulate the underlying ride-matching problem as a budget-constrained min-cost flow problem and present a Lagrangian Relaxation-based algorithm with worst-case optimality bound to solve large-scale instances of this problem in polynomial time. We further propose a polynomial-time budget-balanced version of the problem. Numerical experiments suggest that allocating subsidies to change travel behavior is significantly more beneficial than directly subsidizing rides. Furthermore, using a flat tax rate as low as 1% can double the system’s social welfare in the budget-balanced variant of the incentive program. Neda Masoud is an Assistant Professor of Civil and Environmental Engineering at the University of Michigan. She holds a Bachelor’s of Science Degree in Industrial Engineering and a Master’s of Science degree in Physics. She received her Ph.D. in Civil and Environmental Engineering from the University of California Irvine. Her research focuses on devising operational and planning tools to facilitate the transition into the next generation of mobility systems, which are envisioned to be connected, automated, electrified, and shared.
Incentive Design for Promoting Ridesharing
YouTube Video UC-JzRVBnWGJ3_Jv5iYmpo6A_ONTwsOul_vE
In this seminar, we will talk about the motivation, design framework, and initial implementation of a multi-scale transportation simulation platform. The platform simulates the movements of connected/automated vehicles (Unity), traffic flow dynamics (SUMO), and demand generation on a large-scale transportation network (MATSim). We focus on the SUMO-MATSim development details and the current challenges of integrating the different simulation tools. We will also mention how the multi-scale simulation platform may be used for conducting research for emerging technologies and systems in transportation. Keywords: Autonomous Vehicles, Connected Vehicles, Simulation, Traffic Demand, Traffic Flow
Developing a Multiscale Vehicle-traffic-demand (VTD) Simulation Platform
YouTube Video UC-JzRVBnWGJ3_Jv5iYmpo6A_aehelG29ODY
The COVID-19 pandemic has drastically impacted public life around the world. Public transport, which seems to be inherently incompatible with the social distancing defense against transmission, has been hit particularly hard. As a result of reduced travel demand and explicit advisories against public transport use, ridership has declined sharply, perceptions regarding this mode have become more negative, and there has been a significant shift to personal transport modes. In order to restore confidence in public transport, it is critical to understand how travelers have adapted their behavior under these exigent circumstances and assess which trip-related and contextual factors contribute to the perceived risk of contracting COVID-19 while traveling. Sanmay Shelat and researchers at Delft University of Technology conducted two stated choice experiments with train travelers in the Netherlands. In the first, they analyzed and segmented travelers according to behavior related to three criteria affecting the risk of COVID-19 transmission, namely distance to others, exposure duration, and prevalent infection rate. For the second study, a hierarchical information integration approach was used where they estimated the importance of various factors (e.g., face mask use, lockdown status, on-board crowding, etc.) on perceived COVID-19 risk in public transport and evaluated how perceived risk was traded-off with other travel attributes in a two-stage experiment. Results from these studies will be useful for demand forecasting and understanding the impact of COVID-19 related fear on travel behavior in public transport. Insights from the study may be valuable for policy designs aimed at managing demand and designing marketing campaigns that target skeptical groups and showcase measures mitigating perceived risk.
Traveler Behavior and Public Transport in the Age of COVID-19
YouTube Video UC-JzRVBnWGJ3_Jv5iYmpo6A_coRttI8MIEM
Researchers from UNC Charlotte, present their recent research on using the power of Artificial Intelligence (AI) and Augmented Reality (AR) to enhance the safety of highway workers. They'll discusses the challenges and opportunities to transitioning from the existing reactive safety systems to a true proactive safety system for highway work zones, providing innovative solutions for real-time vehicles trajectory analysis and path prediction, and AR-based User Interface design to provide on-the-spot notification to highway workers. They'll also discuss some challenges to bringing real-time AI to highway work zones, from technology and data limitations to user acceptance and safety protocols.
Toward Smart Highway Work Zones: When Highway Workers Meet AI and AR
YouTube Video UC-JzRVBnWGJ3_Jv5iYmpo6A_jeA1JLYz2mw
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