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Incentive Design for Promoting Ridesharing

Virtual 6 MetroTech Center, Brooklyn, NY, United States

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 event addresses these obstacles by introducing a Traveler Incentive Program (TIP) to promote community-based ridesharing with a ride-back home guarantee among commuters.

Proactive Safety Management Empowered by Big Data

Virtual 6 MetroTech Center, Brooklyn, NY, United States

Professor Kun Xie will share a more proactive and time-efficient approach based on surrogate safety measures (SSMs), which can assess safety by capturing the more frequent “near-crash” situations.

State of the Field: Structural Health Monitoring (SHM) towards Infrastructure Resiliency

Virtual 6 MetroTech Center, Brooklyn, NY, United States

What does the best of transportation engineering research have to say about maintenance, rehabilitation, and replacement of critical infrastructure? Structural Health Monitoring provides critical insight into answering these questions using new technologies that are changing the ways we tackle maintenance and rehabilitation of structures.

Lane Changing of Autonomous Vehicles in Mixed Traffic Environments: A Reinforcement Learning Approach

Virtual Event Virtual Event

The emergence of connected and autonomous vehicles (CAVs) presents increased opportunities to mitigate traffic congestion, improve safety and reduce accidents. Professor Zhong-Ping Jiang, and researchers Leilei Cui and Sayan Chakraborty are applying innovative reinforcement learning control methods to one challenging aspect of CAV control: lane changing in mixed traffic. The team takes a novel approach by reducing the trajectory planning and tracking problem down to the minimization of a cost function that depends on a target way-point in the lane a CAV is targeting.