traffic

The analysis of trajectory datasets has numerous applications ranging from urban planning to human mobility understanding, but to protect the privacy of individuals trajectory datasets are rarely released to researchers. The team uses their proposed, and recently published at IEEE BigData 2022 conference, Data-Driven Trajectory Generator, dubbed DDTG, to generate a synthetic vehicle trajectory dataset in the metropolitan city of Los Angeles.

In this research, we will build upon this past research to develop a new cooperative traffic operation approach that takes advantage of not only infrastructure-to-vehicle communications, but also vehicle-to-infrastructure communications. This effort integrates a dynamic traffic signalization algorithm together with EAD algorithm to achieve even greater traffic efficiency.

This research proposes an adaptive strategy for connected eco-driving towards a signalized intersection under real world conditions.

This project proposes to improve vehicle utilization and energy efficiency by modeling and evaluating the innovative shared mobility services for freight, with a specific focus on on-demand food delivery. Overall, the shared mobility service has great potential to reduce freight transportation VMT cost and emission. With well-designed delivery policy, the on-demand food delivery can mitigate traffic in the urban city and bring a greener transportation system.

This research is aimed at developing and evaluating eco-friendly intelligent transportation systems strategies for freight vehicles and traffic, with a focus on strategies that are applicable to the transportation systems in the South Coast Air Basin.