traffic

This project aims to develop a simplified method for estimation of emissions and energy consumption in large-scale networks using the macroscopic fundamental diagram (MFD).

Researchers propose a hierarchical ramp control system that allows microscopic cooperative maneuvers for connected and automated electric vehicles (CAEVs) on the ramp to merge into mainline traffic flow under certain controlled ramp inflow rate.

This includes data used in the project "Coordinated Traffic Flow Control in a Connected Environment." The data sharing mechanism strengthens the connection between the freeway and arterial networks and enhances the control performance.

This contains the entire dataset used in the NCST report: Evaluating Alternative Strategies for Traffic Reduction in Los Angeles, consisting of data from Caltran's Performance Measurement System and on vehicle crashes in the Los Angeles Metropolitan Statistical Area.

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.