traffic

This dissertation aims to study effective and efficient ways for both travelers and transportation authorities to consider the actions of the other side when they make their corresponding travel or management decisions, such that certain common goals, such as mitigating congestion, reducing cost in travel expenses and improving the overall reliability of the transportation system can be achieved.

In this paper, the authors build upon 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. The research was carried out in a high-fidelity simulation environment and shows upwards of 15% fuel savings and 85% reductions in waiting time.

In this paper, focusing on meal delivery, the researchers proposed an eco-friendly on-demand meal delivery (ODMD) system to maximize the utilities of electric vehicles (EVs) to mitigate greenhouse gas (GHG) emissions and maintain low operational cost and delay cost.

This study demonstrates a model framework that combines a microscopic traffic simulation with emissions and microscale dispersion models to quantify the potential impacts of truck-only lanes on fuel consumption, emissions, and near-road pollutant concentrations.

This paper proposes a unified framework to quantify the collective cooperativeness of driving agents in mixed-autonomy traffic, based on a behavioral equilibrium model and real-world trajectory data.

This project is to investigate how accessibility of city blocks is quantified through the transport systems and real traffic flow data from the Los Angeles Metropolitan Area. The authors investigate the reachability problem and provide a solution with a functional system that is capable of visualizing the reachability map (isochrone)