Negar Mehr, UC Berkeley. “Analysis and Control of the Current and Future Traffic Networks”

Position:  PhD Candidate

Current Institution:  University of California, Berkeley

Abstract:  Analysis and Control of the Current and Future Traffic Networks

The rapid increase of vehicular traffic congestion, delays, and emissions in metropolitan areas points to the importance of traffic management and control. As autonomous vehicles become tangible technologies, it will be crucial to investigate the impacts of autonomy deployment on such costs. Aligned with this, I have pursued a two-fold theme of research work: developing control and planning strategies for mitigating congestion in current traffic networks and studying the potential impacts of autonomy deployment on future transportation networks. Because infrastructure expansion is a prolonged and high-priced process, development of traffic control strategies for increasing the throughput of the existing traffic networks is important. We have worked on development of such strategies and studied their effectiveness in various case studies. We have developed ramp-metering strategies and traffic-signal controls for reducing travel time of vehicles in freeway and urban traffic networks. To derive effective controls, we have also developed traffic models capable of capturing network-wide impact of drivers’ behavior such as lane change maneuvers. Recently, with the emergence of autonomous vehicles, it is envisioned that autonomy deployment will boost the network mobility. We have studied the validity of this impact under selfish routing behavior of the drivers in networks with mixed autonomy. We derived the conditions under which the network mobility is guaranteed to increase as a result of autonomy increase. We have shown that when these conditions do not hold, deployment of autonomous vehicles can worsen the total delay in traffic networks. Currently, we are studying routing and pricing mechanisms which can mitigate the inefficiencies that arise from the coexistence of autonomous and regular vehicles.

Negar Mehr is a PhD candidate in the Department  of Mechanical Engineering at UC Berkeley. She received a bachelor’s degree in mechanical engineering from Sharif University of Technology in Iran in 2013.  She is the co-recipient of the first prize for the best student paper award at the International Conference on Intelligent Transportation Systems 2016.  She is the graduate winner of the 2017 Women Transportation Seminars (WTS-OC) scholarship.  Negar was awarded the Chang-Lin Tien Graduate Fellowship in 2015 and 2017.  She was also awarded the Graduate Division Block Grant Award in 2015 and 2018 and Eltoukhy East-West Gateway Fellowship in 2013.  Her research interests include controls cyber-physical systems transportation engineering and robotics.  Her current focus is on analyzing transportation networks with mixed autonomy.  She is developing algorithms and scalable tools for efficient management of future traffic networks.