Dissertation/Thesis Abstract

Future Traffic Management System Empowered by Reinforcement Learning and V2X Technologies
by Zhang, Rusheng, Ph.D., Carnegie Mellon University, 2019, 157; 27545076
Abstract (Summary)

Traffic congestion in major cities around the globe is a daunting problem that is getting worse as the speed of urbanization keeps increasing. The role of traffic lights at intersections in regulating flows cannot be underestimated. Though there are a variety of actuated traffic signals based on cameras or loop detectors, the cost of these technologies is prohibitively large. This is why the vast majority of existing traffic lights in the world employ a timer-based decision logic which is clearly not very effective.

Dedicated Short-range Communication (DSRC) is a relatively new short to medium range wireless communication technology used for inter-vehicular communication. With this communications technology or other existing technologies such as cellular, Wifi, Bluetooth or UWB, an intelligent traffic signal control (ITSC) can be much more cost-effective since the majority of intersections can be equipped with actuated traffic signals with low cost.

On the other hand, wireless Vehicle-to-Vehicle (V2V) or Vehicle to Infrastructure (V2I) communications based ITSC is considerably different from traditional ITS; hence, adapting current ITS to V2X based systems is pretty difficult. Traffic control algorithms will also need to be re-designed to implement the unique properties of wireless communication.

In this thesis, several new forms of possible future infrastructure-based and infrastructure-free ITSC systems are researched, in terms of simulation results, prototype system design, algorithm studies. The future traffic systems introduced are Virtual Traffic Lights (VTL), DSRC-Actuated traffic signal control (DSRC-ATSC), and a more advanced version of DSRC-ATSC: Partially Detected Intelligent Traffic Signal Control (PD-ITSC) System. We explore the capacity of state-of-art Reinforcement Learning algorithms on the PD-ITSC systems.

Indexing (document details)
Advisor: Tonguz, Ozan K.
Commitee: Bhagavatula, Vijayakumar, Dolan, John M., Joe-Wong, Carlee, Talty, Timothy
School: Carnegie Mellon University
Department: Electrical and Computer Engineering
School Location: United States -- Pennsylvania
Source: DAI-A 81/5(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Communication, Artificial intelligence, Computer Engineering
Keywords: Intelligent traffic system, Reinforcement learning, Vehicle-to-infrastructure communication, Vehicle-to-vehicle communication, Vehicular network, Wireless communication
Publication Number: 27545076
ISBN: 9781392644287
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