Ph.D. Defense: Fatemeh Alimardani

Wednesday, July 6, 2022
1:00 p.m.
AVW 1146
Maria Hoo
301 405 3681
mch@umd.edu

ANNOUNCEMENT: Ph.D. Defense
 
 
 
Name:  Fatemeh Alimardani

Committee:
Professor John S. Baras (Chair)
Professor Eyad H. Abed
Professor William Levine
Professor Dinesh Manocha
Professor Bruce L. Golden (Dean’s Representative)

Date/time: 
Wednesday, July 6th, 2022 at 1:00 pm
 
Location:
AVW 1146
 
Title:
Dynamic Traffic Management of Highway Networks

 
 
Abstract:
Efficient operation of traffic networks via management strategies can guarantee overall societal benefits for both humans and the environment. As the number of vehicles and the need for transportation grows, dynamic traffic management aims to increase the safety and efficiency of the traffic networks without the need to change the infrastructure of the existing roads. Since the highway networks are considered permanent investments that are expensive to build and maintain, the main scope of this dissertation is to propose traffic flow models and methods to improve the efficiency of the current highway systems without the need to change their infrastructure.

 When all vehicles in a network are Human-Driven Vehicles (HDVs), and changing the infrastructure is either so expensive or impossible, then one reasonable approach to improving the efficiency of traffic networks is through the control of traffic signal lights especially because the behavior of the human drivers cannot be directly controlled. A literature review of highway traffic control demonstrates that Ramp Metering (RM) is one of the most commonly used approaches as it improves the network performance in regards to travel time, travel distance, throughput, etc, and cost-wise, it is a very economical approach. As such, in this research, the ultimate goal focus is to extend the current literature on traffic management of highway networks by offering new models and algorithms to improve this field.

To reach this goal, the first step is to focus on improving and extending the current traffic flow models. There are two categories of traffic flow models in the literature: First-order models, and Second-order models. Many different extensions of the famous first-order model called the Cell-Transmission Model (CTM) have been proposed throughout the past decades, each one proposed based on different criteria and the specific needs of different applications. In the first part of this dissertation, a performance assessment of the most important extensions of CTM will be performed. Then, based on this evaluation, an extended version of the CTM, called the Piecewise Affine Approximation-CTM (PWA-CTM ), will be offered which will be proven to have better performance regarding the evolution of traffic flow and computation time compared to the previous versions of this model.

In the next step, the focus will be shifted to second-order models as they have better capabilities for modeling the behavior of traffic flow compared to the first-order models. However, any optimization scheme for highway traffic control based on these models is highly nonlinear and computationally intensive. As such, in this part of the research, a linearization of the famous second-order model called the METANET will be offered which is based on partially Piecewise Affine Approximations and Synthetic Data Generation techniques. With extensive simulations, it will be shown that this linearized approximation can greatly impact the computational complexity of any optimization-based traffic control framework based on this second-order traffic flow model.

 Moreover, to have significant traffic management improvements, not only the underlying traffic models but also the control strategies should be enhanced. The availability of increasing computational power and sensing and communication capabilities, as well as advances in the field of machine learning, have developed learning-based control approaches that can address constraint satisfaction and closed-loop performance optimization. In this chapter, Reinforcement Learning (RL) algorithms will be investigated to solve the optimal control problem of RM. In the case of RM, RL-based techniques offer a potentially appealing alternative method to solve the problem at hand, since they are data-based and make no assumptions about the underlying model parameters. Towards this direction, it is convenient to study the road model as a multi-agent system of non-homogeneous networked agents. In the following, a novel formulation of the RM problem as an optimal control problem based on a first-order multi-agent dynamical system will be offered. Then, applying policy gradient RL algorithms, a probabilistic policy will be found that solves the ramp-metering problem. Finally, the effect of risk-sensitivity in the optimal solution will be studied by applying new risk-sensitive policy-gradient reinforcement learning algorithms based on exponential criteria.
 

Audience: Graduate  Faculty 

remind we with google calendar

 

August 2022

SU MO TU WE TH FR SA
31 1 2 3 4 5 6
7 8 9 10 11 12 13
14 15 16 17 18 19 20
21 22 23 24 25 26 27
28 29 30 31 1 2 3
Submit an Event