Traffic Operations and Management

A Methodology for Evaluating the Performance of Model-Based Traffic Prediction Systems

Gomes, Gabriel
Gan, Qijian
Bayen, Alexandre M.
2018

Model-based traffic prediction systems (mbTPS) are a central component of the decision support and ICM (integrated corridor management) systems currently used in several large urban traffic management centers. These models are intended to generate real-time predictions of the system’s response to candidate operational interventions. They must therefore be kept calibrated and trustworthy. The methodologies currently available for tracking the validity of a mbTPS have been adapted from approaches originally designed for off-line operational planning models. These approaches are insensitive...

Time-Continuous Instantaneous and Past Memory Routing on Traffic Networks: A Mathematical Analysis on the Basis of the Link-Delay Model

Bayen, Alexandre
Keimer, Alexander
Porter, Emily
Spinola, Michele
2019

The problem of routing messages along near-shortest paths in a distributed network without using complete routing tables is considered. It is assumed that the nodes of the network can be assigned suitable short names at the time the network is established. Two space-efficient near-shortest-path routing schemes are given for the class of planar networks. Both schemes use the separator property of planar networks in assigning the node names and performing the routings. For an n-node network, the first scheme uses $O(\log n)$-bit names and a total of $O(n^{{4 / 3}} )$ items of routing...

Boundary Observer for Congested Freeway Traffic State Estimation via Aw-Rascle-Zhang model

Yu, Huan
Bayen, Alexandre M.
Krstic, Miroslav
2019

This paper develops boundary observer for estimation of congested freeway traffic states based on Aw-Rascle-Zhang(ARZ) partial differential equations (PDE) model. Traffic state estimation refers to acquisition of traffic state information from partially observed traffic data. This problem is relevant for freeway due to its limited accessibility to real-time traffic information. We propose a boundary observer design so that estimates of aggregated traffic states in a freeway segment are obtained simply from boundary measurement of flow and velocity. The macroscopic traffic dynamics is...

Simulation to Scaled City: Zero-Shot Policy Transfer for Traffic Control via Autonomous Vehicles

Jang, Kathy
Vinitsky, Eugene
Chalaki, Behdad
Remer, Ben
Beaver, Logan E.
Malikopoulos, Andreas A.
Bayen, Alexandre
2019

Using deep reinforcement learning, we successfully train a set of two autonomous vehicles to lead a fleet of vehicles onto a round-about and then transfer this policy from simulation to a scaled city without fine-tuning. We use Flow, a library for deep reinforcement learning in microsimulators, to train two policies, (1) a policy with noise injected into the state and action space and (2) a policy without any injected noise. In simulation, the autonomous vehicles learn an emergent metering behavior for both policies which allows smooth merging. We then directly transfer this policy without...

Regrets in Routing Networks: Measuring the Impact of Routing Apps in Traffic

Cabannes, Theophile
Sangiovanni, Marco
Keimer, Alexander
Bayen, Alexandre M.
2019

The impact of the recent increase in routing apps usage on road traffic remains uncertain to this day. The article introduces, for the first time, a criterion to evaluate a distance between an observed state of traffic and the user equilibrium of the traffic assignment: the average marginal regret. The average marginal regret provides a quantitative measure of the impact of routing apps on traffic using only link flows, link travel times, and travel demand. In non-atomic routing games (or static traffic assignment models), the average marginal regret is a measure of selfish drivers’...

Routing on Traffic Networks Incorporating Past Memory up to Real-Time Information on the Network State

Keimer, Alexander
Bayen, Alexandre
2020

In this review, we discuss routing algorithms for the dynamic traffic assignment (DTA) problem that assigns traffic flow in a given road network as realistically as possible. We present a new class of so-called routing operators that route traffic flow at intersections based on either real-time information about the status of the network or historical data. These routing operators thus cover the distribution of traffic flow at all possible intersections. To model traffic flow on the links, we use a well-known macroscopic ordinary delay differential equation. We prove the existence and...

Zero-Shot Autonomous Vehicle Policy Transfer: From Simulation to Real-World via Adversarial Learning

Chalaki, Behdad
Beaver, Logan E.
Remer, Ben
Jang, Kathy
Vinitsky, Eugene
Bayen, Alexandre
Malikopoulos, Andreas A.
2020

In this article, we demonstrate a zero-shot transfer of an autonomous driving policy from simulation to University of Delaware's scaled smart city with adversarial multi-agent reinforcement learning, in which an adversary attempts to decrease the net reward by perturbing both the inputs and outputs of the autonomous vehicles during training. We train the autonomous vehicles to coordinate with each other while crossing a roundabout in the presence of an adversary in simulation. The adversarial policy successfully reproduces the simulated behavior and incidentally outperforms, in terms of...

Optimizing Mixed Autonomy Traffic Flow With Decentralized Autonomous Vehicles and Multi-Agent RL

Vinitsky, Eugene
Lichtle, Nathan
Parvate, Kanaad
Bayen, Alexandre
2020

We study the ability of autonomous vehicles to improve the throughput of a bottleneck using a fully decentralized control scheme in a mixed autonomy setting. We consider the problem of improving the throughput of a scaled model of the San Francisco-Oakland Bay Bridge: a two-stage bottleneck where four lanes reduce to two and then reduce to one. Although there is extensive work examining variants of bottleneck control in a centralized setting, there is less study of the challenging multi-agent setting where the large number of interacting AVs leads to significant optimization difficulties...

Continual Learning of Microscopic Traffic Models Using Neural Networks

Farid, Yashar Zeinali
Kreidieh, Abdul Rahman
Khalighi, Farnoush
Lobel, Hans
Bayen, Alexandre M.
2021

In a mixed-autonomy traffic scenario, where human drivers and autonomous vehicles share the streets, self-driving cars need to be able to predict in a robust manner the behaviour of human-driven vehicles, in order to guarantee a safe and smooth driving experience. Although traffic theory provides several models of human drivers, these models are often parameterized by few parameters which can limit their performance in modeling complex behaviors. The lack of sufficient model capacity and the behavioral shifts in human driving reduces the usefulness of these methods in real-life...

Solving N-Player Dynamic Routing Games with Congestion: A Mean Field Approach

Cabannes, Theophile
Lauriere, Mathieu
Perolat, Julien
Marinier, Raphael
Girgin, Sertan
2021

The recent emergence of navigational tools has changed traffic patterns and has now enabled new types of congestion-aware routing control like dynamic road pricing. Using the fundamental diagram of traffic flows - applied in macroscopic and mesoscopic traffic modeling - the article introduces a new N-player dynamic routing game with explicit congestion dynamics. The model is well-posed and can reproduce heterogeneous departure times and congestion spill back phenomena. However, as Nash equilibrium computations are PPAD-complete, solving the game becomes intractable for large but realistic...