Traffic Operations and Management

From LOS to VMT, VHT and Beyond Through Data Fusion: Application to Integrate Corridor Management

Alexandre Bayen
Gan, Qijian
Gomes, Gabriel
2016

Traffic performance metrics such as delay and Level Of Service (LOS), which are well documented in the Highway Capacity Manual (HCM), have been widely used by most of the transportation consulting companies, public agencies, and etc. For arterial delay analysis, prevailing commercial tools like Synchro have adopted the method proposed by the HCM, which is rooted in the Webster’s delay calculation proposed more than 50 years ago. The LOS is obtained using a lookup table that assigns a certain grade (from A to F) to the estimated delay according to its value. Without knowing detailed...

Heterogeneous Fleets of Active and Passive Floating Sensors for River Studies

Tinka, Andrew
Wu, Qingfang
Weekly, Kevin
Oroza, Carlos
Beard, Jonathan
Alexandre Bayen
2016

Lagrangian sensing for tracing hydrodynamic trajectories is an innovative approach for studying estuarial environments. Actuated Lagrangian sensors are capable of avoiding obstacles and navigating when active and retain a passive hydrodynamic profile that is suited for Lagrangian sensing when passive. A heterogeneous fleet of actuated and passive drifting sensors is presented. Data assimilation using a high-performance computing (HPC) cluster that runs the ensemble Kalman filter (EnKF) is an essential component of the estuarial state estimation system. The performance of the mixed...

Creating Complex Congestion Patterns via Multi-Objective Optimal Freeway Traffic Control with Application to Cyber-Security

Reilly, Jack
Martin, Sébastien
Payer, Mathias
Alexandre Bayen
2016

This article presents a study on freeway networks instrumented with coordinated ramp metering and the ability of such control systems to produce arbitrarily complex congestion patterns within the dynamical limits of the traffic system. The developed method is used to evaluate the potential for an adversary with access to control infrastructure to enact high-level attacks on the underlying freeway system. The attacks are executed using a predictive, coordinated ramp metering controller based on finite-horizon optimal control and multi-objective optimization techniques. The efficacy of the...

Optimizing the Diamond Lane: A More Tractable Carpool Problem and Algorithms

Wu, Cathy
Shankari, K.
Kamar, Ece
Katz, Randy
Culler, David
Alexandre Bayen
2016

Carpooling has been long deemed a promising approach to better utilizing existing transportation infrastructure. However, there are several reasons carpooling is still not the preferred mode of commute in the United States: first, complex human factors, including time constraints and not having right incentive structures, discourage the sharing of rides; second, algorithmic and technical barriers inhibit the development of online services for matching riders. In this work, we study algorithms for 3+ high-occupancy vehicle (HOV) lanes, which permit vehicles that hold three or more people....

Negative Externalities of GPS-Enabled Routing Applications: A Game Theoretical Approach

Thai, Jérôme
Laurent-Brouty, Nicolas
Alexandre Bayen
2016

This work studies the impact of the increasing penetration of routing apps on road usage. Its conclusions apply both to manned vehicles in which human drivers follow app directions, and unmanned vehicles following shortest path algorithms. To address the problem caused by the increased usage of routing apps, we model two distinct classes of users, one having limited knowledge of low-capacity road links. This approach is in sharp contrast with some previous studies assuming that each user has full knowledge of the network and optimizes his/her own travel time. We show that the increased...

Framework for Control and Deep Reinforcement Learning in Traffic

Wu, Cathy
Parvate, Kanaad
Kheterpal, Nishant
Dickstein, Leah
Mehta, Ankur
Vinitsky, Eugene
Alexandre Bayen
2017

Recent advances in deep reinforcement learning (RL) offer an opportunity to revisit complex traffic control problems at the level of vehicle dynamics, with the aim of learning locally optimal policies (with respect to the policy parameterization) for a variety of objectives such as matching a target velocity or minimizing fuel consumption. In this article, we present a framework called CISTAR (Customized Interface for SUMO, TraCI, and RLLab) that integrates the widely used traffic simulator SUMO with a standard deep reinforcement learning library RLLab. We create an interface allowing for...

Traffic State Estimation on Highway: A Comprehensive Survey

Seo, Toru
Alexandre Bayen
Kusakabe, Takahiko
Asakura, Yasuo
2017

Traffic state estimation (TSE) refers to the process of the inference of traffic state variables (i.e., flow, density, speed and other equivalent variables) on road segments using partially observed traffic data. It is a key component of traffic control and operations, because traffic variables are measured not everywhere due to technological and financial limitations, and their measurement is noisy. Therefore, numerous studies have proposed TSE methods relying on various approaches, traffic flow models, and input data. In this review article, we conduct a survey of highway TSE methods, a...

Spatio-temporal Road Charge: A Potential Remedy for Increasing Local Streets Congestion

Alexandre Bayen
Forscher, Teddy
2017

US population. Additionally, the emergence of large ridesourcing or transportation network companies (TNCs) totaling up to tens of thousands of registered drivers in single cities (all using the same routing app), there is further consolidation. Across the US, this has led to new or increased congestion patterns that are progressively asphyxiating local streets due to so-called “cut-through traffic.” As neighborhoods have started to realize this, private citizens have begun to resist, by trying to sabotage or trick the apps, or shaming the through traffic through opinion articles, and news...

Filter Comparison for Estimation on Discretized PDEs Modeling Traffic: Ensemble Kalman Filter and Minimax Filter

Seo, Toru
Tchrakian, Tigran T.
Zhuk, Sergiy
Alexandre Bayen
2016

Traffic State Estimation (TSE) refers to the estimation of the state (i.e., density, speed, or other parameters) of vehicular traffic on roads based on partial observation data (e.g., road-side detectors and on-vehicle GPS devices). It can be used as a component in applications such as traffic control systems as a means to identify and alleviate congestion. In existing studies, the Kalman Filter and its extensions, notably the Ensemble Kalman Filter (EnKF), are commonly employed for TSE. Recently, the MF has been newly adapted to this domain as a filtering algorithm for TSE. In this...

Emergent Behaviors in Mixed-Autonomy Traffic

Wu, Cathy
Kreidieh, Aboudy
Vinitsky, Eugene
Alexandre Bayen
2017

Traffic dynamics are often modeled by complex dynamical systems for which classical analysis tools can struggle to provide tractable policies used by transportation agencies and planners. In light of the introduction of automated vehicles into transportation systems, there is a new need for understanding the impacts of automation on transportation networks. The present article formulates and approaches the mixed-autonomy traffic control problem (where both automated and human-driven vehicles are present) using the powerful framework of deep reinforcement learning (RL). The resulting...