Data

Evaluation of Horizontal and Vertical Queueing Models in Relation to Observed Trajectory Data in a Signalized Urban Traffic Network

Anderson, Leah
Gomes, Gabriel
Bayen, Alexandre M.
2015

While the Cell Transmission Model (CTM) is generally accepted as a standard representation of traffic flows on freeways with long links and uninterrupted flows, less is known about the accuracy of CTM or other macroscopic queueing models on urban road networks with short links and frequent flow blockages due to signal control. In fact, almost all existing validations of CTM focus on modeling freeways. In this paper, the authors aim to provide evidence towards selecting the appropriate queueing model dynamics for use in analysis and control of a large-scale network of signalized...

Projected Sub-Gradient with ℓ 1 or Simplex Constraints via Isotonic Regression

Thai, Jérôme
Wu, Cathy
Pozdnukhov, Alexey
Bayen, Alexandre
2015

We consider two classic problems in convex optimization: 1) minimizing a convex objective over the nonnegative orthant of the ℓ1-ball and 2) minimizing a convex objective over the probability simplex. We propose an efficient and simple equality constraint elimination technique which converts the ℓ1 and simplex constraints into order constraints. We formulate the projection onto the feasible set as an isotonic regression problem, which can be solved exactly in O(n) time via the Pool Adjacent Violators Algorithm (PAVA), where n is the...

Variational Lagrangian Data Assimilation in Open Channel Networks

Wu, Qingfang
Tinka, Andrew
Weekly, Kevin
Beard, Jonathan
Bayen, Alexandre M.
2015

This article presents a data assimilation method in a tidal system, where data from both Lagrangian drifters and Eulerian flow sensors were fused to estimate water velocity. The system is modeled by first-order, hyperbolic partial differential equations subject to periodic forcing. The estimation problem can then be formulated as the minimization of the difference between the observed variables and model outputs, and eventually provide the velocity and water stage of the hydrodynamic system. The governing equations are linearized and discretized using an implicit discretization scheme,...

Reachability Analysis for FollowerStopper: Safety Analysis and Experimental Results

Chou, Fang-Chieh
Gibson, Marsalis
Bhadani, Rahul
Bayen, Alexandre M.
Sprinkle, Jonathan M.
2021

Motivated by earlier work and the developer of a new algorithm, the FollowerStopper, this article uses reachability analysis to verify the safety of the FollowerStopper algorithm, which is a controller designed for dampening stop-and-go traffic waves. With more than 1100 miles of driving data collected by our physical platform, we validate our analysis results by comparing it to human driving behaviors. The FollowerStopper controller has been demonstrated to dampen stop-and-go traffic waves at low speed, but previous analysis on its relative safety has been limited to upper and lower...

PDE Traffic Observer Validated on Freeway Data

Yu, Huan
Gan, Qijian
Bayen, Alexandre
Krstic, Miroslav
2021

This article develops a boundary observer for the estimation of congested freeway traffic states based on the Aw-Rascle-Zhang (ARZ) partial differential equations (PDEs) model. Traffic state estimation refers to the 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 model-driven approach in which the estimation of aggregated traffic states in a freeway segment is obtained simply from the boundary measurement of flow and velocity without knowledge...

The I-24 Trajectory Dataset

Nice, Matthew
Lichtle, Nathan
Gumm, Gracie
Roman, Michael
Vinitsky, Eugene
Elmadani, Safwan
Bayen, Alexandre
2021

This dataset was created by recording CAN and GPS data from a single vehicle driving on I-24. The dataset includes values for Time, Velocity, Acceleration, Space Gap, Lateral Distance, Relative Velocity, Longitude GPS, Latitude GPS and more. This empirical dataset is useful for understanding/simulating real vehicle trajectories and vehicle controller performance.

Deploying Traffic Smoothing Cruise Controllers Learned from Trajectory Data

Lichtle, Nathan
Vinitsky, Eugene
Nice, Matthew
Seibold, Benjamin
Work, Dan
Bayen, Alexandre M.
2022

Autonomous vehicle-based traffic smoothing con-trollers are often not transferred to real-world use due to challenges in calibrating many-agent traffic simulators. We show a pipeline to sidestep such calibration issues by collecting trajectory data and learning controllers directly from trajectory data that are then deployed zero-shot onto the highway. We construct a dataset of 772.3 kilometers of recorded drives on the I–24. We then construct a simple simulator using the recorded drives as the lead vehicle in front of a simulated platoon consisting of one autonomous vehicle and five human...

Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data

Lichtle, Nathan
Jang, Kathy
Shah, Adit
Vinitsky, Eugene
Lee, Jonathan W.
Bayen, Alexandre M.
2023

Designing traffic-smoothing cruise controllers that can be deployed onto autonomous vehicles is a key step towards improving traffic flow, reducing congestion, and enhancing fuel efficiency in mixed autonomy traffic. We bypass the common issue of having to carefully fine-tune a large traffic micro-simulator by leveraging real-world trajectory data from the I–24 highway in Tennessee, replayed in a one-lane simulation. Using standard deep reinforcement learning methods, we train energy-reducing wave-smoothing policies. As an input to the agent, we observe the speed and distance of only the...

Integrating Multi-Source Data for Bi-Level Traffic Simulator Calibration: A Literature Review and Highway Case Study

Samaei, Maryam
Ameli, Mostafa
Davis, Jon F.
McQuade, Sean T.
Lee, Jonathan W.
Piccoli, Benedetto
Bayen, Alexandre M.
2024

Traffic simulation serves as a powerful tool for pre-evaluating policies and technologies. In this context, simulation-based Dynamic traffic assignment (DTA) models are capable of capturing traffic dynamics. They are well-known as critical tools in controlling and predicting traffic situations. The reliability of simulation results heavily depends on the calibration process. Most studies in the literature formulate and calibrate simulators based on a single source of collected data or multiple data sets with the same spatiotemporal characteristics. However, in practice, traffic data is...

Validation and Calibration of Energy Models with Real Vehicle Data from Chassis Dynamometer Experiments

Carpio, Joy
Almatrudi, Sulaiman
Khoudari, Nour
Fu, Zhe
Butts, Kenneth
Lee, Jonathan
Seibold, Benjamin
Bayen, Alexandre
2025

Accurate estimation of vehicle fuel consumption typically requires detailed modeling of complex internal powertrain dynamics, often resulting in computationally intensive simulations. However, many transportation applications-such as traffic flow modeling, optimization, and control-require simplified models that are fast, interpretable, and easy to implement, while still maintaining fidelity to physical energy behavior. This work builds upon a recently developed model reduction pipeline that derives physics-like energy models from high-fidelity Autonomie vehicle simulations. These reduced...