The adaptive smoothing method (ASM) is a widely used approach for traffic state reconstruction. This article presents a Python implementation of ASM, featuring end-to-end calibration using real-world ground truth data. The calibration is formulated as a parameterized kernel optimization problem. The model is calibrated using data from a full-state observation testbed, with input from a sparse radar sensor network. The implementation is developed in PyTorch, enabling integration with various deep learning methods. We evaluate the results in terms of speed distribution, spatio-temporal error distribution, and spatial error to provide benchmark metrics for the traffic reconstruction problem. We further demonstrate the usability of the calibrated method across multiple freeways. This article is reproducible and can serve as a benchmark for various freeway operation tasks.

Analyzing stop-and-go waves at the scale of miles and hours of data is an emerging challenge in traffic research. The past 5 years have seen an explosion in the availability of large-scale traffic data containing traffic waves and complex congestion patterns, making existing approaches unsuitable for repeatable and scalable analysis of traffic waves in these data. This paper makes a first step towards addressing this challenge by introducing an automatic and scalable stop-and-go wave identification method capable of capturing wave generation, propagation, dissipation, as well as bifurcation and merging, which have previously been observed only very rarely. Using a concise and simple critical-speed based definition of a stop-and-go wave, the proposed method identifies all wave boundaries that encompass spatio-temporal points where vehicle speed is below a chosen critical speed. The method is built upon a graph representation of the spatio-temporal points associated with stop-and-go waves, specifically wave front (start) points and wave tail (end) points, and approaches the solution as a graph component identification problem. It enables the measurement of wave properties at scale. The method is implemented in Python and demonstrated on a large-scale dataset, I-24 MOTION INCEPTION. Our results show insights on the complexity of traffic waves. Traffic waves can bifurcate and merge at a scale that has never been observed or described before. The clustering analysis of all the identified wave components reveals the different topological structures of traffic waves. We explored that the wave merge or bifurcation points can be explained by spatial features. The gallery of all the identified wave topologies is demonstrated at https://trafficwaves.github.io/.

Transportation studies generate massive amounts of data that are difficult to store, process, query and visualize quickly and easily. Overcoming these challenges are an essential aspect of making the collected data useful to both the original study and other research that could build on the results. We explore the impact of database implementation, specifically IoTDB, on these aspects of data management with respect to transportation on existing datasets.

This paper describes the persistent monitoring with mounted lidar sensors of a transportation corridor over the course of 120 uninterrupted days in Nashville, TN. The data for these 120 days are publicly released as part of publication of this work. The goal of this monitoring is to provide opportunities to design interventions for unsafe intersections, better understand traffic dynamics, and explore the potential for closed-loop control of vehicle signals and walk signs. Lidar units are installed with some overlapping, and some non-overlapping fields of view, offering coverage of eight locations (five intersections and three mid-block areas) across a span of two miles. Continuous object trajectories for all modes of travel are generated from edge processing of raw lidar point cloud data. Unlike camera-based sensing, lidar is natively privacy preserving. This provides an opportunity for improved reception in communities. The paper provides a full description of the corridor, the types of classifications performed by each lidar installation, and the refresh rates and data types recorded. In addition, sample analyses are given to demonstrate the richness of the data. Sample results include hot spots for near-miss events between classified objects, daily turning count statistical analysis, and out of crosswalk pedestrian activity.

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment leveraged a heterogeneous fleet of 100 longitudinally-controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this paper. The MegaController is a hierarchical control architecture, which consists of two main layers. The upper layer is called Speed Planner, and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock on-board sensors. The Speed Planner ingests live data feeds provided by third parties, as well as data from our own control vehicles, and uses both to perform the speed assignment. The architecture of the speed planner allows for modular use of standard control techniques, such as optimal control, model predictive control, kernel methods and others, including Deep RL, model predictive control and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers, or only some. Control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars, and electronic selection of ACC set points in others. The proposed architecture allows for the combination of all possible settings proposed above. Most configurations were tested throughout the ramp up to the MegaVandertest.

This study investigates the assisted lane change functionality of five different vehicles equipped with advanced driver assistance systems (ADAS). The goal is to examine novel, under-researched features of commercially available ADAS technologies. The experimental campaign, conducted in the I-24 highway near Nashville, TN, US, collected data on the kinematics and safety margins of assisted lane changes in real-world conditions. The results show that the kinematics of assisted lane changes are consistent for each system, with four out of five vehicles using slower speeds and decelerations than human drivers. However, one system consistently performed more assertive lane changes, completing the maneuver in around 5 seconds. Regarding safety margins, only three vehicles are investigated. Those operated in the US are not restricted by relevant UN regulations, and their designs were found not to adhere to these regulatory requirements. A simulation method used to classify the challenge level for the vehicle receiving the lane change, showing that these systems can force trailing vehicles to decelerate to keep a safe gap. One assisted system was found to have performed a maneuver that posed a hard challenge level for the other vehicle, raising concerns about the safety of these systems in real-world operation. All three vehicles were found to carry out lane changes that induced decelerations to the vehicle in the target lane. Those decelerations could affect traffic flow, inducing traffic shockwaves.

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. We explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

This review offers a comprehensive overview of current traffic modeling, estimation, and control methods, along with resulting field experiments. It highlights key developments and future directions in leveraging technological advancements to improve traffic management and safety. The focus is on macroscopic, microscopic, and micro-macro models, as well as state-of-the-art control techniques and estimation methods for deploying vehicles in traffic field experiments.

Stop-and-go waves are a fundamental phenomenon in freeway traffic flow, contributing to inefficiencies, crashes, and emissions. Recent advancements in high-fidelity sensor technologies have improved the ability to capture detailed traffic dynamics, yet such systems remain scarce and costly. In contrast, conventional traffic sensors are widely deployed but suffer from relatively coarse-grain data resolution, potentially impeding accurate analysis of stop-and-go waves. This article explores whether generative AI models can enhance the resolution of conventional traffic sensor to approximate the quality of high-fidelity observations. We present a novel approach using a conditional diffusion denoising model, designed to reconstruct finegrained traffic speed field from radar-based conventional sensors via iterative refinement. We introduce a new dataset, WaveX (Ji et al., 2025a), comprising 132 hours of data from both low and high-fidelity sensor systems, totaling over 2 million vehicle miles traveled. Our approach leverages this dataset to formulate the traffic state refinement problem as a spatio-temporal super-resolution task. We demonstrate that our model can effectively reproduce the patterns of stop-and-go waves, achieving high accuracy in capturing these critical traffic dynamics. Our results show promising advancements in traffic state refinement, offering a cost-effective way to leverage existing low spatio-temporal resolution sensor networks for improved traffic analysis and management. We also open-source our dataset, trained model and code to enable further research and applications.

This article presents the first field deployment of a multiagent reinforcement learning (MARL)-based variable- speed-limit (VSL) control system on Interstate 24 (I-24) near Nashville, TN, USA. We design and demonstrate a full pipeline from training MARL agents in a traffic simulator to a field deployment on a 17-mi segment of I-24 encompassing 67 VSL controllers. The system was launched on 8 March 2024 and has made approximately 35 million decisions on 28 million trips in six months of operation. We apply an invalid action masking mechanism and several safety guards to ensure real-world constraints. The MARL-based implementation operates up to 98% of the time, with the safety guards overriding the MARL decisions for the remaining time. We evaluate the performance of the MARL-based algorithm in comparison to a previously deployed non-RL VSL benchmark algorithm on I-24. The results show that the MARL-based VSL control system achieves a superior performance.

This work presents testing of a Control Barrier Function (CBF) supervised Automated Vehicle (AV) in live freeway traffic. The CBF is designed and implemented using a common dynamical model for AV longitudinal control and a time-gap based safety property combined with CAN bus injection software/hardware. 1.17 hours of car-following data is collected from driving in congested freeway traffic. We analyze the extent to which the CBF controlled AV satisfied three properties: 1) forward-invariance of the safety property, 2) recovery of the safety property, and 3) collision avoidance. Our main findings are as follows. Forward-invariance was not strictly satisfied across all states. When trajectories begin satisfying the safety property it was violated by a maximum of 3.4[m], and 90% of violations were less than 2[m]. Recovery was also not strictly satisfied across all states. For trajectories which begin outside of the safe set due to merge-in events, if the violation to the safety property was by more than 5[m] the AV was recovering back to the safe set in more than 98% of the time. Finally, the minimum spacing-gap was 11.7[m]. Across the tests the AV remained far from any collisions. We additionally analyze errors between control inputs and achieved outputs and hypothesize that unaccounted for modeling errors may lead to under-braking compared to what the CBF logic specifies, which may contribute to the observed property violations.

This article presents experimental evidence of the ability of a single automated vehicle acting as a controller to effectively dissipate stop-and-go waves in real traffic. The automated vehicle succeeded in stabilizing the speed profile by reducing oscillations in time and speed variations between vehicles during rush hour on I-24 in the Nashville area. We detail the control design, deployment and results obtained in this experiment, conducted as part of the CIRCLES consortium’s “MegaVanderTest” 2022, which involved a total of 100 automated vehicles.

The goal of this paper is to estimate whether a human or Adaptive Cruise Control (ACC) is managing a vehicle's speed control, based on observations by external sensors. The driving characteristics of individual vehicles-whether human-driven or ACC-controlled-play a crucial role in shaping overall traffic flow. To enable advanced traffic control strategies tailored to specific vehicle behaviors, this paper introduces a time-series deep learning classifier that leverages multiple models, including One-Dimensional Convolutional Neural Networks (1D-CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Temporal Fusion Transformers (TFT). These models distinguish between human-driven and ACC-controlled trajectories using signals such as the ego vehicle's velocity, the distance to the leading vehicle, and derived features. Unlike previous studies relying solely on simulation data, our classifier uses large-scale, real-world datasets from field experiments and daily commute data. By utilizing low-latency, low-anomaly signals decoded from Controller Area Network (CAN) bus messages, the model achieves a high accuracy of 98.85% in classifying human-driven and ACC-controlled vehicles within three seconds, outperforming existing methods that require longer trajectory data or pre-calibrated models. The approach is scalable and can be integrated with large-scale traffic trajectory datasets, such as those from the I-24 Motion project, enabling more precise estimation of ACC penetration, fuel consumption, and emissions.

The key objective of the connected and automated vehicle (CAV) platoon control problem is to regulate CAVs' position while ensuring stability and accounting for vehicle dynamics. The unconstrained version of this problem has thoroughly been investigated in the literature. We elaborate on the constrained version of this problem to theoretically mitigate the two shortcomings of the unconstrained counterpart: (i) the synthesis of unrealistic high-gain control parameters due to the lack of a systematic way to incorporate the lower and upper bounds on the control parameters, and (ii) the performance sensitivity to the communication delay due to inaccurate Taylor series approximation. The former is mitigated via a systematic parameterization of the control gains based on the Hurwitz stability criterion. The latter is mitigated by taking advantage of the well-known Pade approximation. The usefulness of the proposed theoretical results is assessed by performing numerous numerical simulations. Furthermore, a thorough comparative analysis is empirically conducted between the constrained and unconstrained versions of the CAV platoon control problem with application to the mixed vehicular platoon. Modern transportation systems will benefit from the proposed CAV controls by effectively attenuating the stop-and-go disturbance-a single cycle of deceleration followed by acceleration-amplification throughout the mixed vehicular platoon as it will potentially reduce collisions.

This work introduces a multi-camera tracking dataset consisting of 234 hours of video data recorded concurrently from 234 overlapping HD cameras covering a 4.2 mile stretch of 8-10 lane interstate highway near Nashville, TN. Video is recorded in cooperation with Tennessee State Department of Transportation and its policies. The video is recorded during a period of high traffic density with 500+ objects typically visible within the scene and typical object longevities of 3-15 minutes. GPS trajectories from 270 vehicle passes through the scene are manually corrected in the video data to provide a set of ground-truth trajectories for recall-oriented tracking metrics, and object detections are provided for each camera in the scene (159 million total before cross-camera fusion). Initial benchmarking of tracking-by-detection algorithms is performed against the GPS trajectories, and a best HOTA of only 9.5% is obtained (best recall 75.9% at IOU 0.1, 47.9 average IDs per ground truth object), indicating the benchmarked trackers do not perform sufficiently well at the long temporal and spatial durations required for traffic scene understanding. Video data, scene information, and vehicle trajectories are made publicly available at i24motion.org.

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. We explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

Early and accurate detection of anomalous events on the freeway, such as accidents, can improve emergency response and clearance. However, existing delays and errors in event identification and reporting make it a difficult problem to solve. Current large-scale freeway traffic datasets are not designed for anomaly detection and ignore these challenges. In this paper, we introduce the first large-scale lane-level freeway traffic dataset for anomaly detection. Our dataset consists of a month of weekday radar detection sensor data collected in 4 lanes along an 18-mile stretch of Interstate 24 heading toward Nashville, TN, comprising over 3.7 million sensor measurements. We also collect official crash reports from the Nashville Traffic Management Center and manually label all other potential anomalies in the dataset. To show the potential for our dataset to be used in future machine learning and traffic research, we benchmark numerous deep learning anomaly detection models on our dataset. We find that unsupervised graph neural network autoencoders are a promising solution for this problem and that ignoring spatial relationships leads to decreased performance. We demonstrate that our methods can reduce reporting delays by over 10 minutes on average while detecting 75 Our dataset and all preprocessing code needed to get started are publicly released at https://vu.edu/ft-aed/ to facilitate future research.

This paper introduces a novel approach that seeks a middle ground for traffic control in multi-lane congestion, where prevailing traffic speeds are too fast, and speed recommendations designed to dampen traffic waves are too slow. Advanced controllers that modify the speed of an automated car for wave-dampening, eco-driving, or other goals, typically are designed with forward collision safety in mind. Our approach goes further, by considering how dangerous it can be for a controller to drive so slowly relative to prevailing traffic that it creates a significant issue for safety and comfort. This paper explores open-road scenarios where large gaps between prevailing speeds and desired speeds can exist, specifically when infrastructure-based variable speed limit systems are not strictly followed at all times by other drivers. Our designed, implemented, and deployed algorithm is able to follow variable speed limits when others also follow it, avoid collisions with vehicles ahead, and adapt to prevailing traffic when other motorists are traveling well above the posted speeds. The key is to reject unsafe speed recommendations from infrastructure-based traffic smoothing systems, based on real-time local traffic conditions observed by the vehicle under control. This solution is implemented and deployed on two control vehicles in heavy multi-lane highway congestion. The results include analysis from system design, and field tests that validate the system's performance using an existing Variable Speed Limit system as the external source for speed recommendations, and the on-board sensors of a stock Toyota Rav4 for inputs that estimate the prevailing speed of traffic around the vehicle under control.

The Interstate-24 MObility Technology Interstate Observation Network (I-24 MOTION) is a new instrument for traffic science located near Nashville, Tennessee. I-24 MOTION consists of 276 pole-mounted high-resolution traffic cameras that provide seamless coverage of approximately 4.2 miles I-24, a 4-5 lane (each direction) freeway with frequently observed congestion. The cameras are connected via fiber optic network to a compute facility where vehicle trajectories are extracted from the video imagery using computer vision techniques. Approximately 230 million vehicle miles of travel occur within I-24 MOTION annually. The main output of the instrument are vehicle trajectory datasets that contain the position of each vehicle on the freeway, as well as other supplementary information vehicle dimensions and class. This article describes the design and creation of the instrument, and provides the first publicly available datasets generated from the instrument. The datasets published with this article contains at least 4 hours of vehicle trajectory data for each of 10 days. As the system continues to mature, all trajectory data will be made publicly available at i24motion.org/data.