The real-world potential of stop-and-go wave smoothing at scale remains largely unquantified. Smoothing freeway waves requires opening a gap large enough for them to dissipate, but that gap is often impractically large. We propose a counterfactual wave-smoothing benchmark that reconstructs a smooth, feasible trajectory from each empirical trajectory by solving a quadratic program with fixed boundary conditions and a maximum-gap constraint, and we use the MOVES model to estimate the resulting emission reduction potential. Applying the framework to nine weeks of weekday peak-period data from the I-24 MOTION testbed, which exhibits rich day-to-day variation in wave dynamics, we find meaningful potential for passenger cars under a 0.1-mile maximum-gap constraint: average CO2 reductions of 9.80% to 14.08% across lanes, with 12.94% to 25.70% in CO, 24.29% to 29.76% in HC, and 29.40% to 36.03% in NOx. Trucks emerge as particularly attractive targets, showing roughly twice the reduction potential of passenger cars.

The main objective of the connected and automated vehicle (CAV) platoon control problem is to regulate CAVs' position while ensuring stability and accounting for vehicle dynamics. Although this problem has been studied in the literature, existing research has some limitations. This paper presents two new theoretical results that address these limitations: (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. To be more precise, taking advantage of the well-known Padé approximation, this paper proposes a constrained CAV platoon controller synthesis that (i) systematically incorporates the lower and upper bounds on the control parameters, and (ii) significantly improves the performance sensitivity to the communication delay. The effectiveness of the presented results is verified through conducting extensive numerical simulations. The proposed controller effectively attenuates the stop-and-go disturbance —a single cycle of deceleration followed by acceleration—amplification throughout the mixed platoon (consisting of CAVs and human-driven vehicles). Modern transportation systems will benefit from the proposed CAV controls in terms of effective disturbance attenuation as it will potentially reduce collisions.

This article presents the comprehensive design, setup, execution, and evaluation of the MegaVanderTest (MVT) experiment conducted by the Congestion Impacts Reduction via CAV-in-the-Loop Lagrangian Energy Smoothing (CIRCLES) Consortium, which aimed to mitigate traffic congestion using partially autonomous vehicles (AVs) (see “Summary”). The experiment involved 100 vehicles on Nashville’s Interstate 24 (I-24) highway, utilizing various control algorithms to smooth stop-and-go traffic waves. The execution of the MVT experiment required a coordinated effort from multiple teams. This article details the meticulous planning process, the coordinated efforts of multiple teams, and the innovative use of a dynamic agent-based simulation framework for traffic evaluation. The contributions of this work include demonstrating and providing a detailed roadmap for large-scale live traffic experiments, illustrating the lessons learned from the MVT experiment, and introducing the other articles in this issue and their complementary relationship in the MVT experiment.

Undetected anomalous incidents on the freeway, such as crashes, can cause increased congestion and secondary crashes. To address this problem, we recently released a real freeway traffic anomalous event detection dataset. However, our evaluation was limited to a static week of traffic data. This demo presents a live anomaly detection dashboard, processing sensor data with a 1-minute delay. It has interactive components to adjust the anomaly detection performance, providing a realistic evaluation of graph anomaly detection methods for long-term analysis and method improvements.

The energy efficiency of today's vehicular mobility relies on the un-integrated combination of i) control via static assets (traffic lights, metering, variable speed limits, etc.); and ii) onboard vehicle automation (adaptive cruise control (ACC), ecodriving, etc.). These two families of control were not co-designed and are not engineered to work in coordination. Recent studies have shown i) limitations of controls, and even ii) negative impacts of ACC [1]. This project focused on the technology development, implementation and prototyping, and validation of Mobile Traffic Control (MTC). MTC can be viewed as an extension of classical traffic control (in which static infrastructure actuates traffic flow). In the MTC paradigm, automated vehicles actuate the entire flow via their behavior, offering enhanced possibilities to optimize the energy footprint of traffic, if designed correctly. We set out to demonstrate for the first time that considerably reduced fuel consumption of all vehicles in traffic can be achieved via distributed control of a small proportion of CAVs. Compared to baseline vehicular technologies, our work offers a significant design departure: control algorithms for the CAVs consider the impact one vehicle can have on overall traffic, improving resulting overall fuel consumption. We focus on using a few vehicles (as traffic controllers via CAV technology) to improve the energy efficiency of traffic flow to further optimize energy efficiency. A live-traffic demonstration in November 2022 featured the deployment of 100 specially-equipped CAVs, constituting an approximate local penetration rate upwards of 2%.

This article proposes a novel approach to traffic signal control that combines phase re-service with reinforcement learning (RL). The RL agent directly determines the duration of the next phase in a pre-defined sequence. Before the RL agent's decision is executed, we use the shock wave theory to estimate queue expansion at the designated movement allowed for re-service and decide if phase re-service is necessary. If necessary, a temporary phase re-service is inserted before the next regular phase. We formulate the RL problem as a semi-Markov decision process (SMDP) and solve it with proximal policy optimization (PPO). We conducted a series of experiments that showed significant improvements thanks to the introduction of phase re-service. Vehicle delays are reduced by up to 29.95% of the average and up to 59.21% of the standard deviation. The number of stops is reduced by 26.05% on average with 45.77% less standard deviation.

This article presents the first field deployment of a multi-agent reinforcement-learning (MARL) based variable speed limit (VSL) control system on the I-24 freeway near Nashville, Tennessee. We describe how we train MARL agents in a traffic simulator and directly deploy the simulation-based policy on a 17-mile stretch of Interstate 24 with 67 VSL controllers. We use invalid action masking and several safety guards to ensure the posted speed limits satisfy the real-world constraints from the traffic management center and the Tennessee Department of Transportation. Since the time of launch of the system through April, 2024, the system has made approximately 10,000,000 decisions on 8,000,000 trips. The analysis of the controller shows that the MARL policy takes control for up to 98% of the time without intervention from safety guards. The time-space diagrams of traffic speed and control commands illustrate how the algorithm behaves during rush hour. Finally, we quantify the domain mismatch between the simulation and real-world data and demonstrate the robustness of the MARL policy to this mismatch.

This study addresses the important issue of energy inefficiency and air pollution resulting from stop-and-go waves on highways by introducing a novel controller called the Attenuative Kerner's Model (AKM). The objective of AKM is to enhance an existing Adaptive Cruise Control (ACC) system to improve vehicle following in stop-and-go waves. It is designed as a hybrid controller that is compatible with a wide range of commercial vehicles equipped with ACC. The article demonstrates the local string stability of the controller. Next, it presents a comparative analysis of AKM against two benchmarks: a human driver and a commercial ACC system, through numerical simulations and physical experiments using a 2022 Cadillac XT5. The findings reveal that AKM substantially outperforms both the human driver and the ACC in controlling low-speed, stop-and-go waves. The results indicate that AKM could act as an additional control layer for existing ACC systems, potentially improving their operational efficiency and reducing pollution emissions, thus contributing to more sustainable highway transportation.

A method to rapidly deploy test software in a multipurpose Cyber-Physical System (CPS) platform is presented in the form of metadata called an app. Logistics in conducting an experiment involving a CPS can be challenging and is often minimally discussed in research results. Generally papers focus mainly on theory, experimental data, and analysis, but CPS research often requires careful validation and deployment efforts. A platform like a self-driving car is also likely to span various experiment use cases, so software must be cleanly uninstalled to avoid conflict in subsequent experiments. Managing a multipurpose yet safety-critical CPS between software changes can add a tremendous amount of setup time that only experts of the particular platform may be able to perform. The concept of experiment apps is presented to reduce experiment setup time in a CPS. The app structure is built to be domain-specific, targeting an open-source self driving vehicle ecosystem developed by Vanderbilt. The effort is done to further democratize the CPS development ecosystem by reducing the burden of software integration.

Designing and validating controllers for connected and automated vehicles to enhance traffic flow presents significant challenges, from the complexity of replicating real-world stop-and-go traffic dynamics in simulation, to the intricacies involved in transitioning from simulation to actual deployment. In this work, we present a full pipeline from data collection to controller deployment. Specifically, we collect 772 km of driving data from the I-24 in Tennessee, and use it to build a one-lane simulator, placing simulated vehicles behind real-world trajectories. Using policy-gradient methods with an asymmetric critic, we improve fuel efficiency by over 10% when simulating congested scenarios. Our comprehensive approach includes reinforcement learning for controller training, software verification, hardware validation and setup, and navigating various sim-to-real challenges. Furthermore, we analyze the controller's behavior and wave-smoothing properties, and deploy it on four Toyota Rav4's in a real-world validation experiment on the I-24. Finally, we release the driving dataset (Nice et al., 2021), the simulator and the trained controller (Lichtl & eacute; et al., 2022), to enable future benchmarking and controller design.

Variable Speed Limit (VSL) is a promising highway traffic management strategy deployed worldwide. Most algorithms deployed operate based on a set of predefined rules which hinders their ability to perform optimally across diverse traffic conditions. Recent studies applying multi-agent reinforcement learning (MARL) techniques to VSL control problems have shown promising results. However, none of the existing MARL frameworks comply with real-world requirements imposed by most traffic agencies across the United States. In this work, we propose MARVEL (Multi-Agent Reinforcement-learning for large-scale Variable spEed Limits), a novel MARL framework for large-scale VSL highway control with real-world deployment capabilities. MARVEL only relies on sensing information available in most real-world settings and learns through a reward structure that incorporates three abstract goals to ensure reasonable behaviors under different traffic conditions. Through parameter sharing among all VSL agents, the proposed framework scales to cover corridors equipped with multiple VSL gantries. The policies are trained in a microscopic traffic simulation environment, focusing on a short freeway stretch with 8 VSL agents spanning 7 miles. For testing, these policies are applied to a more extensive network with 34 VSL agents spanning 17 miles of Interstate 24 (I-24) near Nashville, Tennessee, the United States. MARVEL improves traffic safety by 63.4% compared to the no-control scenario and enhances traffic mobility by 58.6% compared to a state-of-the-practice algorithm that has been deployed on I-24. Finally, we test the response of the policy learned in simulation with real-world data collected from I-24 and illustrate its deployment capability.

Spatial-temporal forecasting is crucial in various domains, including traffic flow prediction for Intelligent Transportation Systems (ITS). Despite the challenges posed by complex spatial-temporal dependencies in traffic networks, Partial Differential Equations (PDEs) have proven effective for capturing traffic dynamics. However, recent trends favor data-driven approaches like Graph Neural Networks (GNNs) for traffic forecasting, often overlooking the principles described by PDEs. In this paper, we propose a Graph Partial Differential Equation Network (GPDE) that integrates PDE principles with GNNs to enhance traffic flow forecasting. Our approach leverages dynamic graph structures based on PDE flux functions, incorporating residual connections and learnable rates for improved model performance. Extensive experiments on realworld traffic datasets demonstrate the superiority of GPDE over existing methods in both short-term and long-term traffic speed prediction tasks.

In this paper we propose an automatic trajectory data reconciliation to correct common errors in vision-based vehicle trajectory data. Given "raw" vehicle detection and tracking information from automatic video processing algorithms, we propose a pipeline including (a) an online data association algorithm to match fragments that describe the same object (vehicle), which is formulated as a min-cost network circulation problem of a graph, and (b) a one-step trajectory rectification procedure formulated as a quadratic program to enhance raw detection data. The pipeline leverages vehicle dynamics and physical constraints to associate tracked objects when they become fragmented, remove measurement noises and outliers and impute missing data due to fragmentations. We assess the capability of the proposed two-step pipeline to reconstruct three benchmarking datasets: (1) a microsimulation dataset that is artificially downgraded to replicate upstream errors, (2) a 15-min NGSIM data that is manually perturbed, and (3) tracking data consists of 3 scenes from collections of video data recorded from 16-17 cameras on a section of the I-24 MOTION system, and compare with the corresponding manually-labeled ground truth vehicle bounding boxes. All of the experiments show that the reconciled trajectories improve the accuracy on all the tested input data for a wide range of measures. Lastly, we show the design of a software architecture that is currently deployed on the full-scale I-24 MOTION system consisting of 276 cameras that covers 4.2 miles of I-24. We demonstrate the scalability of the proposed reconciliation pipeline to process high-volume data on a daily basis.

This article introduces a new virtual trajectory dataset derived from the I-24 MOTION INCEPTION v1.0.0 dataset to address challenges in analyzing large but noisy trajectory datasets. Building on the concept of virtual trajectories, we provide a Python implementation to generate virtual trajectories from large raw datasets that are typically challenging to process due to their size. We demonstrate the practical utility of these trajectories in assessing speed variability and travel times across different lanes within the INCEPTION dataset. The virtual trajectory dataset opens future research on traffic waves and their impact on energy.

Previous controlled experiments on single-lane ring roads have shown that a single partially autonomous vehicle (AV) can effectively mitigate traffic waves. This naturally prompts the question of how these findings can be generalized to field operational, high-density traffic conditions. To address this question, the Congestion Impacts Reduction via CAV-in-the-loop Lagrangian Energy Smoothing (CIRCLES) Consortium conducted MegaVanderTest (MVT), a live traffic control experiment involving 100 vehicles near Nashville, TN, USA. This article is a tutorial for developing analytical and simulation-based tools essential for designing and executing a live traffic control experiment like the MVT. It presents an overview of the proposed roadmap and various procedures used in designing, monitoring, and conducting the MVT, which is the largest mobile traffic control experiment at the time. The design process is aimed at evaluating the impact of the CIRCLES AVs on surrounding traffic. The article discusses the agent-based traffic simulation framework created for this evaluation. A novel methodological framework is introduced to calibrate this microsimulation, aiming to accurately capture traffic dynamics and assess the impact of adding 100 vehicles to existing traffic. The calibration model's effectiveness is verified using data from a six-mile section of Nashville's I-24 highway. The results indicate that the proposed model establishes an effective feedback loop between the optimizer and the simulator, thereby calibrating flow and speed with different spatiotemporal characteristics to minimize the error between simulated and real-world data. Finally, We simulate AVs in multiple scenarios to assess their effect on traffic congestion. This evaluation validates the AV routes, thereby contributing to the execution of a safe and successful live traffic control experiment via AVs.

This paper leverages emerging highway vehicle platooning technology to improve the efficiency and effectiveness of fleeting trains at minimum headways under moving blocks. The research aims to better understand how closely following trains respond to different throttle and brake control algorithms, and, using insights gained from automobile and truck platooning technology, develop improved train control algorithms balancing fuel efficiency and train headway. To do so, a detailed multi-train performance simulator is developed to evaluate following train control algorithms and then adapt highway vehicle platooning control methods to the heavy haul freight rail domain. Five following train control algorithms under two different communication topologies are formulated to more intelligently consider information on the status of the train ahead when specifying throttle or brake settings for each following train. With string stability, following trains attenuate the actions of preceding trains and each successive train requires less aggressive acceleration and braking rates to maintain headways. The simulation results suggest that certain families of control laws are better than others at managing train separation and fuel consumption within train fleets. The results of this research will allow industry practitioners to develop improved locomotive driver advisory and semi-autonomous adaptive train cruise control systems for the operation of fleets of trains under moving blocks, and railroad operators to make more informed decisions regarding the potential fuel efficiency and capacity benefits of these systems.

Variable speed limit (VSL) control has emerged as a promising traffic management strategy for enhancing safety and mobility. In this study, we introduce a multi-agent reinforcement learning framework for implementing a large-scale VSL system to address recurring congestion in transportation corridors. The VSL control problem is modeled as a Markov game, using only data widely available on freeways. By employing parameter sharing among all VSL agents, the proposed algorithm can efficiently scale to cover extensive corridors. The agents are trained using a reward structure that incorporates adaptability, safety, mobility, and penalty terms; enabling agents to learn a coordinated policy that effectively reduces spatial speed variations while minimizing the impact on mobility. Our findings reveal that the proposed algorithm leads to a significant reduction in speed variation, which holds the potential to reduce incidents. Furthermore, the proposed approach performs satisfactorily under varying traffic demand and compliance rates.

The topic of traffic signal control (TSC) for urban intersection networks has been a key research area for many years, but comprehensive comparisons among state-of-the-art methodologies remain limited. This calls for a more exhaustive approach to benchmarking TSC techniques. This study builds upon previous TSC benchmarking work by evaluating the performance of both reinforcement learning (RL) and model-based TSC algorithms across a spectrum of demand levels. We adapt new metrics for in-depth simulation analysis and ground our investigation in a real-world urban arterial scenario. Our results unveil a noteworthy observation: the performance of tested algorithms is inconsistent when evaluated under diverse demand levels and across different metrics. This highlights the importance of implementing multidimensional evaluations in future TSC studies for a more nuanced understanding of their performance.

This work investigates traffic control via controlled connected and automated vehicles (CAVs) using novel controllers derived from the linear-quadratic regulator (LQR) theory. CAV-platoons are modeled as moving bottlenecks impacting the surrounding traffic with their speeds as control inputs. An iterative controller algorithm based on the LQR theory is proposed along with a variant that allows for penalizing abrupt changes in platoon speeds. The controllers use the Lighthill-Whitham-Richards (LWR) model implemented using an extended cell transmission model (CTM) which considers the capacity drop phenomenon for a realistic representation of traffic in congestion. The effectiveness of the proposed traffic control algorithms is tested using a traffic control example and compared with existing proportional-integral (PI)- and model predictive control (MPC)- based controllers from the literature. A case study using the TransModeler traffic microsimulation software is conducted to test the usability of the proposed controller in a realistic setting. It is observed that the proposed controller works well in both settings to mitigate the impact of the jam caused by a fixed bottleneck. The computation time required by the controller is also small making it suitable for real-time control.