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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Energy Impact of Connected and Automated Vehicle Technologies. Final report

The overarching goal of this project is to understand the potential impact of connected and automated vehicles. The goal was achieved through data collection, model development, algorithm designs, simulations, and limited field tests. The main outcomes from this project include: (1) we collected energy consumption and GPS data from 500 vehicles over one year, with a total mileage of 8 million miles; (2) Based on the collected data and other datasets collected at the University of Michigan, we developed a calibrated Ann Arbor model in Polaris (model developed by ANL), and the fuel economy accuracy was found to be around 3.9% by comparison with field collected data; (3) An open-source SUMO model of Ann Arbor was developed; (4) Eco-Routing algorithms in Ann Arbor using the SUMO model shows 6% fuel saving potential; (5) Experiments conducted at the Mcity test facility shows that human drivers roughly follow the Eco-driving suggestions roughly 70% of the time; (6) Based in the Ann Arbor travel patterns, we found that each shared automated vehicle can replace around 4 individually owned vehicles; and (7) Adaptive Traffic Signal Control Algorithm developed through this project has been validated both in simulations and preliminary test results. For connected and automated vehicles, on average the performance is 13% delay reduction, and 10% fuel reduction. While connected and automated vehicles are in their early stage of deployment, the results from this project confirm that there is significant potential for energy saving if the technologies are developed and used properly. The three main technologies studied in this project include eco-routing, shared autonomous rides, and adaptive traffic signal controls. The data collected and model developed through the project can be used to study many other connected and automated vehicle technologies.

02 PETROLEUM↗
DOE OSTI2021

Safe Operations at Roadway Junctions - Design Principles from Automated Guideway Transit

Herein this paper describes a system-level view of a fully automated transit system comprising a fleet of automated vehicles (AVs) in driverless operation, each with an SAE level 4 Automated Driving System, along with its related safety infrastructure and other system equipment. This AV system-level control is compared to the automatic train control system used in automated guideway transit technology, particularly that of communications-based train control (CBTC). Drawing from the safety principles, analysis methods, and risk assessments of CBTC systems, comparable functional subsystem definitions are proposed for AV fleets in driverless operation. With the prospect of multiple AV fleets operating within a single automated mobility district, the criticality of protecting roadway junctions requires an approach like that of automated fixed-guideway transit systems, in which a guideway switch zone "interlocking" at each junction location deconflicts railway traffic, affirming safe passage. The analogous AV protection safety subsystem is defined as fail-safe equipment that monitors roadway intersections and junctions, communicates traffic signal status, perceives and communicates alerts and signals to AV connected vehicles concerning potential unsafe conditions, and performs related primary safety functions. Conclusions are drawn that the AV protection roadway intersection functions must be performed by local roadside equipment dedicated to protecting each roadway intersection and junction. Further, it is concluded that the communications technology connecting the infrastructure with the vehicle to perform this vital, fail-safe protection should meet specific functional and performance criteria.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections

The traffic volume of each movement at signalized intersections can provide valuable information on real-time traffic conditions that enable traffic control systems to dynamically respond to the fluctuated traffic demands. Real-time movement-based traffic volume prediction is challenging due to various nonlinear spatial relationships at different locations/approaches and the complicated underlying temporal dependencies. In this study, a novel deep intersection spatial-temporal network (DISTN) is developed for real-time movement-based traffic volume prediction at signalized intersections, which considers both spatial and temporal features by the convolutional neural network (CNN) and long short-term memory (LSTM), respectively. In addition, the within-day, daily, and weekly periodic trends of traffic volume are also considered in the proposed model. This is the first time that a deep-learning method has been applied for movement-based traffic volume prediction at signalized intersections. In the numerical experiment, the proposed model is evaluated using real-world data and simulation data to demonstrate its effectiveness. The impacts of various structures of traffic networks on the proposed model are also discussed. Results show that the proposed model outperforms some of the state-of-the-art volume prediction methods currently in the literature.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Optimal Eco-Driving Control of Connected and Autonomous Vehicles Through Signalized Intersections

This article focuses on the speed planning problem for connected and automated vehicles (CAVs) communicating to traffic lights. The uncertainty of traffic signal timing for signalized intersections on the road is considered. The eco-driving problem is formulated as a data-driven chance-constrained robust optimization problem. Effective red-light duration (ERD) is defined as a random variable, and describes the feasible passing time through the signalized intersections. Usually, the true probability distribution for ERD is unknown. Consequently, a data-driven approach is adopted to formulate chance constraints based on empirical sample data. This incorporates robustness into the eco-driving control problem with respect to uncertain signal timing. Dynamic programming (DP) is employed to solve the optimization problem. The simulation results demonstrate that the proposed method can generate optimal speed reference trajectories with 40% less vehicle fuel consumption, while maintaining the arrival time at a similar level compared to a modified intelligent driver model (IDM). The proposed control approach significantly improves the controller's robustness in the face of uncertain signal timing, without requiring to know the distribution of the random variable a priori.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Cycle-by-cycle Delay Estimation at Signalized Intersections by using Machine Learning and Simulated Video Detection Data

Accurate estimation of delay is crucial for efficient traffic signal operations. Estimation of delay in the real-time manner using traditional loop detectors requires advanced detectors (in addition to stop-bar detection). In cases when this detection layout is not in place, delay estimates are approximated with a lower accuracy. Video detection is one of the most frequently deployed detection systems at signalized intersections in recent years. In most cases video detection operates in the same way as traditional inductive loops. However, when coupled with computer vision algorithms, video detection systems could be used to retrieve additional information (e.g., vehicular arrivals and departures) that cannot be taken out from the conventional systems (e.g., long stop-bar loop detectors). Although present for several decades, video detection data were not frequently examined for delay estimation purposes. In this study, we proposed a novel delay estimation model which can be developed with only data from stop-bar video detectors. Relevant data were collected from a simulation model of 11 signalized intersections at downtown Chattanooga, TN and processed to create needed inputs for model development. With the use of multigene genetic programming the authors developed a delay model that outperforms accuracy of multi regression model. Furthermore, authors evaluated the developed model by comparison with the other benchmark delay models, such as HCM and approach delay model. It was found that the developed MGGP delay model outperforms benchmark models for a wide range of traffic and signal operation conditions.

Erdagi, Ismet Goksad↗
DOE OSTI2022

Developing Novel Performance Measures for Traffic Congestion Management and Operational Planning Based on Connected Vehicle Data

In this study, the authors present their efforts in exploring a new type of traffic data, referred to as internet-connected vehicle (ICV) data, for traffic congestion management and operational planning. Most currently manufactured vehicles contain onboard GPS and cellular modules, and they constantly connect to automobile manufacturers' clouds via cellular networks and upload their status. Some automobile manufacturers have recently redistributed the nonpersonal part of such data, such as geolocation, to third-party organizations for innovative applications. Compared with the traditional vehicle GPS data, the ICV data contain high-resolution GPS waypoints accompanied with the vehicles' abnormal moving events (e.g., hard braking). The ICV data also have huge potential in congestion management and operational planning. They explore to identify and analyze traffic congestion on both freeways and arterials using the ICV data. The ICV data adopted for this research are redistributed by Wejo Data Service, representing 10%-15% of all moving vehicles in the Dallas-Fort Worth (DFW) area in Texas. Through one case study for a freeway segment and one for an arterial segment, new traffic performance metrics based on the characteristics of ICV data have been presented. The highlights of these efforts are as follows: (I) queue length and propagation at freeway bottlenecks can be directly measured based on where and when most internet-connected vehicles slow down and join the queue; (II) an internet-connected vehicle's actual delay time on arterials can be directly measured according to its slow movement percentage, without assuming the nondelay travel speed; and (III) the ICV data set are also combined with the high-resolution traffic signal events to generate a ground-truth time-space diagram (TSD) on arterials - a common visualization of arterial signal performance for transportation planning and operations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Optimizing on-ramp merging for connected and automated vehicles: A hierarchical approach using deep reinforcement learning and optimal control

On-ramp merging for Connected and Automated Vehicles (CAVs) presents significant challenges in dynamic traffic environments. Traditional methods and recent learning-based approaches often fail to simultaneously address decision-making complexity and execution precision under fluctuating conditions. This study introduces a novel hierarchical framework that combines: (1) a high-level Deep Reinforcement Learning (DRL) module that coordinates merging sequences through Virtual Traffic Signals (VTS) with Yield/Green phases and (2) a low-level optimal controller generating collision-free speed trajectories via pseudospectral convex optimization. A convolutional autoencoder compresses high-dimensional traffic states to enhance responsiveness. Extensive simulations demonstrate a 12.5% improvement in mainline throughput a 28% reduction in emergency braking events, and 31.66% lower fuel consumption compared to baseline methods. Furthermore, the framework’s effectiveness in coordinating CAV merges highlights its potential for real-world deployment. Future work will extend validation to multi-lane scenarios with mixed traffic and large-scale multiple merging points.

Connected and automated vehicles↗
DOE OSTI2023

Estimation of Arrivals on Green at Signalized Intersections Using Stop-Bar Video Detection

Across the world, traffic congestion is increasing with alarming rapidity. Traffic signal control effectiveness, in coordinated networks, is often investigated in relation to the type of vehicle arrivals at the signalized intersections. Recently, several transportation agencies have switched from traditional loop detectors to video detection. When video cameras are accompanied by computer vision, one can extract more information about traffic “dynamics” than by using traditional inductive loop detectors. Collecting arrival times of multiple vehicles after the first arrival at the stop-bar detector might be challenging when using inductive loop detectors (since after the first arrival, detector status is always occupied). However, emerging video detection systems allow tracking of each vehicle’s entrance time in the detection zone, departure time from the detection zone, and the type of vehicle. This information can be used to estimate vehicular arrival and departure times, which then can be fed into machine learning algorithms to estimate arrivals on green (AOG). However, such research ideas have not been documented so far. Thus, this paper presents an estimation model for AOG, which was developed using multigene genetic programming. A robust experimental dataset was collected from a highly calibrated and validated microsimulation model of an 11-intersection corridor in Chattanooga, TN. The results of the model’s performance analysis showed the high accuracy of the training-, testing-, and validation datasets. The practical benefit of this model is that it can be applied to estimate arrival types at intersections where only stop-bar video detection exists.

Engineering↗
DOE OSTI2021

A Cyber-Physical System for Freeway Ramp Meter Signal Control Using Deep Reinforcement Learning in a Connected Environment

Freeway bottlenecks such as on-ramp merging areas account for about 40% of recurring freeway congestion. It is generally agreed that building more roads and adding more lanes to existing infrastructure does not solve the congestion problem, and so dynamic traffic control measures offer a more cost-effective alternative. Ramp meters, traffic signal devices that regulate traffic flow entering freeways, are among the most effective measures to mitigate congestion at on-ramp merging areas on freeways. The confluence of deep reinforcement learning (RL) and connectivity provides a possible solution to advance ramp meter signal control. Deep RL is a group of machine-learning methods that enables an agent learning from the environment to improve its performance. In this study, three deep RL methods-proximal policy optimization (PPO), Ape-X deep Q-network (DQN), and asynchronous advantage actor-critic agents (A3C)-are explored for ramp meter signal control to maximize vehicle speed and traffic throughput, as well as to minimize energy consumption and emissions at freeway on-ramp merging areas in a connected environment. The low computational requirement and scalability of deep RL for deployment make it a powerful optimization tool for time-sensitive applications such as ramp meter signal control. The results of this study show that deep RL methods yield superior performance to both a fixed-time controller and ALINE A, a state-of-the-art feedback controller.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2025

Automating Traffic Microsimulation from SYNCHRO UTDF to SUMO

Modern transportation research relies on seamlessly integrating traffic signal data with robust network representation and simulation tools. This study presents utdf2gmns, an open-source Python tool that automates conversion of the Universal Traffic Data Format, including network representation, signalized intersections, and turning volumes into the General Modeling Network Specification (GMNS) Standard. The resulting GMNS-compliant network can be converted for microsimulation in SUMO. By automatically extracting intersection control parameters and aligning them with GMNS conventions, utdf2gmns minimizes manual preprocessing and data loss. utdf2gmns also integrates with the Sigma-X engine to extract and visualize key traffic control metrics, such as phasing diagrams, turning volumes, volume-tocapacity ratios, and control delays. This streamlined workflow enables efficient scenario testing, accurate model building, and consistent data management. Validated through case studies, utdf2gmns reliably models complex urban corridors, promoting reproducibility and standardization. Documentation is available on GitHub and PyPI, supporting easy integration and community engagement.

Luo, Roy [ORNL] (ORCID:0009000312909983)↗
DOE OSTI2023

Smart Mobility in the Cloud: Enabling Real-Time Situational Awareness and Cyber-Physical Control Through a Digital Twin for Traffic

This article presents the design, implementation, and use cases of the Chattanooga Digital Twin (CTwin) towards the vision for next-generation smart city applications for urban mobility management. CTwin is an end-to-end web-based platform that incorporates various aspects of the decision-making process for optimizing urban transportation systems in Chattanooga, Tennessee, to reduce traffic congestion, incidents, and vehicle fuel consumption. The platform serves as a cyberinfrastructure to collect and integrate multi-domain urban mobility data from various online repositories and Internet of Things (IoT) sensors, covering multiple urban aspects (e.g., traffic, natural hazards, weather, and safety) that are relevant to urban mobility management. The platform enables advanced capabilities for: (a) real-time situational awareness on traffic and infrastructure conditions on highways and urban roads, (b) cyber-physical control for optimizing traffic signal timing, and (c) interactive visual analytics on big urban mobility data and various metrics for traffic prediction and transportation performance evaluation. The platform is designed using a multi-level componentization paradigm and is implemented using modular and adaptive architecture, rendering it as a generalizable and extendable prototype for other urban management applications. We present several use cases to demonstrate CTwin's core capabilities for supporting decision-making in smart urban mobility management.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Real-time control of connected vehicles in signalized corridors using pseudospectral convex optimization

Recent advances in Connected and Automated Vehicle (CAV) technologies have opened up new opportunities to enable safe, efficient, and sustainable transportation systems. However, developing reliable and rapid speed control algorithms in highly dynamic environments with complex inter-vehicle interactions and nonlinear vehicle dynamics is still a daunting task. In this paper, we develop a novel speed control method for CAVs to produce optimal speed profiles that minimize the fuel consumption and avoid idling at signalized intersections. To this end, an optimal control problem is formulated using the information of the upcoming traffic signal to adapt vehicles' speeds to avoid frequent stop-and-go driving patterns. Here, by applying the pseudospectral discretization method and the sequential convex programming method, the computational efficiency is greatly improved, enabling potential real-time on-vehicle applications. In addition, the algorithm is implemented under a model predictive control framework to ensure online control with instant response for collision avoidance and robust vehicle coordination. The proposed algorithm is verified through numerical simulations of three different traffic scenarios. The convergence and accuracy of the proposed approach are demonstrated by comparing with a popular nonlinear solver. Furthermore, the benefit of the proposed method in both traffic mobility and fuel efficiency is validated using the speed profile determined from a traffic following model in a simulation software as the baseline.

42 ENGINEERING↗
DOE OSTI2025

Enhancing traffic safety analysis with digital twin technology: integrating vehicle dynamics and environmental factors into microscopic traffic simulation

Traffic safety is a critical concern in transportation engineering and urban planning. Traditional traffic safety analysis requires trained observers to collect data in the field, which is time-consuming, labor-intensive, and sometimes inaccurate. In recent years, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, have been utilized to study traffic safety. However, microscopic traffic simulation only focuses on traffic-related factors, such as traffic volume, traffic signals, and lane configurations, neglecting vehicle dynamics and environment-related factors like weather and lighting conditions, which can significantly impact traffic safety. In light of this, this paper explores the application of digital twin technology in traffic safety analysis, integrating vehicle simulators, which consider vehicle dynamics and environmental factors, and microscopic traffic simulators, which simulate the operations of traffic flow, for enhanced safety evaluations. Various scenarios, including different weather conditions and visibility levels, are simulated using a digital twin of a road segment in Tuscaloosa, Alabama. The simulations employ Surrogate Safety Measures (SSMs) like Time to Collision (TTC) and Deceleration Rate to Avoid a Crash (DRAC) to assess safety under varying conditions. The results demonstrate that traffic digital twin can identify potential safety issues that traditional microscopic simulation cannot, providing insights for improving traffic control strategies and transportation infrastructure to enhance traffic safety.

Civil engineering↗
DOE OSTI2024

Digital Twinning and Predictive Modeling of Traffic for Safe, Efficient, and Reliable Intersections

Over the last decade, the advances in connected and autonomous vehicles (CAVs) have far surpassed the technological realm of transportation infrastructure. There is a growing need to have a technologically commensurate transportation infrastructure to enable safe and reliable movement of goods and people. The recent creation of Advanced Research Projects Agency - Infrastructure (ARPA-I) through the Infrastructure Investment and Jobs Act by the U.S. Department of Transportation (USDOT) has further amplified the need to revolutionize the transportation infrastructure system in the US. This need is perhaps felt most at traffic intersections, as more than 50 percent of the combined total of fatal and injury crashes occur at or near intersections. The proposed concept of Infrastructure Perception and Control (IPC) is aimed at bridging this technological gap by building a real-time digital twin of traffic by fusing detections from sensors installed at the intersection. This digital twin can then empower a wide variety of applications such as smart traffic signals or infrastructure-to-everything (I2X) communications. Smart signaling can help avoid crashes through early-prediction, while I2X can augment the CAV sensors under uncertain driving conditions and provide connected vehicles (CVs) with traffic information they can use to optimize their travel.

connected vehicles↗
DOE OSTI2025

Next-Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR) - Predictive Data-Driven Vehicle Dynamics and Powertrain Control: from ECU to the Cloud (Final Scientific/Technical Report)

This project developed and demonstrated a predictive, data-driven vehicle control system designed to improve energy efficiency and driving performance. The team created intelligent self-driving car technology that optimizes fuel and electricity use by proactively planning vehicle actions. By combining Level 4 autonomous driving capabilities with vehicle-to-everything (V2X) connectivity, the system enables vehicles to adjust speed and change lanes in response to traffic signals, surrounding vehicles, and road conditions, reducing unnecessary stops and delays. In testing, the system improved vehicle fuel economy by more than 30% and reduced travel time by approximately 10%, compared to a conventional adaptive cruise control baseline. These results demonstrate the technical effectiveness of using predictive, V2X-enabled strategies, such as traffic light timing and surrounding traffic awareness, to inform real-time vehicle powertrain control and driving behavior. Additionally, a supporting cloud platform was developed to provide dispatch and route recommendations as well as to log vehicle data, demonstrating the economic feasibility of this approach at the fleet level. By optimizing dispatching and routing operations, this technology enables electric fleet operators to use their vehicles more efficiently and reduce reliance on diesel backups, lowering both operating costs and energy consumption. Overall, this project’s technology advances the future of clean, energy-efficient transportation, enabling vehicles and fleets to reduce energy waste, cut costs, and lower emissions through intelligent automation and connectivity.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

High Performance Computing Traffic Simulations for Real-Time Traffic Control of Mobility in Chattanooga Region

In 2019, highway congestion wasted over 3 billion gallons of fuel and caused 8.8 billion hours of lost productivity.1 Research has shown that introducing near-real time traffic controls can significantly reduce congestion. Validated and calibrated traffic simulations enable the modeling of transportation systems and the evaluation of different traffic control actions and schemes given a variety of circumstances that represent likely future scenarios. The developed scenarios can inform the deployment of controls in near real-time to improve freight and passenger vehicle congestion and energy use. In this work, we present simulations used to model the traffic in the Chattanooga, Tennessee, metropolitan area. Simulations were constructed and calibrated using a variety of local, data science enhanced, data sources utilizing open source software including the Simulation of Urban Mobility (SUMO) simulator. High-Performance Computing (HPC) provides a scalable platform with enough computing for the high-fidelity simulation of many scenarios and the application of advance data science especially for large-scale systems. Our simulations include microscopic simulations at a corridor level for traffic signal control, and mesoscopic simulations to evaluate regional operational controls and infrastructure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Ecological Adaptive Cruise Control of Plug-In Hybrid Electric Vehicle With Connected Infrastructure and On-Road Experiments

Abstract This paper examines both mathematical formulation and practical implementation of an ecological adaptive cruise controller (ECO-ACC) with connected infrastructure. Human errors are typical sources of accidents in urban driving, which can be remedied by rigorous control theories. Designing an ECO-ACC is, therefore, a classical research problem to improve safety and energy efficiency. We add two main contributions to the literature. First, we propose a mathematical framework of an online ECO-ACC for plug-in hybrid electric vehicle (PHEV). Second, we demonstrate ECO-ACC in a real world, which includes other human drivers and uncertain traffic signals on a 2.6 (km) length of the corridor with eight signalized intersections in Southern California. The demonstration results show, on average, 30.98% of energy efficiency improvement and 8.51% additional travel time.

Automation & Control Systems↗
DOE OSTI2023

Evaluating The Impact Of Connected Vehicle Technology On Heavy Duty Vehicle Emissions

Vehicles with advanced driver assistance systems (ADAS) like adaptive cruise control (ACC) could use connected (V2X) data to improve fuel economy  Eco driving generates an energy efficient speed profile by minimizing acceleration events  SwRI demonstrated a 7% reduction in energy consumption for fully loaded class 8 trucks by using nearby vehicle and traffic signal information  How does eco driving impact vehicle emissions and aftertreatment (AT) systems?

Gankov, Stanislav↗