Digital twin (DT) systems aim to create virtual replicas of physical objects that are updated in real-time with their physical counterparts and evolve alongside the physical assets throughout their lifecycle. DT technology promises to significantly enhance the capabilities of intelligent transportation systems (ITS) through improving safety and mobility. The objectives of this review paper are to: (i) conduct an in-depth review of DT technology applications in the transportation domain–i.e., safety or mobility applications; (ii) develop a reference architecture for a Transportation DT (TDT) system based on a hierarchical concept to guide in developing TDT systems at any scale; (iii) identify research challenges for each component, i.e. the physical space, the communication gateway and the digital space of the TDT system, to realize safe and operationally efficient ITS; and (iv) provide future research perspectives to develop and deploy networkwide transportation DT systems considering the scale and dynamic nature of future connected and automated transportation systems. Thus, this paper focuses on providing a comprehensive understanding of the requirements, reference architecture, challenges, and future research opportunities for a TDT system, starting from individual transportation assets and building up to the entire networkwide TDT.

This paper presents a sensor fusion-based Global Navigation Satellite System (GNSS) spoofing attack detection framework for autonomous vehicles (AVs) that consists of two strategies: (i) comparison between predicted location shift—i.e., distance traveled between two consecutive timestamps—and inertial sensor based location shift in addition to monitoring of vehicle motion states—i.e., standstill/ in motion; and (ii) detection and classification of turns (left or right) along with detection of vehicle motion states. In the first strategy, data from low-cost in-vehicle inertial sensors—i.e., speedometer, accelerometer, and steering angle sensor—are fused and fed to a long short-term memory (LSTM) neural network to predict the distance an AV will travel between two consecutive timestamps. The second strategy combines k-Nearest Neighbors (k-NN) and Dynamic Time Warping (DTW) algorithms to detect a turn and then classify left and right turns using steering angle sensor output. In both strategies, the GNSS-derived speed is compared with speedometer output to improve the effectiveness of the framework presented in this paper. To prove the efficacy of the sensor fusion-based attack detection framework, attack datasets are created for four unique spoofing attack scenarios—turn-by-turn, overshoot, wrong turn, and stop, using the publicly available real-world Honda Research Institute Driving Dataset (HDD). Analyses conducted in this study reveal that the sensor fusion-based detection framework successfully detects all four types of spoofing attacks within the required computational latency threshold.

A resilient positioning, navigation, and timing (PNT) system is a necessity for the robust navigation of autonomous vehicles (AVs). A global navigation satellite system (GNSS) provides satellite-based PNT services. However, a spoofer can tamper the authentic GNSS signal and could transmit wrong position information to an AV. Therefore, an AV must have the capability of real-time detection of spoofing attacks related to PNT receivers, whereby it will help the end-user (the AV in this case) to navigate safely even if the GNSS is compromised. This paper aims to develop a deep reinforcement learning (RL)-based turn-by-turn spoofing attack detection method using low-cost in-vehicle sensor data. We have utilized the Honda Research Institute Driving Dataset to create attack and non-attack datasets to develop a deep RL model and have evaluated the performance of the deep RL-based attack detection model. We find that the accuracy of the deep RL model ranges from 99.99% to 100%, and the recall value is 100%. Furthermore, the precision ranges from 93.44% to 100%, and the f1 score ranges from 96.61% to 100%. Overall, the analyses reveal that the RL model is effective in turn-by-turn spoofing attack detection.

In this study, a digital twin (DT) technology based Adaptive Traffic Signal Control (ATSC) framework is presented for improving signalized intersection performance and user satisfaction. Specifically, real-time vehicle trajectory data, future traffic demand prediction and parallel simulation strategy are considered to develop two DT-based ATSC algorithms, namely DT1 (Digital Twin 1) and DT2 (Digital Twin 2). DT1 uses the delay experienced by each vehicle from all approaches connected to the target intersection, while DT2 uses the delay of each vehicle that occurred in all the approaches connected to the target intersection as well as immediate adjacent intersection. To demonstrate the effectiveness of these algorithms, the DT-based ATSC algorithms are evaluated with varying traffic demands at intersection, and individual user level. Evaluation results show that both DT1 and DT2 performs significantly better compared to the density-based baseline algorithm in terms of control delay reductions ranging from 1% to 52% for low traffic demands. DT1 outperforms baseline algorithm for moderate traffic demands, achieving reduction in control delay ranging from 3% to 19%, while the performance of DT2 declines with increasing demand. For high traffic demands, DT1 achieved control delay reduction ranging from 1% to 45% and DT2 achieved 8% to 36% compared to the baseline algorithm. Moreover, DT1 and DT2 effectively distribute the delay per vehicle among all the vehicles, which approach towards intersection, compared to the baseline ATSC algorithm. DT-based ATSC could help to improve user satisfaction by reducing prolonged delays at signalized intersections, specifically, for moderate and high traffic demands.

Digital twin (DT) systems aim to create virtual replicas of physical objects that are updated in real time with their physical counterparts and evolve alongside the physical assets throughout its lifecycle. Transportation systems are poised to significantly benefit from this new paradigm. In particular, DT technology can augment the capabilities of intelligent transportation systems. However, the development and deployment of networkwide transportation DT systems need to take into consideration the scale and dynamic nature of future connected and automated transportation systems. Motivated by the need of understanding the requirements and challenges involved in developing and implementing such systems, this paper proposes a hierarchical concept for a Transportation DT (TDT) system starting from individual transportation assets and building up to the entire networkwide TDT. A reference architecture is proposed for TDT systems that could be used as a guide in developing TDT systems at any scale within the presented hierarchical concept. In addition, several use cases are presented based upon the reference architecture which illustrate the utility of a TDT system from transportation safety, mobility and environmental applications perspective. This is followed by a review of current studies in the domain of TDT systems. Finally, the critical challenges and promising future research directions in TDT are discussed to overcome existing barriers to realize a safe and operationally efficient connected and automated transportation systems.

Proactive safety systems that anticipate and mitigate traffic risks before incidents occur are increasingly recognized as essential for improving work zone safety. Unlike traditional reactive safety approaches, proactive systems rely on real-time sensing, trajectory prediction, and intelligent infrastructure to detect potential safety hazards. Existing simulation-based and real-world deployment studies often overlook and rarely discuss the practical challenges associated with deploying such proactive systems in operational settings, particularly those involving roadside infrastructure enabled multisensor integration and fusion. This study addresses that gap by presenting deployment insights and technical lessons learned from a real-world implementation of a proactive safety system utilizing multiple sensors mounted on a roadside infrastructure at an active bridge repair work zone along the N-2/US-75 corridor in Nebraska, USA. The deployed system combines lidar, radar, and camera sensors with an edge computing platform to support multimodal object tracking, trajectory fusion, and real-time predictive analytics. Specifically, this study presents key lessons learned across three critical stages of deployment: 1) sensor selection and placement; 2) sensor calibration, system integration, and validation; and 3) sensor fusion. Additionally, we propose a predictive digital twin framework that uses fused trajectory data to predict vehicle path for enabling early conflict detection and real-time warning generation, thereby enabling proactive safety interventions.