Accurate trajectory prediction is essential for the safety and efficiency of autonomous driving. Traditional models often struggle with real-time processing, capturing non-linearity and uncertainty in traffic environments, efficiency in dense traffic, and modeling temporal dynamics of interactions. We introduce NEST (Neuromodulated Small-world Hypergraph Trajectory Prediction), a novel framework that integrates Small-world Networks and hypergraphs for superior interaction modeling and prediction accuracy. This integration enables the capture of both local and extended vehicle interactions, while the Neuromodulator component adapts dynamically to changing traffic conditions. We validate the NEST model on several real-world datasets, including nuScenes, MoCAD, and HighD. The results consistently demonstrate that NEST outperforms existing methods in various traffic scenarios, showcasing its exceptional generalization capability, efficiency, and temporal foresight. Our comprehensive evaluation illustrates that NEST significantly improves the reliability and operational efficiency of autonomous driving systems, making it a robust solution for trajectory prediction in complex traffic environments.

Spatially partitioning heterogeneous traffic networks into multiple subnetworks is crucial for practical tasks, such as distributed signal control and model parallel processing. Existing partitioning methods that account for traffic characteristics over a certain period typically require calculating similarities between the time series of all sensors. Due to the quadratic increase in complexity with network size, these methods are inefficient for large-scale networks and extended time periods. Additionally, calculating similarities requires complete data, making such methods highly sensitive to missing data and lacking robustness. To address these issues, this article proposes a four-step fundamental-diagram-informed traffic network partitioning method. First, spatially adjacent sensors are clustered into subclusters. Next, the S3 speed-occupancy function is used to fit the aggregated data of each subcluster to extract fundamental diagram information. Then, this information is used to perform secondary clustering to form clusters. Finally, the cluster boundaries are fine-tuned to produce subnetworks with smooth boundaries. The proposed method calculates the parameter similarity between subclusters instead of the time series similarity between all sensors. This reduces computational costs and effectively handles data missing. A case study using real-world data verifies the effectiveness of the proposed method and its stability in the presence of missing data. Compared to spectral clustering, the total within-cluster variance and NcutSilhouette metric decrease by 5.7% and 17.8%, respectively. The proposed method enhances distributed or parallel tasks on traffic networks by providing stable and meaningful partitioning results. This method is beneficial for the analysis and effective management of complex heterogeneous traffic networks.

Vehicular digital forensics is crucial for the security of intelligent transportation systems. However, current vehicular forensics approaches suffer from a single source of evidence, weak verification of evidence authenticity, and inadequate integrity assurance for cloud-stored data during vehicle offline periods. To address these issues, we propose a group-based vehicular digital forensics and proxy-assisted integrity auditing (GVDF-PIA) scheme with the following three techniques. First, we design a multi-perspective digital forensics and efficient storage scheme based on vehicular groups, enabling multi-perspective forensics and improving storage efficiency. Second, we construct a proxy-assisted decentralized integrity auditing mechanism that effectively verifies cloud-stored data even when vehicles are offline. Third, we introduce a time-hash-chain-based method for temporal verification of digital evidence, ensuring the authenticity and chronological integrity of vehicular data, thus making it legally enforceable. Security analysis and experimental results based on general evaluation standards demonstrate that the proposed scheme significantly enhances forensic reliability, while also improving storage efficiency, validating its practical applicability in intelligent transportation systems.

Real-time traffic prediction models play a pivotal role in smart mobility systems and have been widely used in route guidance, emerging mobility services, and advanced traffic management systems. With the availability of massive traffic data, neural network-based deep learning methods, especially graph convolutional networks (GCNs) have demonstrated outstanding performance in mining spatio-temporal information and achieving high prediction accuracy. Recent studies reveal the vulnerability of GCN under adversarial attacks, while there is a lack of studies to understand the vulnerability issues of the GCN-based traffic prediction models. Given this, this article proposes a new task—diffusion attack, to study the robustness of GCN-based traffic prediction models. The diffusion attack aims to select and simulate attacks on a small set of nodes to degrade the performance of the traffic prediction models, and it can be used to examine vulnerabilities of the traffic prediction models. We propose a novel attack algorithm, which consists of two major components: 1) approximating the gradient of the black-box prediction model with simultaneous perturbation stochastic approximation (SPSA) and 2) adapting the knapsack greedy algorithm to select the attack nodes. The proposed algorithm is examined with three GCN-based traffic prediction models: 1) ST-GCN; 2) T-GCN; and 3) A3T-GCN on four cities. The proposed algorithm demonstrates high efficiency in adversarial attack tasks under various scenarios, and it can still generate adversarial samples under the drop regularization, such as DROP OUT, DROP NODE, and DROP EDGE. The research outcomes could help to improve the robustness of the GCN-based traffic prediction models and better protect the smart mobility systems.

The rapid development of electric vehicles raises a higher requirement for charging station operation and management. Therefore, this work proposes a data-driven method aimed at enhancing the economical operation of charging stations. Considering the privacy of charging data and the influence of traffic flow, this data-driven method simulates the spatiotemporal charging demand based on the predicted traffic flow. To obtain precise prediction, this work develops an efficient gated-attention spatiotemporal convolutional network (GSTCN) to explore the long-term spatial and temporal dependence of traffic flow. GSTCN is constructed by two main components: a spatial gated attention (SGA) unit and a temporal gated (T-Gated) attention layer. The spatial pattern of traffic flow is unearthed by the well-designed SGA unit, while the temporal correlation between different time steps is captured through the proposed T-Gated attention layer. Then, an energy storage system (ESS) is employed to improve the effective management of charging stations. Numerical results demonstrate the efficiency of GSTCN in charging station management. GSTCN can produce more accurate prediction data, which leads to a more economical operation of the energy storage system compared with the benchmarks.

This paper presents a novel and practical methodology for freight tonnage estimation by leveraging two complementary datasets: Telemetric Traffic Monitoring Sites (TTMS) data and Weigh-In-Motion (WIM) systems. To estimate freight tonnage statewide and potentially nationwide with limited truck weigh-in-motion stations, we have proposed a multi-objective location-allocation model that associated TTMSs with WIM stations based on similar attributes. Additionally, we have developed a fuzzy k-prototype clustering-based non-dominated sorting genetic algorithm - simulated annealing algorithm (FKC-NSGASA) to solve the multi-objective location-allocation problem, enabling accurate estimation of truck volumes. To address the over-counting problem, we introduced a truck volume elimination method. Finally, we have aggregated annual truck tonnage using the truck volume data and the average tonnage of WIM stations. The proposed methodologies are validated using WIM data from 2012 and 2017 in Florida. The results demonstrate that our approach achieves higher estimation accuracy, showcasing its potential for accurately estimating statewide freight tonnage. Furthermore, the developed estimation framework and algorithm offer an effective and computationally efficient method for statewide freight traffic evaluation.

Traffic speed is one of the critical indicators reflecting traffic status of roadway networks. The abnormality and sudden changes of traffic speed indicate the occurrence of traffic congestions, accidents, and events. Traffic control and management systems usually take the spatiotemporal variations of traffic speed as the critical evidence to dynamically adjust the traffic signal timing plan, broadcast traffic accidents, and form a management strategy. Meanwhile, transport is multimodal in most cities, including vehicles, pedestrians, and bicyclists. Traffic states of different traffic modes are usually used simultaneously as the significant input of advanced traffic control systems, e.g., multiobjective traffic signal control system, connected vehicles, and autonomous driving. In previous studies, Wi-Fi and Bluetooth passive sensing technology was demonstrated as an effective method for obtaining traffic speed data. However, there are some challenges that greatly affect the accuracy the estimated traffic speed, e.g., traffic mode uncertainty and the errors caused by sensors’ detection range. Thus, this study develops a real-time method for estimating the multimodal traffic speed of road networks covered by Wi-Fi and Bluetooth passive sensors. To address the two identified challenges, an algorithm is developed to correct the biased estimated traffic speed based on the received signal strength indicator of Wi-Fi and Bluetooth signals, and a novel semisupervised Possibilistic Fuzzy$C$-Means clustering algorithm is proposed for identifying traffic modes of Wi-Fi and Bluetooth device owners. The performance of the proposed algorithms is evaluated by comparing with the selected baseline algorithms. The experimental results indicate the superiority of the proposed algorithm. The proposed method of this study can provide accurate and real-time multimodal traffic speed information for supporting traffic control and management, and, thus, improving the operational performance of the whole road network.

Traffic forecasting has attracted considerable attention due to its importance in proactive urban traffic control and management. Scholars and engineers have exerted considerable efforts in improving the performance of traffic forecasting algorithms in terms of accuracy, reliability, and efficiency. Spatial feature representation of traffic flow is a core component that greatly influences traffic forecasting performance. In previous studies, several spatial attributes of traffic flow are ignored due to the following issues: a) traffic flow propagation does not comply with the road network, b) the spatial pattern of traffic flow varies over time, and c) single adjacent matrix cannot handle the complex and hierarchical urban traffic flow. To address the abovementioned issues, this study proposes a novel traffic forecasting algorithm called traffic transformer, which achieves great success in natural language processing. The multihead attention mechanism and stacking layers enable the transformer to learn dynamic and hierarchical features in sequential data. Two components, namely, global encoder and global–local decoder, are proposed to extract and fuse the spatial patterns globally and locally. Experimental results indicate that the proposed traffic transformer outperforms state-of-the-art methods. The learned dynamic and hierarchical features of traffic flow can help achieve a better understanding of spatial dependency of traffic flow for effective and efficient traffic control and management strategies.

With the significant increase of e-commerce, freight transportation demand has surged significantly over the past decade. Most of the demand has been served by trucks in the United States. One major problem commonly identified across the country is the worsening truck parking availability because the increase of truck parking facilities has lagged behind the growth of trucking activities. The lack of parking spaces and real-time parking availability information greatly exacerbate the uncertainty of trips, and often results in illegal and potentially dangerous parking or overtime driving. This paper elaborates on pilot research on improving truck parking facilities cooperated with the Washington State Department of Transportation (WSDOT), building and testing the advanced Truck Parking Information and Management System (TPIMS) with the real-time user visualization and prediction function empowered by artificial intelligence. Furthermore, by analyzing the activities of truck drivers, the researchers aggregated the regularity of truck parking patterns by a customized sequential similarity methodology. A Truck Parking Occupancy Prediction (TPOP) neural network for time-variant occupancy prediction by deep learning and attributes embedding is proposed and integrated into the TPIMS. The TPOP achieves 5.82%, 5.07%, 4.84%, and 4.19% mean average percentage error (MAPE) for 16, 8, 4, and 2 minutes ahead of occupancy prediction respectively, significantly outperforms other state-of-the-art methods. Clearly, the proposed solutions can benefit both the truck drivers and government agencies by a more efficient and smart TPIMS.

Real-time public transit ridership flow and origin-destination (O-D) information is essential for improving transit service quality and optimizing transit networks in smart cities. The effectiveness and accuracy of the traditional survey-based methods and smart card data-driven methods for O-D information inference have multiple disadvantages in terms of biased results, high latency, insufficient sample size, and the high cost of time and energy. By considering the ubiquity of smart mobile devices in the world, monitoring public transit ridership flow can be accomplished by passively sensing Wi-Fi and Bluetooth (BT) mobile devices of passengers. This study proposed a system for monitoring real-time public transit passenger ridership flow and O-D information based on customized Wi-Fi and BT sensing device. By combining the consideration of the assumed overlapping feature spaces of passenger and nonpassenger media access control address data, a three-step data-driven algorithm framework for estimating transit ridership flow and O-D information is proposed. The observed ridership flow is used as the ground truth for evaluating the performance of the proposed algorithm. According to the evaluation results, the proposed algorithm outperformed all selected baseline models and the existing filtering methods. The findings of this study can help to provide real time and precise transit ridership flow and O-D information for supporting transit vehicle management and the quality of service enhancement.

Cloud computing has been a main-stream computing service for years. Recently, with the rapid development in urbanization, massive video surveillance data are produced at an unprecedented speed. A traditional solution to deal with the big data would require a large amount of computing and storage resources. With the advances in Internet of things (IoT), artificial intelligence, and communication technologies, edge computing offers a new solution to the problem by processing all or part of the data locally at the edge of a surveillance system. In this study, we investigate the feasibility of using edge computing for smart parking surveillance tasks, specifically, parking occupancy detection using the real-time video feed. The system processing pipeline is carefully designed with the consideration of flexibility, online surveillance, data transmission, detection accuracy, and system reliability. It enables artificial intelligence at the edge by implementing an enhanced single shot multibox detector (SSD). A few more algorithms are developed either locally at the edge of the system or on the centralized data server targeting optimal system efficiency and accuracy. Thorough field tests were conducted in the Angle Lake parking garage for three months. The experimental results are promising that the final detection method achieves over 95% accuracy in real-world scenarios with high efficiency and reliability. The proposed smart parking surveillance system is a critical component of smart cities and can be a solid foundation for future applications in intelligent transportation systems.

Variable speed limits (VSL) is a major Intelligent Transportation System (ITS) technology for controlling freeway mainline traffic, which have been increasingly used to improve traffic safety and operations efficiency of freeway traffic management. The primary objective of this study is to evaluate the safety impacts of the VSL system implemented on Interstate 5 in Seattle, United States since 2010. A Full Bayesian (FB) before-after analysis was conducted based on 9,787 crashes that occurred in a 72-month study period. The analysis was conducted for all crashes, crash severity levels, crash types, and crash causes. The FB before-after results implied that the total crash count was reduced by 32.23% with a standard deviation of 3.58% after the VSL system was applied on the freeway. The count of crashes with no injury decreased more than crashes with severe injury and possible injury. The effect of rear-end crash reduction was the most beneficial among all crash types, while the effect on sideswipe crash reduction was the least. The study also compared the traffic speed features in the before and after periods to fully evaluate the impacts of the VSL system on traffic operations. The result indicated that, with VSL control, the difference in speed was reduced by the VSL system. The results of this study are particularly valuable for policy and control strategy development, and cost-benefit evaluation associated with VSL system implementations.