Traffic forecasting is essential for intelligent transportation systems. Accurate forecasting relies on continuous observations collected by traffic sensors. However, due to high deployment and maintenance costs, not all regions are equipped with such sensors. This paper aims to forecast for regions without traffic sensors, where the lack of historical traffic observations challenges the generalisability of existing models. We propose a model named **GenCast**, the core idea of which is to exploit external knowledge to compensate for the missing observations and to enhance generalisation. We integrate physics-informed neural networks into GenCast, enabling physical principles to regularise the learning process. We introduce an external signal learning module to explore correlations between traffic states and external signals such as weather conditions, further improving model generalisability. Additionally, we design a spatial grouping module to filter localised features that hinder model generalisability. Extensive experiments show that GenCast consistently reduces forecasting errors on multiple real-world datasets.

The integration of edge and cloud computing combines low latency with high computational power, addressing the constraints of edge resources and high access latency inherent in cloud environments. This is essential for deploying Internet of Things (IoT) applications, which are mainly developed by Containers within these heterogeneous environments. However, the open, multi-user nature of edge computing, compounded by a lack of standardized practices, introduces substantial security challenges with severe economic implications. In response, we propose SecConEC, an economically driven framework designed to secure the deployment and execution of containerized IoT applications. We conducted systematic threat modeling using the STRIDE framework, explicitly incorporating quantitative economic risk assessment to identify and prioritize security threats based on their potential economic impacts. We particularly focus on tampering and resource hijacking threats. SecConEC implements robust yet lightweight mitigation and detection mechanisms informed by the MITRE ATT&CK framework through a Security Information and Event Management (SIEM) system. Also, SecConEC introduces a dynamic, security-aware scheduling mechanism that balances performance and security considerations, proactively mitigating economic risks associated with potential security threats. Extensive performance evaluation shows that SecConEC significantly mitigates prioritized threats, effectively securing IoT application deployment and execution in edge-cloud environments, while maintaining low service latency with a minimal performance overhead of 1.7%.

The rapid growth of online retail and e-commerce has made effective and efficient Vehicle Routing Problem (VRP) solutions essential. To meet rising demand, companies are adding more depots, which changes the VRP problem to a complex optimization task of Multi-Depot VRP (MDVRP) where the routing decisions of vehicles from multiple depots are highly interdependent. The complexities render traditional VRP methods suboptimal and non-scalable for the MDVRP. In this paper, we propose a novel approach to solve MDVRP addressing these interdependencies, hence achieving more effective results. The key idea is, the MDVRP can be broken down into two core spatial tasks: assigning customers to depots and optimizing the sequence of customer visits. We adopt task-decoupling approach and propose a two-stage framework that is scalable: (i) an interdependent partitioning module that embeds spatial and tour context directly into the representation space to globally match customers to depots and assign them to tours; and (ii) an independent routing module that determines the optimal visit sequence within each tour. Extensive experiments on both synthetic and real-world datasets demonstrate that our method outperforms all baselines across varying problem sizes, including the adaptations of learning-based solutions for single-depot VRP. Its adaptability and performance make it a practical and readily deployable solution for real-world logistics challenges.

Traffic forecasting is crucial for transportation systems optimisation. Current models minimise the mean forecasting errors, often favouring periodic events prevalent in the training data, while overlooking critical aperiodic ones like traffic incidents. To address this, we propose DualCast, a dual-branch framework that disentangles traffic signals into intrinsic spatial-temporal patterns and external environmental contexts, including aperiodic events. DualCast also employs a cross-time attention mechanism to capture high-order spatial-temporal relationships from both periodic and aperiodic patterns. DualCast is versatile. We integrate it with recent traffic forecasting models, consistently reducing their forecasting errors by up to 9.6% on multiple real datasets.

Graph Convolution Networks (GCNs) have shown superiority in many applications entailing classification and prediction tasks. Vanilla GCNs are based on updating a node embedding locally with directly connected neighbors and not considering distant nodes. To address this issue, a straightforward solution is to increase the number of GCN layers, letting messages (i.e., information) from distant nodes to be propagated to a target node after a few iterations. Such an approach, however, suffers from resembling of node representations. In this paper, we propose a model named NACFormer that incorporates distant node information based on the Transformer model and graph coarsening. NACFormer jointly learns the embeddings produced from multi-head attention blocks and from a GCN model trained on a smaller graph. Further, we train the model on a range of coarsened graphs, which not only increases model accuracy but also enhances its adaptability across varying graph structures. By experiments on real-world datasets for node classification tasks, we demonstrate that NACFormer better captures the information of distant nodes, thus producing node embeddings of higher quality that improve node classification accuracy.

Accurate traffic state estimation is essential for effective traffic management and control in intelligent transportation systems. Traditional data-driven approaches often require large amounts of training data and may not fully capture the underlying physical dynamics of traffic flow. Physics-Informed Neural Networks (PINN) have emerged as a promising solution, incorporating the governing physical laws into the neural network training process. However, PINN models face challenges such as slow convergence and limited data availability in real-world scenarios. To address these issues, this paper presents a novel Residual Physics-Informed Neural Network (Res-PINN) architecture for traffic state estimation using connected vehicle data. The Res-PINN model incorporates residual connections to improve information propagation and convergence speed, enhancing the training process and model performance. Furthermore, a transfer learning approach is explored to leverage knowledge from data-rich scenarios and improve the performance of Res-PINN in situations with limited data availability. The proposed Res-PINN model and transfer learning approach are evaluated using real-world connected vehicle data from a major vehicle telematics aggregator, focusing on traffic speed estimation on two freeway corridors in Melbourne, Australia. The results demonstrate the superiority of the Res-PINN model over traditional PINN models and highlight the effectiveness of transfer learning in enhancing traffic state estimation accuracy. The proposed framework advances the field of traffic state estimation by providing accurate and reliable methods, enabling better traffic management and control strategies for more efficient transportation systems.

Time series forecasting is a crucial task for control and decision in various fields. Recent efforts focus on integrating complex deep learning techniques, such as RNN or Transformer, into sequential models. However, these solutions are often criticized due to their excessive complexity. Inspired by the effectiveness of Gradient Boosted Regression Trees (GBRT) methods (such as XGBoost) on tabular datasets, this study proposes a hybrid method for time series forecasting. In this method, we design a memory mechanism for GBRT, namely, Recursive Variational AutoEncoder (RecVAE), which can generate compressed representations of historical sequences by recursively summarizing a section of input time series and preceding internal outputs into current internal outputs. This compensates for the limitation of the GBRT in incorporating long historical sequences for time series forecasting. The resulting memory-fused forecasting model, namely, RecVAE-GBRT, is tested on 4 real-world time series datasets. The results indicate that it generates competitive results compared to Transformer-based time series forecasting methods, all happening at the same level of computation efficiency or better.

It is of paramount importance to detect traffic data anomalies in a real-time manner as it helps efficient traffic control and management. Several unsupervised anomaly detection algorithms are proposed previously in the literature; however, lack of proper ground truth labels for traffic data has been always a substantial barrier to deploy and evaluate them. In this paper, we introduce a concept named Temporal Positioning of Flow-Density Samples (TP-FDS) that can be used by domain experts for fast and reliable traffic data labeling. We mathematically show that deviations in two-dimensional TP-FDS completely reflect point and subsequence anomalies previously defined in the literature of time series data. Furthermore, benefiting from this concept, we propose a novel anomaly detection framework with the help of Fast Angle Based Outlier Detection (Fast-ABOD) to be used for traffic data. Extensive data labeling experiments are conducted with the opinions of 20 different experts. Implementation of several machine learning algorithms, like KNN, OC-SVM, iForest, and LOF, is also adapted with two different setups of hyper-parameters to be used in the proposed framework. Results indicate that our framework integrated with Fast-ABOD is able to detect anomalies in traffic data better than other machine learning and state-of-the-art deep learning algorithms with more than 72% and 96% of F1 score and AUC.

Recent research mainly applies deep learning (DL) methods to short-term traffic forecasting. However, there is a growing interest in long-term forecasting, which allows action optimization at more steps in the future. Motivated by the encouraging success of contrastive representation learning, we propose a powerful and light framework, namely, Traffic Representation Extraction with Contrastive learning frameworK (TRECK), to improve traffic forecasting performance, especially for longer prediction terms. TRECK i) learns disentangled seasonal representations with contrastive learning, ii) enhances the learning of event data with entity embedding and iii) improves generalization and encourages obtaining more effective representations for the forecasting task through multi-task learning. TRECK can be directly applied to typical sequence-to-sequence DL prediction models. We evaluate TRECK when integrated with vanilla base models (RNN and BiLSTM) on large-size and real-world datasets. Experimental results show that TRECK can considerably boost the performance of base models and offer them the capability of handling increasing forecasting horizons. With TRECK, even a naive model like RNN can outperform state-of-the-art Transformer-based and GNN-based methods. Moreover, while avoiding any laborious feature design, the representations extracted by TRECK are more desirable than hand-crafted time features, yielding an 18.69% lower average MAE. Further analysis highlights its efficacy in diverse traffic conditions and in generating prediction intervals.

Fog and Edge computing extend cloud services to the proximity of end users, allowing many Internet of Things (IoT) use cases, particularly latency-critical applications. Smart devices, such as traffic and surveillance cameras, often do not have sufficient resources to process computation-intensive and latency-critical services. Hence, the constituent parts of services can be offloaded to nearby Edge/Fog resources for processing and storage. However, making offloading decisions for complex services in highly stochastic and dynamic environments is an important, yet difficult task. Recently, Deep Reinforcement Learning (DRL) has been used in many complex service offloading problems; however, existing techniques are most suitable for centralized environments, and their convergence to the best-suitable solutions is slow. In addition, constituent parts of services often have predefined data dependencies and quality of service constraints, which further intensify the complexity of service offloading. To solve these issues, we propose a distributed DRL technique following the actor-critic architecture based on Asynchronous Proximal Policy Optimization (APPO) to achieve efficient and diverse distributed experience trajectory generation. Also, we employ PPO clipping and V-trace techniques for off-policy correction for faster convergence to the most suitable service offloading solutions. The results obtained demonstrate that our technique converges quickly, offers high scalability and adaptability, and outperforms its counterparts by improving the execution time of heterogeneous services.

Traffic prediction is an important and yet highly challenging problem due to the complexity and constantly changing nature of traffic systems. To address the challenges, we propose a graph and attentive multi-path convolutional network (GAMCN) model to predict traffic conditions such as traffic speed across a given road network into the future. Our model focuses on the spatial and temporal factors that impact traffic conditions. To model the spatial factors, we propose a variant of the graph convolutional network (GCN) named LPGCN to embed road network graph vertices into a latent space, where vertices with correlated traffic conditions are close to each other. To model the temporal factors, we use a multi-path convolutional neural network (CNN) to learn the joint impact of different combinations of past traffic conditions on the future traffic conditions. Such a joint impact is further modulated by an attention generated from an embedding of the prediction time, which encodes the periodic patterns of traffic conditions. We evaluate our model on real-world road networks and traffic data. The experimental results show that our model outperforms state-of-art traffic prediction models by up to 18.9% in terms of prediction errors and 23.4% in terms of prediction efficiency.

While the electric vehicle (EV) industry is facing some challenges concerning its refueling, its rapid growth in popularity is increasing these difficulties. In this paper, we demonstrate the gravity of the problems that EVs may experience for charging,both now and in the near future, and show how establishing new charging stations can be challenging. We also present the challenges in optimizing the use of charging stations by EV users. Then, we envisage opportunities for the rise of alternative charging options, such as distributed generation, crowdsourced, wireless and mobile charging stations. Additionally, we explain directions on how route and charging stations' location planning can cater to optimizing the charging infrastructure.

Due to the importance of road freight, there is a significant cost of delaying freight vehicles on the road. In this work, we focus on freight vehicle optimization by reducing delays at intersections. Our simulation study evaluates the effectiveness of an autonomous intersection management strategy that prioritizes connected freight vehicles using intelligent traffic lights. We simulate a wide range of traffic scenarios on our microscopic traffic simulator. Our results show that the strategy can help reduce the delay of freight vehicles with a minimal impact on other vehicles in a real road network. Our simulations also reveal the scenarios where the strategy works best and where it should be avoided. Effects of individual parameters are also measured through simulations.

This paper presents an innovative method to adaptively optimize traffic signal plans based on the estimation of traffic situation achieved from the information of various penetration rates of Connected Vehicles (CVs). The network-wide signal control problem is formulated as a linear optimization problem. Moreover, we develop a Kalman filter (KF) and Neural Network (NN) algorithms to predict and update the traffic situation under mixed non-connected and connected vehicles environment. To capture the dynamic of the traffic flow, we employ the cell transmission model synched with the Vissim traffic simulator. The methodology is tested using a challenging network of six intersections. We test our model for various Penetration Rates (PR) of the CV to provide a comparative analysis. The performance of the method is also compared with a conventional actuated-coordinated traffic signal plan. The results show that with a bare minimum PR (say more than 30%), our proposed methodology outperforms the actuated traffic signal plan. (note that the minimum PR is subject to further ongoing research in the literature, to the extent that lower PRs might be plausible). Though a 100% PR is highly desirable, our method can fetch the maximum benefit just by 60% PR.

Wireless sensor network (WSN) plays an important role in monitoring applications in many areas. This paper presents a WSN called EN-Nets which is based on a newly designed small-size and low-power sensor node (EN-Node) for monitoring real-time environmental conditions. Detailed design and implementation of the sensor node for sensing and communicating environmental parameters are described. A power management method utilizing transistor switch connections to sensors to control their operations was used to reduce the overall power consumption of each sensor node. The important testing results for the proposed sensor board are discussed, such as routing method, transmission packet delay and sensors' performance. A comprehensive and interactive graphical user interface program has been developed to allow users to interact with the sensor network system. A real-time deployment has been performed in a campus area and presented in here.

This paper proposes a detailed formulation to optimize transit road space priority at the network level and utilizes an efficient heuristic method to find the optimum solution. Previous approaches to transit priority have a localized focus in which only limited combinations of transit exclusive lanes could be assessed. The aim of this work is to reallocate the road space between private car and transit modes so that the system is optimized. A bilevel programming approach is adapted for this purpose. The upper level involves an objective function from the system managers' perspective, whereas at the lower level, a users' perspective is modeled. To take into account the major effects of a priority provision, three models are used: 1) a modal split; 2) a user equilibrium traffic assignment; and 3) a transit assignment. A genetic algorithm (GA) approach is used, which enables the method to be applied to large networks. Application of a parallel GA is also demonstrated in the solution method, which has a considerably shorter execution time. The methodology is applied to an example network, and results are discussed. It is found that the proposed methodology can successfully consider benefits of all stakeholders in the introduction of transit lanes. Furthermore, using parallel GA enables the methodology to be used for real-world-network scale in a shorter computer processing time.

This paper investigates the effect of heavy commercial vehicles on traffic characteristics and operation of freeway merging sections. Freeways are designed to facilitate the flow of traffic, including passenger cars and trucks. The impact of these different vehicle types is not uniform, creating problems in freeway operations and safety, particularly in the vicinity of merging sections. There have been very few studies that are concerned with the traffic behavior and characteristics of heavy vehicles in these situations. Therefore, a three-year study was undertaken to investigate traffic behavior and operating characteristics during the merging process under congested traffic conditions. First extensive traffic data collection captured a wide range of traffic and geometric information using detectors, videotaping, and surveys at several interchanges. The macroscopic detector data were used to identify and quantify the impact of heavy commercial vehicles on the capacity of merging sections. Subsequently, the microscopic data were utilized to establish a model for the behavior of drivers at merging sections. Based on this behavioral model, a microsimulation program was developed to simulate the actual traffic conditions. This model was used to evaluate the capacity of a merging section for a given geometric design and traffic flow condition. In addition, this model was employed to develop a variety of intelligent transport system control strategies that are associated with heavy commercial vehicles with the goal of designing safer and less-congested freeway merging points. The implementation of the proposed control strategies showed significant improvement over the capacity of merging sections.

This paper describes a microsimulation program developed to study freeway ramp merging phenomena under congested traffic conditions. The results of extensive macroscopic and microscopic studies are used to establish a model for the behavior of merging drivers. A theoretical framework for modeling the ramp and freeway lag driver acceleration-deceleration behavior guided the model development. This methodology uses the stimuli-response psychophysical concept as a fundamental rule and is formulated as a modified form of the conventional car-following models. Data collected at the two merging points are used to calibrate the hypothesized ramp and freeway lag vehicle acceleration models. Drawing on this behavioral model, the freeway merging capacity simulation program (FMCSP) is developed to simulate actual traffic conditions. This model evaluates the capacity of a merging section for a given geometric design and flow condition. Validation of FMCSP is performed using the observed flow, vehicle trajectories, and lane-changing maneuvers. The simulation model is applied to investigate a variety of merging strategies. The results indicated that the FMCSP is capable of simulating the actual traffic conditions of congested freeway ramp merging sections and will aid in the development of traffic management strategies for complex freeway ramp merging areas.