As vehicles increasingly integrate with infrastructure and each other, the risk of cyberattacks is escalating significantly. Existing research mainly focuses on the threats within the environments of connected autonomous vehicles (CAVs). However, connected vehicles (CVs) that are driven by humans are also vulnerable to spoofing attacks. The trajectory of CVs under cyberattacks has not been comprehensively explored. Besides, existing research primarily focuses on the countermeasures for detecting but lacks the strategies to be taken after a cyberattack has occurred. In this study, we propose a Cyberattack Trajectory-based Forecasting and Warning System (Cyber-TFWS) that demonstrates effective perception, prediction, judgement and warning capabilities under cyberattacks to enhance the safety against spoofing attacks in CV environment. We introduce a research framework for effectively collecting the trajectories of human-driven CVs under cyberattacks and the trajectories are processed by an unsupervised algorithm. A novel generative-based algorithm named CAGAN integrating into our system is also proposed. The results indicate the proposed system successfully issues the warning actions to the specific drivers and prevented 100% of red-light running behaviors and also significantly enhanced the safety. The proposed system has the potential to be incorporated with CV applications and can benefit governs or police markers in developing the cyberattack protection system.

In vehicle trajectory prediction, traditional methods like Kalman filtering often rely heavily on user expertise and prior knowledge, while newer deep learning approaches, such as Long Short-Term Memory (LSTM) networks, also face challenges related to human intervention and subjective hyperparameter selection. This study proposes a systematic approach for Light Detection and Ranging (LiDAR)-based vehicle trajectory prediction, leveraging LSTM networks to predict vehicle trajectories and employing Bayesian optimization to automatically search for optimal hyperparameter values related to both the training scheme and LSTM architectures. In the experimental study, a custom vehicle trajectory dataset extracted from roadside LiDAR data, along with the V2X-Seq-TFD dataset, was utilized for network training and testing. The optimal LSTM network obtained through Bayesian optimization was compared against two benchmark models: a handcrafted LSTM network and a Kalman filter with a 2D constant velocity motion model. The results demonstrate that the proposed deep learning-based framework, with robust hyperparameter selection through Bayesian optimization, yields more accurate and consistent prediction performance than the benchmark models.

Obtaining future traffic state evolution information is critical to traffic control algorithms design and further to intelligent transportation systems. However, accurately predicting traffic state evolution is not an easy task, although the traffic prediction-related study attracted a lot of attention. This study develops a macroscopic traffic flow model-integrated deep learning framework ($\mathbf{MTFD}$) for the high-resolution temporal-spatial traffic state dynamic propagation on a road network, integrating temporal-spatial traffic dependency, traffic flow theory, and data analysis techniques. First, traffic state propagation on every road section is mathematically described by the$\mathbf{CTM}$model given traffic initial and boundary conditions. Next, a temporal-spatial traffic dependency attention ($\mathbf{TSTD}$) recurrent neural network is developed to predict boundary conditions factoring the traffic temporal-spatial dependency. Also, this paper develops a graph theory-based method to capture the temporal-spatial traffic dependency among the traffic on neighboring road sections. Last, the extended Kalman Filter ($\mathbf{EKF}$) is introduced to adjust the predicted traffic state at an intersection to satisfy the conservation law. The numerical experiments illustrate that the proposed method predicts the traffic state evolution in a freeway network within 30 minutes with accuracy varying from 75%-95%. It has a better performance compared to the tested baseline models (APTN, Graph CNN-LSTM, and so on). The experimental results also illustrate that factoring traffic dependency and integrating data assimilation techniques can improve prediction accuracy.

Since the past decade, Light Detection and Ranging (LiDAR) data have been extensively adopted for traffic object recognition tasks. Existing methodologies often assume LiDAR data are acquired under normal weather conditions. Nevertheless, many researchers have observed that the LiDAR data captured under inclement weather are often contaminated with noises such as fog and snow, which may deteriorate the data quality and lead to false detections in traffic object recognition. This paper proposes a neighborhood-based noise removal methodology to eliminate snow noises from LiDAR data. It identifies a point of interest from a specific laser channel as an outlier, if the number of neighboring points in the same channel within a dynamic search radius is fewer than a threshold. Unlike existing methods that filter the entire LiDAR point cloud, the proposed methodology processes LiDAR data channel-by-channel, which helps reduce the data dimensionality and decouple the snow effects along the vertical axis of the 3D point cloud, leading to more effective and efficient outlier detection. Furthermore, by dynamically changing the search radius based on the point-to-sensor distance rather than adopting a fixed search radius, the proposed methodology can account for the reduced point density at far distances caused by the non-uniformity of LiDAR data. In the experimental study, the proposed methodology is compared against some existing LiDAR de-noising approaches, including two state-of-the-art methods, and demonstrates superior performance in both accuracy (i.e., F1 score$=$98.3%) and efficiency.

In this study, we propose a novel CLAHE-based nighttime image contrast enhancement approach for vehicle detection under nighttime conditions, which improves the contrast of low-quality nighttime images while preventing over-enhancement by employing the image dehazing technique. To implement and evaluate our proposed contrast enhancement method on nighttime images, we consider a scenario of using a camera-based Internet of Things (IoT)-edge computing device for traffic and road surveillance. Edge-computing and IoT technology enable significant amounts of novel studies to advance traffic system monitoring, sensing, control, and management. Considering multiple metrics of image enhancement quality, the proposed nighttime image contrast enhancement method outperforms some existing well-performing CLAHE-based methods. To provide accurate vehicle detection under nighttime conditions and different challenges, including vehicle overlapping, low-light conditions, camera vibrations, and image distortion, must be addressed. For this purpose, a deep neural network based on YOLOv5 architecture has been designed and trained using our custom-labeled dataset. The developed neural network is proven to be effective in the detection of different vehicles under low-light ambient conditions using video captured from a stationary camera. Experiments on our dataset show that the proposed contrast enhancement method greatly improves the detection performance of the trained YOLOv5 model under low-environment-light conditions compared with the model trained using unenhanced images. The model trained with enhanced images can provide an improvement of 5.7% on F1 score, 6.3% on mAP0.5, and 3.4% on mAP0.5:0.95 under specific conditions.

A significant number of camera-based solutions suffer from different kinds of performance issues, caused by unstable weather conditions e.g., wind, object overlapping due to heavy traffic flow, and lack of adjustment for a certain location where the camera is installed and fixed. In this paper, we developed an effective approach to traffic flow monitoring under daytime conditions by applying machine learning and computer vision techniques to extract motion traffic data parameters from the videos captured by the static surveillance camera installed and fixed at the intersection. We address the issue of video camera vibration and jittering by applying image-matching algorithms. In order to calculate the vehicle speed, we utilize the projective transformation to compute the real object distance from an image taken by a single camera installed at the road intersection. We adapt and employ state-of-the-art object detectors, transfer learning, and analytical computer vision methods to detect and track vehicles and measure moving speed and vehicle volume from a predefined detection area extracted from monocular videos. The developed approach is proven to be effective in estimating vehicle speed and vehicle volume using video sequences captured from a stationary camera. Experimental results obtained during the study prove the efficiency of the proposed method under strong vibration conditions. The proposed framework achieved promising tracking performance on custom-labeled test video clips with a small absolute error of 3.97% for traffic flow average speed estimation. An additional test on a video from BrnoCompSpeed Dataset shows that the proposed method can reduce the average speed estimation error by 51.8%.

This paper studies the traffic delay prediction modeling for multiple signalized intersections along the Ala Moana Boulevard and Nimitz Highway in Hawaii. Several machine learning (ML) based approaches have been studied in the literature, and most of them focused on prediction accuracy rather than the end use of real-time control and implementation. These ML models tend to be very complex and non-linear in nature, making it challenging to achieve fast inferences and are computationally heavy for real-time signal control implementation. In this paper, a simple yet accurate hybrid modeling method is proposed to predict traffic delay one-step ahead with the model made suitable for real-time implementation to control traffic flow. Since real-time road-side measurements are recorded in unstructured form, the paper also discusses other issues related to data extraction and the pre-processing process. Finally, a simple signal control loop is developed to demonstrate the proposed modeling approach, which has shown advantages in model accuracy and computation efficiency compared against several existing modeling methods.

Recent technological advancements in computer vision algorithms and data acquisition devices have greatly facilitated the research and applications of deep learning-based traffic object recognition from Light Detection and Ranging (LiDAR) data. The majority of existing methodologies applied deep learning (DL)-based techniques, especially Convolutional Neural Networks (CNNs), for vehicle detection and tracking on autonomous driving datasets. Nevertheless, fewer studies were focused on DL-based vehicle detection using roadside LiDAR data, partially due to the lack of publicly available roadside LiDAR datasets for network training and testing. This paper develops a novel framework based on CNNs and LiDAR data for automated vehicle detection. It leverages the domain knowledge of CNNs trained on large-scale autonomous driving datasets for vehicle detection from roadside LiDAR data. In the experimental study, roadside LiDAR data were collected at a road intersection in Reno, Nevada, U.S. Meanwhile, a CNN architecture was proposed to detect vehicles from LiDAR data through 3D bounding boxes. The proposed CNN was modified from the established PointPillars network by adding dense connections to the convolutional layers to achieve more comprehensive feature extraction. Three CNNs, including the proposed CNN, PointPillars, and YOLOv4, were trained and tested on PandaSet, a publicly available large-scale autonomous driving LiDAR dataset. Subsequently, the trained CNNs were reused for vehicle detection from the captured roadside LiDAR data. The experimental results demonstrated that the proposed CNN outperformed the others in the testing metrics. All three networks showed good performance on vehicle detection from the captured roadside LiDAR data.

In this paper, a deep learning (DL) framework was proposed to predict the taxi-passenger demand while the spatial, the temporal, and external dependencies were considered simultaneously. The proposed DL framework combined a modified density-based spatial clustering algorithm with noise (DBSCAN) and a conditional generative adversarial network (CGAN) model. More specifically, the modified DBSCAN model was applied to produce a number of sub-networks considering the spatial correlation of taxi pick-up events in the road network. And the CGAN model, fed with the historical taxi passenger demand and other conditional information, was capable to predict the taxi-passenger demands. The proposed CGAN model was made up with two long short-term memory (LSTM) neural networks, which are termed as the generative network G and the discriminative network D, respectively. Adversarial training process was conducted to the two LSTMs. In the numerical experiment, different model layouts were compared. It was found that different network layouts provided reasonable accuracy. With limited training data, more LSTM layers in the generator network resulted in not only higher accuracy, but also more difficulties in training. Comparisons were also conducted between the proposed prediction model and four typical approaches, including the moving average method, the autoregressive integrated moving method, the neural network model, and the LSTM neural network model. The comparison results showed that the proposed model outperformed all the other methods. And the repeated experiment indicated that the proposed CGAN model provided significant better predictions than the LSTM model did. Future research was recommended to include more datasets for testing the model and more information for improving predictive performance.

Pavement surface distress conditions are critical inputs for quantifying roadway infrastructure serviceability. Numerous computer-aided automatic examination techniques have been deployed for pavement distress condition assessments, such as digital image processing methods. However, their effectiveness and applicability are impeded due to information losses in 2-D image combination processes or extremely high costs in 3-D geo-referenced data set. In this paper, a cost-effective Kinect-based approach is proposed for 3-D pavement surface reconstruction and cracking recognition. We propose a comprehensive computational solution for the detection and recognition of pavement distress feature identification. Various cracking measurements such as alligator cracking, traverse cracking, longitudinal cracking, and so on. are identified and recognized for their severity examinations based on associated geometrical features. The experimental results indicate that this method is effective in reducing data collection costs and extracting analytical information on pavement cracking measurements. The research findings confirm that the proposed approach provides a viable, applicable solution to an automatic pavement surface condition detection and evaluation. The proposed methodology is transferable for pavement surface reconstruction and distress condition detection based on the other 3-D cloud point data. It provides an alternative inexpensive complement to existing pavement examination methodologies.

Recent technology advances significantly push forward the development and the deployment of the concept of smart, such as smart community and smart city. Smart transportation is one of the core components in modern urbanization processes. Under this context, the connected autonomous vehicle (CAV) system presents a promising solution towards the enhanced traffic safety and mobility through state-of-the-art wireless communications and autonomous driving techniques. Being capable of collecting and transmitting real-time vehicle-specific, location-specific, and area-wide traffic information, it is believed that CAV-enabled transportation systems will revolutionize the existing understanding of network-wide traffic operations and reestablish traffic flow theory. This paper develops a new continuum dynamics model for the future CAV-enabled traffic system, realized by encapsulating mutually-coupled vehicle interactions using virtual internal and external forces. Leveraging Newton's second law of motion, our model naturally preserves the traffic volume and automatically handles both the longitudinal and lateral traffic operations due to its 2-D nature, which sets us apart from the existing macroscopic traffic flow models. Our model can also be rolled back to handle the conventional traffic of human drivers, and the experiment shows that the model describes real-world traffic behavior well. Therefore, we consider the proposed model a complement and generalization of the existing traffic theory. We also develop a smoothed particle hydrodynamics-based numerical simulation and an interactive traffic visualization framework. By posing user-specified external constraints, our system allows users to visually understand the impact of different traffic operations interactively.

In this paper, a self-adaptive tolling strategy (SATS) is developed for dynamically and systematically enhancing highoccupancy toll (HOT) lane system operations. This strategy enhances the overall system performance of both the HOT and general purpose (GP) lanes by better utilizing the HOT lane capacity while maintaining high speed and/or high travel-time reliability for HOT lane traffic when GP lanes are congested. To formulate SATS, the Lighthill-Whitham-Richards kinematic wave model is used to characterize HOT lane traffic flow evolution, and the unilateral Laplace transform is used to convert the system representation from the time domain to the frequency domain. Then, an adaptive tolling controller is designed with both the proportional and integral control components. Real-time traffic measurements, including lane occupancy, average speed, and flow rate, are utilized for toll rate calculations. Following a dual-phase control scheme, the appropriate flow rate for HOT lane utilization is computed, and the corresponding toll is estimated backward. To examine the effectiveness of the proposed tolling strategy, microscopic traffic simulation experiments are conducted using VISSIM. The experiment results demonstrate that the proposed tolling strategy performs reasonably well in improving the overall operations of HOT lane systems under various traffic conditions.

Optimization of signal control at isolated intersections has been an important research focus in traffic engineering over the past few years. Due to its flexibility and practicality, fully actuated control has been extensively deployed. In the conventional actuated control scheme, two important parameters, i.e., minimum and maximum green times, are arbitrarily prespecified, although it is widely recognized that they can significantly impact system operations. Previous studies have concentrated on computing these parameters using deterministic models. Due to the stochastic features of traffic arrival, such statically designated green time boundaries cannot sufficiently handle various traffic demands. To solve this problem, a stochastic model is established to dynamically optimize the minimum and maximum green times using real-time queue lengths and traffic arrival characteristics for each phase. Multiple criteria are fused and exploited as control objectives, such as avoiding cycle failures, minimizing control delays, and maximizing total traffic throughputs. Performance of the proposed algorithms is examined using a microscopic traffic simulation program, i.e., VISSIM 4.30, under various scenarios. The results show that the control system operated by the proposed algorithm produces promising improvements in system operation efficiency and fairness under various traffic demands.