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The precise estimation of time-varying demand matrices using traffic data is an essential step for planning, scheduling, and evaluating advanced traffic management systems. This paper presents an innovative method, based on the least squares approach, to handle the inherent complexities of estimating the dynamic characteristics of changing demand flow over time while considering congestion conditions. The time-dependent origin–destination (OD) demand matrices of the network are estimated by exploiting the received partial paths data from an automated vehicle identification system and vehicle counts data from loop detectors on a subset of the links. A traffic assignment approach based on partial paths is embedded into the measurement equations of the least squares model. For all time intervals, the relation between the variable aspects of congestion (the temporal and spatial distribution of the OD traffic flows) is established by their variance–covariance matrices. The LSQR algorithm, an iterative algorithm that is logically equivalent to the conjugate gradient method, is employed for solving the proposed least squares problem. Numerical examples are performed on three different approaches: utilizing only link counts data, utilizing only partial path flows data, and utilizing both of them. The results demonstrate that using variance–covariance matrices provides more precise estimates for time-dependent OD matrices. The effectiveness of the solution algorithm and the main ideas of the model are examined using the Sioux-Falls and Sodermalm networks. This paper reports the features of the discussed model based on different data as a proof of concept that incorporating partial path flows significantly improves the results for solving time-dependent OD matrix estimation problems.

Distributed Acoustic Sensing (DAS), an advanced vibration-sensing technology, shows immense promise for data-centric urban surveillance, notably in tracking vehicle speeds. The extraction of vehicle information from DAS data demands real-time processing. Nonetheless, current techniques face challenges in the precise and automated interpretation of vehicle signals. This study presents an integrated two-phase deep learning framework meticulously designed to facilitate the real-time and automated detection of vehicle speeds and directions using Distributed Acoustic Sensing (DAS) data. Initially, the approach employs a contrastive learning-based model to process DAS signals, eliminating disturbances within the profile. The YOLOv8 detection model subsequently accurately detects the processed DAS signals. The vehicle’s speed and direction are ultimately ascertained by harnessing the positional data derived from the bounding box’s coordinates. Within real-world test scenarios, the method introduced can precisely identify vehicles originating from various directions and sources. The strategy demonstrates robust generalizability even in complex scenarios characterized by intense interference and the presence of multiple vehicles. From a quantitative assessment perspective, the system processes DAS data for 60-second intervals in an average of 4.89 seconds, achieving an accuracy rate of 90.07%, which satisfies the demands for real-time vehicle signal detection. The approach presented in this article offers crucial guidance for the real-time, automatic detection of vehicle signals in DAS systems.

Vehicle re-identification (reID) aims to identify vehicles across different cameras that have non-overlapping views. Most existing vehicle reID approaches train the reID model with well-labeled datasets via a supervised manner, which inevitably causes a severe drop in performance when tested in an unknown domain. Moreover, these supervised approaches require full annotations, which is limiting owing to the amount of unlabeled data. Therefore, with the aim of addressing the aforementioned problems, unsupervised vehicle reID models have attracted considerable attention. It always adopts domain adaptation to transfer discriminative information from supervised domains to unsupervised ones. In this paper, a novel progressive learning method with a multi-scale fusion network is proposed, named PLM, for vehicle reID in the unknown domain, which directly exploits inference from the available abundant data without any annotations. For PLM, a domain adaptation module is employed to smooth the domain bias, which generates images with similar data distribution to unlabeled target domain as “pseudo target samples”. Furthermore, to better exploit the distinct features of vehicle images in the unknown domain, a multi-scale attention network is proposed to train the reID model with the “pseudo target samples” and unlabeled samples; this network embeds low-layer texture features with high-level semantic features to train the reID model. Moreover, a weighted label smoothing (WLS) loss is proposed, which considers the distance between samples and different clusters to balance the confidence of pseudo labels in the feature learning module. Extensive experiments are carried out to verify that our proposed PLM achieves excellent performance on several benchmark datasets.

After several decades of work by several talented researchers, estimation of the origin–destination matrix using traffic data has remained very challenging. This paper presents a set of innovative methods for estimation of the origin–destination matrix of large-scale networks, using vehicle counts on links, partial path data obtained from an automated vehicle identification system, and combinations of both data. These innovative methods are used to solve three origin–destination matrix estimation models. The first model is an extension of Spiess’s model which uses vehicle count data while the second model is an extension of Jamali’s model and it uses partial path data. The third model is a multiobjective model which utilizes combinations of vehicle counts and partial path data. The methods were tested to estimate the origin–destination matrix of a large-scale network from Mashhad City with 163 traffic zones and 2093 links, and the results were compared with the conventional gradient-based algorithm. The results show that the innovative methods performed better as compared to the gradient-based algorithm.

Automatic License Plate Recognition (ALPR) systems are important in Intelligent Transportation Services (ITS) as they help ensure effective law enforcement and security. These systems play a significant role in border surveillance, ensuring safeguards, and handling vehicle-related crime. The most effective approach for implementing ALPR systems utilizes deep learning via a convolutional neural network (CNN). A CNN works on an input image by assigning significance to various features of the image and differentiating them from each other. CNNs are popular for license plate character recognition. However, little has been reported on the results of these systems with regard to unusual varieties of license plates or their success at night. We present an efficient ALPR system that uses a CNN for character recognition. A combination of pre-processing and morphological operations was applied to enhance input image quality, which aids system efficiency. The system has various features, such as the ability to recognize multi-line, skewed, and multi-font license plates. It also works efficiently in night mode and can be used for different vehicle types. An overall accuracy of 98.13% was achieved using the proposed CNN technique.

Intelligent transportation systems (ITS) are the integration of information and communications technologies with applications which are significant in traffic control and management. The increased number of on road vehicles in urban areas urges the need of development of automated methods for traffic management. Vehicle identification, classification and analysis enable the intelligent transportation systems to make decisions. In this paper, an automated method for video analysis for vehicle identification using a modified Region based Convolution Neural Network (RCNN) has been proposed. The traffic videos collected by CCTV cameras installed on the roads are analyzed for vehicle identification in a given frame. The pretrained google net is used to extract features. These features are used by the Region based Convolution Neural Network for vehicle identification. The vehicles are identified using probability score computed using intersection of objects (IoU). The identified vehicles are classified into ten different vehicle classes. The proposed network concatenates features from previous layers to reduce loss and consequently improve the vehicle identification accuracy. The vehicle identification method is further extended for vehicle counting and behavioral analysis. The vehicle counting information can be used for congestion control in smart cities. The behavioral analysis includes computation of speed of vehicles. The speed information is useful for traffic law enforcement in smart cities. The proposed method is applied on MIO-TCD vehicle dataset and EBVT video dataset. The results are calculated using three different metrics namely average accuracy, mean precision and mean recall. Obtained results are also compared with other state of the art methods. The results show significant improvement and thus the method can be effectively used for video analysis.

The technology that can recognize the license plates of vehicles in real time and manage them automatically is a key element of building an intelligent transportation system. License plate recognition is the most important technique in vehicle image processing used to identify a vehicle. Object recognition using a camera is greatly influenced by environmental factors in which the camera is installed. When the vehicle image is acquired, the image is distorted due to the tilting of the license plate, reflection of light, lighting effects, rainy weather, and nighttime, so that it is difficult to accurately recognize the license plate. In addition, when the geometric distortion of the license plate image or the degradation of the image quality is intensified, it may be more difficult to automatically recognize the license plate image. Therefore, in this paper, we propose a deep learning–based vehicles’ license plate recognition method to detect license plate and recognize characters accurately in complex and diverse environments. As a deep learning model, the YOLO model can be used to detect robust license plates in a variety of environments and to recognize characters quickly and accurately. It can also be seen that the license plate accurately recognizes the license plate with geometric distortion.

The movement of vehicles in and out of the predefined enclosure is an important security protocol that we encounter daily. Identification of vehicles is a very important factor for security surveillance. In a smart campus concept, thousands of vehicles access the campus every day, resulting in massive carbon emissions. Automated monitoring of both aspects (pollution and security) are an essential element for an academic institution. Among the reported methods, the automated identification of number plates is the best way to streamline vehicles. The performances of most of the previously designed similar solutions suffer in the context of light exposure, stationary backgrounds, indoor area, specific driveways, etc. We propose a new hybrid single-shot object detector architecture based on the Haar cascade and MobileNet-SSD. In addition, we adopt a new optical character reader mechanism for character identification on number plates. We prove that the proposed hybrid approach is robust and works well on live object detection. The existing research focused on the prediction accuracy, which in most state-of-the-art methods (SOTA) is very similar. Thus, the precision among several use cases is also a good evaluation measure that was ignored in the existing research. It is evident that the performance of prediction systems suffers due to adverse weather conditions stated earlier. In such cases, the precision between events of detection may result in high variance that impacts the prediction of vehicles in unfavorable circumstances. The performance assessment of the proposed solution yields a precision of 98% on real-time data for Malaysian number plates, which can be generalized in the future to all sorts of vehicles around the globe.

The automatic license plate recognition (LPR) system has the advantages of strong continuity, high data accuracy, and large detection samples. The detection data can be used as quasi and full sample sampling of road network vehicles. However, the system has the disadvantage of sparse geographical location, so the data is difficult to be used effectively. In order to obtain the full sample vehicle travel trajectory on an urban road network, this paper investigates the sparse trajectory recovery problem based on LPR data. A trajectory reconstruction algorithm based on the Markov decision process (MDP) in road network space is proposed. The algorithm is divided into two stages, including off-line training and on-line prediction. In the off-line training stage, the LPR data is transformed into the trajectory set represented by the link edge sequence in the road network space. The MDP model is used to describe the vehicle driving behavior, and the design rules of the link reward function in the model are discussed. An unsupervised Bayesian inverse reinforcement learning algorithm is proposed to train the historical vehicle trajectory data and learn the model parameters. In the online prediction stage, the transfer probability between links is calculated according to the trained model. The negative logarithm of the transfer probability modified by the spatio-temporal coefficient is used as the edge weight to construct a directed graph. The shortest path search is used to obtain the path with the highest probability to restore the missing path. The proposed method is implemented on a realistic urban traffic network in Ningbo, China. The comparison with the baseline algorithms indicates that the proposed method has higher accuracy, especially when the coverage rate of the LPR device is low. When the coverage rate is more than 60%, the comprehensive accuracy of the proposed algorithm is more than 85%, and reliable path estimation results can be obtained.

Recently, the number of vehicles on the road, especially in urban centres, has increased dramatically due to the increasing trend of individuals towards urbanisation. As a result, manual detection and recognition of vehicles (i.e., license plates and vehicle manufacturers) become an arduous task and beyond human capabilities. In this paper, we have developed a system using transfer learning-based deep learning (DL) techniques to identify Jordanian vehicles automatically. The YOLOv3 (You Only Look Once) model was re-trained using transfer learning to accomplish license plate detection, character recognition, and vehicle logo detection. In contrast, the VGG16 (Visual Geometry Group) model was re-trained to accomplish the vehicle logo recognition. To train and test these models, four datasets have been collected. The first dataset consists of 7035 Jordanian vehicle images, the second dataset consists of 7176 Jordanian license plates, and the third dataset consists of 8271 Jordanian vehicle images. These datasets have been used to train and test the YOLOv3 model for Jordanian license plate detection, character recognition, and vehicle logo detection. In comparison, the fourth dataset consists of 158,230 vehicle logo images used to train and test the VGG16 model for vehicle logo recognition. Text measures were used to evaluate the performance of our developed system. Moreover, the mean average precision (mAP) measure was used to assess the YOLOv3 model of the detection tasks (i.e., license plate detection and vehicle logo detection). For license plate detection, the precision, recall, F-measure, and mAP were 99.6%, 100%, 99.8%, and 99.9%, respectively. While for character recognition, the precision, recall, and F-measure were 100%, 99.9%, and 99.95%, respectively. The performance of the license plate recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 99.8%, 99.8%, and 99.8%, respectively. Furthermore, for vehicle logo detection, the precision, recall, F-measure, and mAP were 99%, 99.6%, 99.3%, and 99.1%, respectively, while for vehicle logo recognition, the precision, recall, and F-measure were 98%, 98%, and 98%, respectively. The performance of the vehicle logo recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 95.3%, 99.5%, and 97.4%, respectively.