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Global urbanization and increasing traffic volume have intensified traffic congestion throughout transportation infrastructure, particularly at toll plazas, highlighting the critical need to implement proactive transportation infrastructure solutions. Traditional toll plaza management approaches, often relying on manual interventions, suffer from inefficiencies that fail to adapt to dynamic traffic flow and are unable to produce preemptive control strategies, resulting in prolonged queues, extended travel times, and adverse environmental effects. This study proposes a proactive traffic control strategy using advanced technologies to combat toll plaza congestion and optimize traffic management. The approach involves deep learning convolutional neural network models (YOLOv7–Deep SORT) for vehicle counting and an extended short-term memory model for short-term arrival rate prediction. When projected arrival rates exceed a threshold, the strategy proactively activates variable speed limits (VSLs) and ramp metering (RM) strategies during peak hours. The novelty of this study lies in its predictive and adaptive capabilities, ensuring efficient traffic flow management. Validated through a case study at Ravi Toll Plaza Lahore using PTV VISSIMv7, the proposed method reduces queue length by 57% and vehicle delays by 47% while cutting fuel consumption and pollutant emissions by 28.4% and 34%, respectively. Additionally, by identifying the limitations of conventional approaches, this study presents a novel framework alongside the proposed strategy to bridge the gap between theory and practice, making it easier for toll plaza operators and transportation authorities to adopt and benefit from advanced traffic management techniques. Ultimately, this study underscores the importance of integrated and proactive traffic control strategies in enhancing traffic management, minimizing congestion, and fostering a more sustainable transportation system. [ABSTRACT FROM AUTHOR]

Accurate crowd flow prediction is essential for traffic guidance and traffic control. However, the high nonlinearity, temporal complexity, and spatial complexity that crowd flow data have makes this problem challenging. This research proposes a dynamic graph convolutional network model (Res-DGCN) based on the residual network structure for crowd inflow and outflow prediction in urban areas. Firstly, as the attention layer, the spatio-temporal attention module (SA) is employed for capturing the spatial relationship between the target node and the multi-order adjacent nodes by processing the features of the human flow data. Secondly, a conditional convolution module (SCondConv) is used to enhance the model's capacity for learning about the shifting characteristics of crowd flow to obtain spatial dependence. Finally, we train the model with the Huber loss function to lower the model's sensitivity to outliers and achieve optimal convergence. In two public datasets, the mean absolute error (MAE) of the proposed model is improved by 5.2% and 9.4%, respectively, compared to the baseline models, and the root mean square error (RMSE) is improved by 4.8% and 8.8%, confirming the model's usefulness for crowd flow prediction tasks. [ABSTRACT FROM AUTHOR]

One of the most serious and worsening problems in much of the world is traffic congestion, which represents an undoubted menace to the quality of urban life, including wasting of time, and environmental pollution. Therefore, this study proposes a new dynamic cooperative traffic control algorithm based on Vehicle-to-Infrastructure connections to solve this problem. The proposed model contains three parts. First of all, when each vehicle enters the communication zone, it sends its estimated range of arrival time to the control center in the intersection; then the optimal passing sequence is calculated in the control center by dynamically grouping the compatible trajectories with the respect of the related safety constraints; finally, each vehicle optimizes its appropriate speed profile based on the given optimal time and speed to enter the intersection, which is sent by the control center. Several simulation cases are executed for different traffic volumes, whose results prove that vehicles under the proposed algorithm can pass the intersection with less travel time compared with traditional traffic control methods and other algorithms. Therefore, the proposed method can reduce the time delay and improve traffic efficiency. [ABSTRACT FROM AUTHOR]

Traffic congestion is an important problem faced by Intelligent Transportation Systems (ITS), requiring models that allow predicting the impact of different solutions on urban traffic flow. Such an approach typically requires the use of simulations, which should be as realistic as possible. However, achieving high degrees of realism can be complex when the actual traffic patterns, defined through an Origin/Destination (O-D) matrix for the vehicles in a city, remain unknown. Thus, the main contribution of this paper is a heuristic for improving traffic congestion modeling. In particular, we propose a procedure that, starting from real induction loop measurements made available by traffic authorities, iteratively refines the output of DFROUTER, which is a module provided by the SUMO (Simulation of Urban MObility) tool. This way, it is able to generate an O-D matrix for traffic that resembles the real traffic distribution and that can be directly imported by SUMO. We apply our technique to the city of Valencia, and we then compare the obtained results against other existing traffic mobility data for the cities of Cologne (Germany) and Bologna (Italy), thereby validating our approach. We also use our technique to determine what degree of congestion is expectable if certain conditions cause additional traffic to circulate in the city, adopting both a uniform pattern and a hotspot-based pattern for traffic injection to demonstrate how to regulate the overall number of vehicles in the city. This study allows evaluating the impact of vehicle flow changes on the overall traffic congestion levels. [ABSTRACT FROM AUTHOR]

Nowadays, many cities have problems with traffic congestion at certain peak hours, which produces more pollution, noise and stress for citizens. Neural networks (NN) and machine-learning (ML) approaches are increasingly used to solve real-world problems, overcoming analytical and statistical methods, due to their ability to deal with dynamic behavior over time and with a large number of parameters in massive data. In this paper, machine-learning (ML) and deep-learning (DL) algorithms are proposed for predicting traffic flow at an intersection, thus laying the groundwork for adaptive traffic control, either by remote control of traffic lights or by applying an algorithm that adjusts the timing according to the predicted flow. Therefore, this work only focuses on traffic flow prediction. Two public datasets are used to train, validate and test the proposed ML and DL models. The first one contains the number of vehicles sampled every five minutes at six intersections for 56 days using different sensors. For this research, four of the six intersections are used to train the ML and DL models. The Multilayer Perceptron Neural Network (MLP-NN) obtained better results (R-Squared and EV score of 0.93) and took less training time, followed closely by Gradient Boosting then Recurrent Neural Networks (RNNs), with good metrics results but the longer training time, and finally Random Forest, Linear Regression and Stochastic Gradient. All ML and DL algorithms scored good performance metrics, indicating that they are feasible for implementation on smart traffic light controllers. [ABSTRACT FROM AUTHOR]

With the advanced development of the intelligent transportation system, vehicular ad hoc networks have been observed as an excellent technology for the development of intelligent traffic management in smart cities. Recently, researchers and industries have paid great attention to the smart road-tolling system. However, it is still a challenging task to ensure geographical location privacy of vehicles and prevent improper behavior of drivers at the same time. In this paper, a reliable road-tolling system with trustworthiness evaluation is proposed, which guarantees that vehicle location privacy is secure and prevents malicious vehicles from tolling violations at the same time. Vehicle route privacy information is encrypted and uploaded to nearby roadside units, which then forward it to the traffic control center for tolling. The traffic control center can compare data collected by roadside units and video surveillance cameras to analyze whether malicious vehicles have behaved incorrectly. Moreover, a trustworthiness evaluation is applied to comprehensively evaluate the multiple attributes of the vehicle to prevent improper behavior. Finally, security analysis and experimental simulation results show that the proposed scheme has better robustness compared with existing approaches. [ABSTRACT FROM AUTHOR]

A Vehicular Ad-hoc Network (VANET) comprises a group of moving or stationary vehicles connected by a wireless network. VANETs play a vital role in providing safety and comfort to drivers in vehicular environments. They provide smart traffic control and real-time information, event allocation. VANETs have received attention in support of safe driving, intelligent navigation, emergency and entertainment applications in vehicles. Nevertheless, these increasingly linked vehicles pose a range of new safety and security risks to both the host and its associated properties and may even have fatal consequences. Violations of national privacy and vehicle identities are a major obstacle to introducing forced contact protocols in vehicles. Location privacy refers to the privacy of the vehicle (driver) and the location of the vehicle. Whenever a vehicle sends a message, no one but authorized entities should know their real identity and location of the vehicle. All the messages sent by the vehicle must be authenticated before processing, hence location privacy is an important design aspect to be considered in VANETs operations. The novelty of this paper is that it specifically reviews location privacy in VANETs in terms of operational and safety concerns. Furthermore, it presents a critical analysis of various attacks, identity thefts, manipulation and other techniques in vogue for location privacy protection available in state-of-the-art solutions for VANETs. The efforts in this paper will help researchers to develop a great breadth of understanding pertaining to location privacy issues and various security threats encountered by VANETs and present the critical analysis of the available state-of-the- art solutions to maintain location privacy in VANETs. [ABSTRACT FROM AUTHOR]

Intelligent transport systems (ITS) are a convergence of information technology and transportation systems as seen in the variable speed limit (VSL) system. Since the VSL system controls the speed limit according to the traffic conditions, it can improve the safety and efficiency of a transport network. Many researchers have studied the real-time VSL (RVSL) algorithm based on real-time traffic information from multiple stations recording traffic data. However, this method can suffer from inaccurate selection of the VSL start station (VSS), incorrect VSL calculations, and is unable to quickly react to the changing traffic conditions. Unstable VSL systems result in more congestion on freeways. In this study, an enhanced VSL algorithm (EVSL) is proposed to address the limitations of the existing RVSL algorithm. This selects preliminary VSL start stations (pVSS), which is expected to end congestion using acceleration and allocates final VSSs for each congestion interval using selected pVSS. This controls the vehicles that entered the congestion area based on the selected VSS. We used four metrics to evaluate the performance of the proposed VSL (VSS stability assessment, speed control stability assessment, travel time, and shockwave), which were all enhanced when compared to the standard RVSL algorithm. In addition, the EVSL algorithm showed stable VSL performance, which is critical for road safety. [ABSTRACT FROM AUTHOR]

Various traffic-sensing technologies have been employed to facilitate traffic control. Due to certain factors, e.g., malfunctioning devices and artificial mistakes, missing values typically occur in the Intelligent Transportation System (ITS) sensing datasets, resulting in a decrease in the data quality. In this study, an integrated imputation algorithm based on fuzzy C-means (FCM) and the genetic algorithm (GA) is proposed to improve the accuracy of the estimated values. The GA is applied to optimize the parameter of the membership degree and the number of cluster centroids in the FCM model. An experimental test of the taxi global positioning system (GPS) data in Manhattan, New York City, is employed to demonstrate the effectiveness of the integrated imputation approach. Three evaluation criteria, the root mean squared error (RMSE), correlation coefficient (R), and relative accuracy (RA), are used to verify the experimental results. Under the ±5% and ±10% thresholds, the average RAs obtained by the integrated imputation method are 0.576 and 0.785, which remain the highest among different methods, indicating that the integrated imputation method outperforms the history imputation method and the conventional FCM method. On the other hand, the clustering imputation performance with the Euclidean distance is better than that with the Manhattan distance. Thus, our proposed integrated imputation method can be employed to estimate the missing values in the daily traffic management. [ABSTRACT FROM AUTHOR]

Automatic vehicle detection and counting are considered vital in improving traffic control and management. This work presents an effective algorithm for vehicle detection and counting in complex traffic scenes by combining both convolution neural network (CNN) and the optical flow feature tracking-based methods. In this algorithm, both the detection and tracking procedures have been linked together to get robust feature points that are updated regularly every fixed number of frames. The proposed algorithm detects moving vehicles based on a background subtraction method using CNN. Then, the vehicle's robust features are refined and clustered by motion feature points analysis using a combined technique between KLT tracker and K-means clustering. Finally, an efficient strategy is presented using the detected and tracked points information to assign each vehicle label with its corresponding one in the vehicle's trajectories and truly counted it. The proposed method is evaluated on videos representing challenging environments, and the experimental results showed an average detection and counting precision of 96.3% and 96.8%, respectively, which outperforms other existing approaches. [ABSTRACT FROM AUTHOR]

Traffic data are the basis of traffic control, planning, management, and other implementations. Incomplete traffic data that are not conducive to all aspects of transport research and related activities can have adverse effects such as traffic status identification error and poor control performance. For intelligent transportation systems, the data recovery strategy has become increasingly important since the application of the traffic system relies on the traffic data quality. In this study, a bidirectional k-nearest neighbor searching strategy was constructed for effectively detecting and recovering abnormal data considering the symmetric time network and the correlation of the traffic data in time dimension. Moreover, the state vector of the proposed bidirectional searching strategy was designed based the bidirectional retrieval for enhancing the accuracy. In addition, the proposed bidirectional searching strategy shows significantly more accuracy compared to those of the previous methods. [ABSTRACT FROM AUTHOR]

Over the past decades, there has been significant research effort dedicated to the development of intelligent vehicles and V2X systems. This paper proposes a road traffic risk assessment method for road traffic accident prevention of intelligent vehicles. This method is based on HMM (Hidden Markov Model) and is applied to the prediction of steering angle status to (1) evaluate the probabilities of the steering angle in each independent interval and (2) calculate the road traffic risk in different analysis regions. According to the model, the road traffic risk is quantified and presented directly in a visual form by the time-varying risk map, to ensure the accuracy of assessment and prediction. Experiment results are presented, and the results show the effectiveness of the assessment strategies. [ABSTRACT FROM AUTHOR]

Vehicle driving path planning is an important information service in intelligent transportation systems. As an important basis for path planning optimization, the travel time prediction method has attracted much attention. However, traffic flow has features of high nonlinearity, time-varying, and uncertainty, which makes it hard for prediction method with single feature to meet the accuracy demand of intelligent transportation system in big data environment. In this paper, the historical vehicle Global Positioning System (GPS) information data is used to establish the traffic prediction model. Firstly, the Clustering in QUEst (CLIQUE)-based clustering algorithm V-CLIQUE is proposed to analyze the historical vehicle GPS data. Secondly, an artificial neural network (ANN)-based prediction model is proposed. Finally, the ANN-based weighted shortest path algorithm, A-Dijkstra, is proposed. We used mean absolute percentage error (MAPE) to evaluate the predictive model and compare it with the predicted results of Average and support regression vector (SRV). Experiments show that the improved ANN path planning model we proposed can accurately predict real-time traffic status at the given location. It has less relative error and saves time for users' travel while saving social resources. [ABSTRACT FROM AUTHOR]