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Traffic lights are crucial for urban traffic management, as they significantly impact congestion reduction and travel safety. Traditional methods relying on hand-crafted rules and operator experience are limited in their ability to adapt to changing traffic environments. To address this challenge, we have been exploring intelligent traffic light control using deep reinforcement learning techniques. However, current approaches often suffer from inadequate training data and unstable training processes, leading to suboptimal performance and real-world consequences. In this study, we propose RELight, a novel random ensemble reinforcement learning-based traffic light control framework. RELight effectively utilizes collected empirical data, ensuring a stable and efficient training process. To evaluate the performance of our proposed framework, we conducted a comprehensive set of experiments on a variety of datasets, including four synthetic datasets and a real traffic dataset collected from surveillance cameras at an intersection in Hangzhou, China. The results show that RELight outperforms existing approaches, demonstrating its superiority and potential for practical traffic light control applications. [ABSTRACT FROM AUTHOR]

The rapid advancements in computer systems and wireless sensor networks (WSNs) motivate many intelligent transportation systems (ITSs) applications in smart cities, such as vehicular ad hoc networks (VANETs). The WSN supports data exchange between mobile vehicles and servers to provide effective traffic control and smooth service for VANET users. However, the vehicle environment still faces many challenges. One of the VANET concerns is the privacy and security of data flows from heterogeneous systems. Therefore, we suggest a Provably Online/Offline Signcryption (POOSC) protocol for vehicular communication in VANET to preserve data confidentiality and ciphertext unforgeability. In this architecture, the transmitter registered in the Identity-Based Cryptosystems (IBC) environment can safely and efficiently communicate with the receiver in the Public-Key Infrastructure (PKI) environment despite the limitations and challenges of heterogeneous systems. In a Random Oracle Model (ROM), we show that our protocol is safe against Indistinguishability-Adaptive Chosen-Ciphertext Attacks (IND-CCA2) under the Computational Diffie–Hellman (CDH) assumption and Existential Unforgeability-Adaptive Chosen Message Attacks (EUF-CMA) under the Discret-Logarithm (DL) assumption. Moreover, the POOSC protocol overcomes the certificate authority burden. Besides, we designed the POOSC protocol with a free pairing that achieves low computation cost and better scalability for VANET environments. The POOSC effectively meets non-repudiation, integrity, and authentication in a logically single step. [ABSTRACT FROM AUTHOR]

Smart cities can effectively improve urban life quality. But, with the rise in population size, there is an increase in the use of vehicles for transportation. In smart city development, traffic control and route planning are the primary tasks to address the problem of travel time estimation. It has become complex due to concerns of wrapped spatio-temporal data on dynamic real-time traffic conditions. The existing methods failed to estimate travel time efficiently due to not considering the spatio-temporal features and lack of computing resources. There is a need for a system like intelligent transportation system to monitor and accurately predict traffic flow to avoid traffic congestion and reduce the impact on the ecological system. This paper developed a novel hybrid deep learning model to estimate optimized travel time and possible trajectories. In this work, U-Net used to reduce the number of feature points for each temporal data and GNN is used to build the graph with connectivity between vehicular nodes. This hybrid model helps us predict traffic flow and aims to estimate accurate travel time from one location (node) to another. The model's performance is evaluated on the standard benchmark datasets Q-Traffic, TaxiBJ, and Chengdu. The experimental results show that the proposed framework significantly improves performance in extracting travel patterns and provides an optimal route with an estimated travel time than existing methods with RMSE 4%, MAE 20.49%, and MAPE 18%. The proposed model has accurately predicted the estimation of travel time of the vehicle for the given urban traffic data. [ABSTRACT FROM AUTHOR]

Accurate assessment of road conditions can effectively alleviate traffic congestion and guide people's travel plans, traffic control decisions of transportation departments, and formulation of traffic-related laws and regulations. This paper proposes a quantum particle swarm optimization (QPSO) and adaptive hybrid exponential smoothing with residual correction (AHES-RC) for the nonlinearity and randomness of traffic flow. In the proposed algorithm, the single–double-exponential smoothing method is mixed with adaptive weights to form AHES, a new method of mixing weights according to real-time traffic trend changes. After the residual correction of AHES is calculated by the extreme learning machine algorithm, the parameters of AHES-RC are optimized using QPSO to improve the prediction accuracy further. This paper comprehensively compares the QPSO-AHES-RC algorithm with other benchmark models through testing on 26 real-world data sets. The results show that the proposed algorithm can adaptive forecasting under various traffic conditions, yielding the best forecasting performance in terms of various forecast error metrics. Compared with advanced machine learning algorithms such as XGBoost and CatBoost, the mean RMSE and mean MAPE have been improved by over 20% on average. In addition, it is revealed that the real-time dynamic capture of traffic flow can effectively improve prediction accuracy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the enormous growth in the public and private vehicles fleet, traffic congestion is increasing at a very high rate. To deal with this, an intelligent mechanism is required.Therefore, this work proposes a novel Neuro-fuzzy based intelligent traffic light control system, which accounts for vehicle heterogeneity by dynamically generating traffic light phase duration considering the real-time heterogeneous traffic load. For this purpose, the proposed model establishes peer-to-peer connections among neighboring traffic light junctions to fetch the respective real-time traffic conditions and congestion. A fuzzy membership function is utilized to generate an intelligent traffic light phase duration. Further, to obtain an effective fuzzy membership function input value considering real-time heterogeneous traffic scenarios, an adaptive neural network is utilized. The proposed system adopts three execution modes: Congestion Mode (CM), Priority Mode (PM), and Fair Mode (FM). It automatically activates and switches to the best mode based on the live traffic conditions. The performance of the proposed model is evaluated via a realistic simulation on the Gwalior city map of India using an open-source simulator known as Simulation of Urban Mobility (SUMO). The results evident the effectiveness of the proposed model over the existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Cooperation between intersections in large-scale road networks is critical in traffic congestion. Currently, most traffic signals cooperate via pre-defined timing phases, which is extremely inefficient in real-time traffic scenarios. Most existing studies on multi-agent reinforcement learning (MARL) traffic signal control have focused on designing efficient communication methods, but have ignored the importance of how agents interact in cooperative communication. To achieve more efficient cooperation among traffic signals and alleviate urban traffic congestion, this study constructs a Graph Cooperation Q-learning Network Traffic Signal Control (GCQN-TSC) model, which is a graph cooperation network with an embedded self-attention mechanism that enables agents to adjust their attention in real time according to the dynamic traffic flow information, perceive the traffic environment quickly and effectively in a larger range, and help agents achieve more effective collaboration. Moreover, the Deep Graph Q-learning (DGQ) algorithm is proposed in this model to optimize the traffic signal control strategy according to the spatio-temporal characteristics of different traffic scenes and provide the optimal signal phase for each intersection. This study also integrates the ecological traffic concept into MARL traffic signal control, which aims to reduce traffic exhaust emissions. Finally, the proposed GCQN-TSC is experimentally validated both in a synthetic traffic grid and a real-world traffic network using the SUMO simulator. The experimental results show that GCQN-TSC outperforms other traffic signal control methods in almost all performance metrics, including average queue length and waiting time, as it can aggregate information acquired from collaborative agents and make network-level signal optimization decisions. [ABSTRACT FROM AUTHOR]

The traffic forecast system is critical in intelligent transportation system. Vehicular Ad-hoc Networks will play a significant role in future intelligent transportation system (VANET). On the highways, they give perfect service to the drivers. For traffic control systems, information systems for drivers and passengers, such as speed, journey time, congestion, emergency services, weather alerts, and many more services, pattern clarity and traffic forecast are critical. Problematic nonlinear pattern complicates these systems. This study offers a Cloud Based Random Forest Algorithm to prevent such issues (CRFA). For road facilities, the Random Forest approach is used to expect the next condition of traffic based on traffic changing in brief intervals of time. The suggested approach employs Random Forest on VANET to evaluate route optimization to get a congestion free road, accident detection, and prevention, as well as providing various services to drivers and passengers while maintaining QOS. Because it is based on the cloud and the Global Positioning System, even in foggy or misty conditions, each vehicle will know exactly where it is going, and if a target approaches another vehicle, a flash of warning will appear on the screen with a loud beep sound, resulting in the automatic reduction of speed to maintain the speed and distance between the vehicles. [ABSTRACT FROM AUTHOR]

Crowd flow prediction is one of the most remarkable issues in a wide range of areas, from traffic control to public safety, and aims to forecast the inflow and outflow of crowds in each region of a city. Most existing studies adopt CNN and its variants to discover the spatial patterns of grid maps (each grid represents a region) while ignoring the correlation between distant regions that may share similar temporal patterns. In this paper, we propose a gnn-based prediction method, called STGs, for crowd flow prediction, which jointly constructs spatial and temporal graphs from grid maps and then implements graph neural networks to directly capture the relationship between regions. Additionally, we introduce a gated fusion mechanism to combine spatial and temporal embedding from the corresponding graph, which further improves the performance of our STGs. We conduct numerical experiments to compare STGs with other baseline models using two real-world datasets, BikeNYC and TLC. Experimental results demonstrate the superiority of our STGs model; specifically, our model reduces the mean absolute error (MAE) of crowd flow prediction by approximately 7-8% compared to state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

The autonomous vehicles emergence is a significant step forward in the development of safe and dependable intelligent transportation systems. In vehicular automation, digital connectivity has been one of the most critical specifications. In this paper, we focus on the autonomous train, which is one of the most rapidly growing applications of autonomous vehicles. In this context, we address the issue of safety by the proposition of Enhanced Train-centric Communication-Based Train Control (ETcCBTC), which provides efficient control of rail traffic. To ensure the operation's safety, we've implemented a new safety-checking approach based on process algebra, which aims to track and correct in real time the train behavior. The proposed ETcCBTC system's performance is tested using extensive simulations and compared to CBTC and TcCBTC. In terms of checking success, transmission load, and response time, the obtained results demonstrate that ETcCBTC outperforms the concurrent systems. [ABSTRACT FROM AUTHOR]

Traffic flow forecasting (TFF) is critical for constructing intelligent transportation systems and offering real-time traffic applications, and especially accurate flow forecasting based on traffic big data can drive reliable strategies for traffic management and control. To consider the weight of the influence of the spatial correlation among the road segments and capture the nonlinear characteristics of traffic flow, this paper presents a parallel Normal Distribution and Attention Mechanism Weighted Deep Bidirectional Long Short-Term Memory (NAW-DBLSTM) algorithm on Spark. Specifically, we employ the resilient distributed data set (RDD) to preprocess large-scale mobile trajectory data (e.g., taxi GPS trajectory data), and then Kalman Filter (KF) is utilized to smooth the taxi trajectory big data. Next, the parallel NAW-DBLSTM algorithm is put forward on a Spark distributed computing platform to enhance the accuracy and scalability of TFF, combined with the attention mechanism and the normal distribution, and then the time window is used for TFF. Finally, the traffic flow is forecasted successfully on Spark by our NAW-DBLSTM algorithm with the real-world GPS trajectories of taxicabs. The experimental results demonstrate that, compared with LSTM, BiLSTM, DBLSTM, DNN, SVR, KNN, SAEs, BP, CNN, GRU, and ANNs, NAW-DBLSTM can produce better performance with the MAPE value that is 85.1%, 80.1%, 85.8%, 73.1%, 78.2%, 77.9%, 78.8%, 84.6%, 96.4%, 86.2%, and 73.2% lower than that of comparable algorithms, respectively. Particularly, the MAPE value of NAW-DBLSTM is 28.3%, 20.1%, 71.1%, and 79.1% lower than that of LSTM weighted with the normal distribution and the attention mechanism (NAW-LSTM), BiLSTM weighted with the normal distribution and the attention mechanism (NAW-BiLSTM), DBLSTM weighted with the normal distribution (NW-DBLSTM), and NAW-DBLSTM weighted without the time window (NT-NAW-DBLSTM), respectively. [ABSTRACT FROM AUTHOR]