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In this study, a traffic queue model is employed to simulate traffic parameters for optimization purposes. The primary objective is to enhance the average queue length, cumulative delay, and cumulative reward for varying numbers of vehicles generated using an innovative approach. The proposed methodology integrates deep Reinforcement Q-Learning techniques implemented through a realistic simulator, Simulation of Urban MObility, based on OpenStreetMap. This simulator is used to balance the flow of traffic by monitoring traffic conditions and adjusting signals (Red/Yellow/Green) accordingly. The research extensively evaluates simulation outcomes across different scenarios involving 50, 100, 500, and 1000 vehicles, with episodes 10, 50 and 100. The results provide valuable insights into training times, simulation durations, and total rewards obtained, indicating improvements in mitigating traffic congestion. The introduction of a traffic control strategy enhances model effectiveness through the utilization of four-layer deep neural network architectures with 400 neurons in hidden layers. However, the study also highlights persistent challenges related to real-time implementation and complexities in state-action-agent relationships. The progress highlights improvements in reducing congestion and enhancing efficiency at junctions by dynamically adjusting signal phases while leveraging historical data. The results highlight the potential of dynamic traffic management strategies in effectively addressing urban traffic challenges. [ABSTRACT FROM AUTHOR]

In recent years, the sharp increase in the number of residential motor vehicles and non-motorized vehicles has led to a huge challenge in the intelligent control of urban road traffic operation status. To address the problems of low accuracy of small target detection in complex traffic scenes and the difficulty of balancing the accuracy and detection speed of multiple targets, we propose a deep learning-based Semantic interpolation target detection network SI-SSD based on feature pyramid network FPN and feature graph fusion method, and transfer it to target detection in complex traffic scenes. Shallow feature maps have multi-edge information and deep feature maps have high semantic information. With this idea in mind, the high-resolution shallow feature layer Conv4_3 for the detection of small targets and the deep feature layer Conv7_2 with rich semantic information in SSD network are fused, so as to enhance the sensitivity of the network to the feature information of small targets and improve the network's ability to detect small targets in complex traffic scenarios. The experimental results show that the improved SI-SSD network model has good robustness in the detection of relatively small targets, which can ensure the detection speed and improve the accuracy of detection and recognition significantly. Tests of the data sets UA-DETRAC and VOC2007 show that in the detection of relatively small targets such as Motorbike and Pedestrian, the accuracy is 89.3% and 86.7%, respectively, and the mean average precesion (mAP) is 89.05%. Compared with other target detection networks based on deep learning, the average accuracy of all types of target detection is improved by 4%, and the detection speed is maintained at about 32 fps. [ABSTRACT FROM AUTHOR]

Noise and air pollution are among the most important problems and challenges in urban environments. Noise can cause several problems in functioning within urban settlements and may result in a variety of health problems. Continuous monitoring, prevention, and active work on solving these problems are in the focus of research communities, local and national agencies and organizations, and many international organizations. Traffic noise on city roads has been identified as one of the major problems in urban settlements. Fortunately, it can be monitored and controlled in a way that can reduce its level and negative effects. One of the most effective and cheapest ways to monitor and control traffic noise is to use wireless sensor networks, IoT technology, and cloud-based architectures. This article presents an approach in designing urban traffic noise monitoring system based on LoRaWAN and microservices oriented architecture supported with open-source hardware and software solutions. Design of the system includes planning LoRaWAN network and development of the IoT system layered architecture. Methods for traffic routing and rerouting in urban areas, based on experiments that uses Dijkstra and Floyd–Warshall algorithms are presented. The system implementation and evaluation of the proposed routing modifications are presented in the case of the city of Zrenjanin in Serbia. Discussion of extensions and improvements of the presented system, as well as future research directions, are also presented. [ABSTRACT FROM AUTHOR]

Pedestrian detection is one of the challenging tasks in the urban traffic environments. A natural urban traffic environments include different objects like buildings, vehicles, pedestrians and so on. The conventional approach only used for a particular traffic scenario and it does not suitable for different scenarios. A novel approach is required to model these traffic scenarios. Multiresolution Morlet Decomposition Based Iterative Learning Deformable Part Model (MMD-ILDP) is proposed for improving the performance of multiresolution pedestrian detection to control the traffic in the urban area with higher accuracy. The MMD-ILDP Model uses Morlet wavelet transformation for decomposing the image into subbands with multiple resolutions. After wavelet decomposition, histogram of oriented gradients (HOG) feature pyramid is generated. Then, feature matching is performed between the pedestrian objects in the image and the feature pyramid generated with HOG and the root and part scores are computed. Finally, the root and part scores are combined to compute the final score of objects in the image. The performance measures used in evaluating the proposed algorithm are detection accuracy, time and space complexity. The simulation results show that the MMD-ILDP Model gives improved pedestrian detection in urban traffic environment where healthcare systems find more difficult to reach to the people and also reduces the time complexity on detecting the pedestrians in road traffic scenes when compared to the existing DPM and RealBoost methods. [ABSTRACT FROM AUTHOR]