Percolation–Stochastic Model for Traffic Management in Transport Networks

The traffic congestion problem in urban areas is worsening since traditional traffic signal control systems cannot provide efficient traffic control. Therefore, dynamic traffic signal control in intelligent transportation system (ITS) recently has received increasing attention. This study devised an adaptive and cooperative multi-agent fuzzy system for a decentralized traffic signal control. To achieve this goal we have worked on a model, which has three levels of control. Every intersection is controlled by its own traffic situation, its neighboring intersections recommendations and a knowledge base, which provides the traffic pattern of each intersection in any particular day of the week and hour of the day. The proposed architecture comprises a knowledge base, prediction module and a traffic observer that provide data to real traffic data preparation module, then a decision-making layer takes decision to how long should the intersection green light be extended. The proposed architecture can achieve dynamic traffic signal control. We have also developed a NetLogo-based traffic simulator to serve as the agents' world. Our approach is tested with traffic control of a large connected junction and the result obtained is promising; The average delay time can be reduced by 21.76% compared to the conventional fixed sequence traffic signal and 14.77% compared to the vehicle actuated traffic signal control strategy.

Enormous advance has proven throughout the years in the area of traffic surveillance by the growth of intelligent traffic video surveillance system. In the current work, through the traffic videos, the traffic video surveillance automatically keyed out the vehicles like ambulance and trucks, which in turn assisted us in directing the vehicles at the time of emergency. Nevertheless, it doesn’t provide us a vital solution for the regulating the traffic. Moreover, this idea just identifies the vehicles, but it couldn’t notice the accidents expeditiously. Therefore in the proposed work, expeditious traffic video surveillance and monitoring system are presented along with dynamic traffic signal control and accident detection mechanism. Hybrid median filter has been utilized at the beginning for pre-processing of traffic videos, and to remove the noise. Hybrid support vector machine (SVM with extended Kalman filter) has been utilized to chase the vehicles. Next, the histogram of flow gradient features are drew-out to categories the vehicles. According to the traffic density and through video files, vehicles are computed, and then for emergency vehicles, the traffic signal gets switched dynamically. To realize the arrival of ambulances, the cameras have been set to catch traffic videos minimum at 500 m of the signal and deep learning neural networks has been employed. Hence dynamic signal control has been incorporated expeditiously. Likewise, multinomial logistic regression has been utilized in real-time live streaming videos, to identify the accidents correctly. The observational solution shows that the proposed intelligent traffic video surveillance system render expeditious dynamic control of traffic signals and it raises the identification of accidents correctly.

Traffic congestion is a major problem in modern urban areas due to the increasing number of vehicles and the limitations of traditional fixed-time traffic signal systems. These conventional systems fail to respond to real-time traffic conditions, resulting in longer waiting times, higher fuel consumption, and increased environmental pollution. This project presents a Dynamic Traffic Signal Allocation System that combines computer vision and real-time data processing to optimize traffic signal control. The system integrates the SUMO traffic simulator to model road networks and simulate traffic flow, while a YOLO-based object detection model is used to detect and count vehicles from simulated video streams. Based on the detected vehicle counts, a Q-Learning algorithm determines the optimal green signal duration for each lane by considering current traffic density and minimizing overall vehicle delay. A TraCI interface is used to communicate with the SUMO simulation in real time and control signal phase operations. Traffic data and system outputs are stored using Firebase Firestore, enabling realtime monitoring across sessions. A Flask-based backend handles data processing and serves the application, while a web-based dashboard built with HTML, CSS, and JavaScript displays traffic statistics through charts and exportable reports. The proposed system demonstrates improved traffic flow, reduced congestion at intersections, and a practical approach to adaptive signal control. It is designed with scalability in mind and shows potential for integration into broader smart city frameworks as a cost-effective alternative to conventional traffic management reflect the core contribution of the paper. Please avoid citations in the abstract.

A traffic signal system established due to the congestion of vehicles at road intersections. Traffic choking at road convergence becomes a controversy for daily riders and proportionally allowing the violation of traffic rules or other hazards. The conventional traffic signal system has lots of disadvantages. Even the system is automatic but due to fixed time range it gives rise to other problem like traffic violation, air pollution indirectly this would lead to deceitful affair. The signal system has been executed using different types of controller some have fast execution time, a greater number of inputs and outputs, reliability also plays an important role in traffic signal system design. To overcome such issues, we proposed a design in this paper which works on the compactness of vehicles on traffic signals and also detects the traffic signal breach done by the vehicles. This paper shows the simulation of dynamic traffic signal and traffic breach control system with IR sensor or piezo sensor to detect the density of vehicles and traffic breach or jumping of red light. This paper related with an FPGA controller as a VLSI design using Verilog in Xilinx software.

Dynamic traffic signal control in Intelligent Transportation Systems (ITS) has been widely developed in major cities' urban areas around the world to provide more efficient way for solving traffic congestion problem. However, the problems in dynamic traffic signal control systems such as system flexibility, data standard common interface, transmission of required information, prediction performance, and real-time measurement data are all important issues. Therefore, in this paper, we proposed a new framework to integrate dynamic data driven application systems (DDDAS) with service-oriented architecture (SOA) and web services technology, for a decentralized traffic signal control system. An efficient and effective distributed data-driven optimization algorithm is designed in this framework to produce an optimal result and best strategy for signal control.

Introduction Drag on city traffic has escalated to be a pivotal problem in almost all cities in the world, and commuter travel time, fuel consumption and emission have also soared. Fixed-time signal control systems have been traditionally used in traffic signal control, but their optimality highly depends on the time of day they are applied and they are not capable of real-time traffic control. In order to overcome these drawbacks, intelligent and adaptive traffic control approaches are being explored by both scientists and traffic developers. Objective The purpose of this research is to create and assess a dynamic traffic light control system by utilizing Snake Optimization (SO), a novel meta-heuristic algorithm. In order to minimize vehicle queues, cut down on delays, and enhance overall traffic flow, the main goal is to examine how well SO optimizes traffic signal timings based on real-time traffic data. by contrasting conventional static control techniques and meta-heuristics with the SO-based dynamic approach. Proposed Method To find the best traffic light phase durations at intersections, the suggested approach uses the Snake Optimization (SO) algorithm, a bio-inspired meta-heuristic technique. Real traffic data from the Algerian city of Algiers, which depicts realistic traffic volumes and patterns, is used to test the method. The SUMO (Simulation of Urban Mobility) simulator, an open-source program popular for simulating microscopic traffic, is used to construct the experimental framework. Traffic scenario modeling, control algorithm implementation, and performance evaluation in a simulated urban setting are all made possible by SUMO. The following steps are part of the optimization process: gathering of data, using the SUMO simulator to model traffic flow and intersections, incorporating the SO algorithm to modify traffic light timings in real time in response to inputs, evaluation of performance using metrics like vehicle throughput, average waiting time, reduction of CO2 emission and total travel time, and evaluation of the benefits obtained by the SO-based approach through comparison with a baseline static control system and ACO-DSOS meta-heuristic. Conclusion According to the simulation results, the suggested approach, which is based on the Snake Optimization algorithm, performs better than other meta-heuristics and conventional static control systems. The algorithm's effectiveness in controlling traffic flows is validated by quantitative comparisons. According to the study's findings, SO is a practical and successful strategy for maximizing traffic light control in practical settings.

Traffic signals, if properly designed and justified according to current engineering practice, can improve the traffic safety and traffic operations at intersections. The effects of resulting crashes and the impacts on safety from the installation of traffic signals at unsignalized intersections are complicated, and the current literature and research on this topic are limited, even though traffic signals have been used for many years. The current crash warrants in the Ontario Traffic Manual, Book 12: Traffic Signals do not take into consideration the effect of traffic volume variations in crashes. It states that if conditions such as "five or more 'correctable' crashes per 12 month period averaged over a 36 month period" are satisfied, a signal may be warranted. The purpose of this paper is to develop and recommend a safety analysis and evaluation tool for estimating the expected safety of the installation of traffic signals through use of historical crash and volume data. The paper also provides a quantitative process tool for determining whether a traffic signal is justified at an unsignalized intersection. The proposed approach uses the empirical Bayes method and crash prediction models for estimating the safety effects on unsignalized intersections that are being considered for traffic signal installation. The performance of the proposed procedure is evaluated and analyzed on 17 unsignalized intersections taken from different jurisdictions in the province of Ontario, Canada.

In recent days number of vehicles is increasing day by day, especially in metropolitan cities. As vehicles increases, traffic density also increases on the road, that results us in slowing the speed, it takes more time to reach the destination, increases in vehicle queuing. If there is no control over traffic signals there may chance of getting accidents also. Improvement in the traffic control is required to overcome all these problems. Traditional traffic control system is not enough to control the current traffic density; traffic control system requires some enhancement to the existing system to avoid the traffic congestion. In the proposed work Intelligent Traffic Control System using Global System for Mobile communication (GSM) is used to overcome the congestion on the roads. The density of the vehicles is obtained by connecting the IR sensors. And depending on the density of the vehicles the traffic light signals are controlled by the microcontroller. The vehicles can also change the route if the traffic is heavy in the current route and information regarding heavy traffic is provided to vehicles in advance through the GSM.

Unsignalized intersections belong to the most common ways of traffic management at intersections. The capacity of the minor entries depends on many factors. One of them is the traffic flow pattern on the major street. In the case of slow-moving queue of vehicles on the main street caused by a downstream traffic signal, the capacity of movements on the minor street joining the queue of vehicles on the main street is significantly reduced. The surveys of traffic parameters were performed in three Polish cities: Wroclaw, Opole, and Krakow. On the basis of these studies, a simulation model representing the traffic conditions at such an intersection was developed. The influence of pedestrian traffic and the number of storage spaces in the median on the possibility of vehicles from the minor entries joining the queue of vehicles on the main street was examined.

Traffic congestion poses a significant challenge in urban environments, leading to delays, increased fuel consumption, and heightened environmental pollution. Addressing this issue, our study presents an innovative dynamic traffic signal control system designed to adapt to real-time traffic conditions. Traditional traffic management systems typically rely on fixed schedules, which are inefficient when dealing with fluctuating traffic volumes. In contrast, our proposed system leverages advanced technologies to optimize traffic flow dynamically. At the core of the system are convolutional neural networks, which process video footage from CCTV cameras to accurately detect and count vehicles. This real-time data enables the system to dynamically adjust traffic signal timings, prioritizing lanes with heavier congestion and minimizing overall delays. By effectively responding to changing traffic patterns, the system reduces wait times and alleviates congestion, as validated through extensive simulations and experimental studies. The proposed approach is both cost-effective and scalable, making it particularly suitable for densely populated regions, such as urban centers in India. Additionally, the system capitalizes on existing infrastructure, providing a practical and efficient solution to urban traffic challenges. Future developments aim to integrate features like emergency vehicle detection and weather condition monitoring, further enhancing the system's responsiveness and optimizing urban traffic management.

The acceleration of urbanization has left cities around the world facing significant challenges, including traffic congestion and inefficient management. Intelligent transportation systems provide new technological means to address these issues, especially in traffic signal control, where advanced communication technologies play an important role in facilitating the process. The potential of long range radio (LoRa) technology in intelligent traffic signal control systems is attracting growing interest, largely due to its capacity for long-range transmission and low power consumption. This paper explores the potential benefits of LoRa technology in traffic flow monitoring and signal control by reviewing existing studies, especially in the field of long-distance transmission and low-power communication. The study indicates that LoRa technology can effectively boost the efficacy of traffic management, reduce traffic congestion, and provide technical support for the development of future intelligent transportation systems. Nevertheless, the stability of LoRa technology in high-density urban environments and its integration with other technologies still present challenges. Further research can investigate the anti-jamming ability, communication rate optimization, and its potential for wider application of LoRa technology in Intelligent Transport Systems (ITS) to promote more efficient urban traffic management

Emergency vehicle detection in urban traffic is a critical component of intelligent transportation systems, directly impacting response times and public safety. This paper proposes a robust deep learning-based approach for real-time emergency vehicle detection using the YOLOv5 object detection framework. Urban environments present numerous challenges for accurate detection, including high vehicle density, varying lighting conditions, frequent occlusions, and diverse vehicle appearances. To address these, we curated a comprehensive dataset comprising real-world traffic scenarios with labeled emergency and non-emergency vehicles across multiple urban settings. The YOLOv5 architecture was fine-tuned with custom augmentation strategies such as random scaling, contrast adjustment, and noise injection to enhance generalization. Additionally, we incorporated an attention mechanism within the detection pipeline to better focus on distinguishing visual features of emergency vehicles such as sirens, lights, and color patterns. The proposed model achieved an attenuation-adjusted mean Average Precision (mAP) of 0.96, demonstrating superior detection accuracy and robustness under challenging conditions. Extensive experiments validate the model's capability to perform real-time inference with low latency, making it suitable for deployment in edge-based traffic monitoring systems. This work significantly contributes to the advancement of smart city infrastructure by enabling automatic emergency vehicle recognition, aiding in dynamic traffic signal control and improved emergency response coordination.

The integration of autonomous systems in traffic management has become increasingly important as urban populations and vehicle numbers continue to rise, leading to significant congestion. Traditional traffic signal control systems, which rely on fixed timing, are no longer sufficient to handle the dynamic and complex nature of urban traffic. To address these challenges, the proposed explainable Deep Reinforcement Learning (DRL) framework aims to optimize traffic signal control by dynamically adjusting traffic signals based on real-time data. This approach enhances traffic flow efficiency, reduces congestion, and improves overall system performance. The framework leverages Vehicle-to-Everything (V2X) communication, which enables real-time data exchange between vehicles, infrastructure, and other road users, extending the perception range of autonomous vehicles and providing valuable insights for traffic signal optimization. Additionally, the integration of smart infrastructure, such as smart intersections, plays a crucial role in enabling adaptive traffic management and facilitating better coordination across multiple intersections. One of the key advantages of the proposed system is its transparency, achieved through the implementation of explainable AI (XAI) techniques. These mechanisms provide clear insights into the decision-making processes, ensuring that traffic management authorities and system users can understand the rationale behind the system’s decisions. Although challenges such as data accuracy, scalability, and cybersecurity risks remain, the proposed DRL framework shows great promise in revolutionizing traffic management systems. Future research directions include enhancing data collection methods, improving the scalability of the system for larger cities, and further developing explainability features to improve trust and adoption in real-world applications.

Dynamic traffic assignment (DTA) applications require traffic signal control data that are typically difficult to obtain and cumbersome to code in the required format. In addition, the evaluation of future scenarios requires future traffic signal settings consistent with the forecast demand. These signal settings are not known a priori and are costly to estimate. Intuitively, future signal timings need to be reasonably optimized so as to represent what the traffic management agency will do. In the literature, integration between traffic control and DTA models has been formulated as a bi-level or single-level optimization problem with system or user optimal constraints. Most existing solution procedures require certain nested structure with an inner-loop algorithm solving the problem of user-equilibrium or system-optimal assignment and the outer-loop algorithm searching for the optimal signal-timing settings. Most of these solution approaches remain only research tools without practical use because of computational intractability. This research proposes an efficient solution algorithm to the problem. An adaptive traffic signal control model is embedded in a simulation-based DTA model. For each inbound approach at an intersection of interest, the adaptive model uses upstream information and a dynamic rolling-horizon approach to project traffic flow conditions for a dynamic but short (projection) period. The adaptive model provides the signal settings during the entire process of traffic flow simulation and for every iteration of the solution algorithm. Thus, during the entire solution process, the experienced travel times and resulting traffic assignment flows are based on the adaptive (demand-responsive) signal settings, allowing the DTA flows and the adaptive signal settings to be generated simultaneously in a single-loop algorithmic structure. Simulation experiments illustrate the capabilities of the proposed approach.

Dynamic capacity estimation of mixed traffic flows with application in adaptive traffic signal control