Queuing theory guided intelligent traffic scheduling through video analysis using Dirichlet process mixture model

Pre-timed traffic light system, which is currently used, is no longer sufficient at handling vehicular congestion. Various intelligent traffic control signals are present to replace the currently used system. These systems only focused on the parameters of the vehicles. Most intersections contain traffic signals for pedestrians and vehicles, pedestrian lanes must also be considered in the decision making of the intelligent traffic signals. Cameras were used to estimate the number of pedestrian and vehicle at an intersection. A fuzzy logic controller is used to receive and process the information for the decision making of the traffic light system. NetLogo — a multi-agent programmable modeling environment was used to create a traffic model and a simulation environment to simulate and test the developed fuzzy logic system. Results shows that the fuzzy logic system poses a lower rate of car congestion compared to the pre timed system, and this is tested for 3 sets of data including a different time. A lesser number of vehicles congestion will result to less waiting time at a traffic intersection. The developed fuzzy logic based adaptive traffic light system has proven the effectiveness of reducing the congestion and waiting time of vehicles.

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

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.

Traffic signal systems are employed to continuously manage traffic flow and promote a safe and smooth flow of transport. Traditional traffic signal system provides instructions to stop the vehicle, but if a person breaks the traffic network system, then this system cannot apprehend them, and there are chances of malpractice. Due to the continuous increase in traffic congestion, it is necessary to evaluate the current system and implement improvised technologies to provide an intelligent traffic signal system. Hence, to increase the security of traffic signals, reduce human errors, and avoid malpractices, an intelligent traffic signal system in which the Dijkstra algorithm is practised is introduced. This paper analyzes how to resist a malicious software assault on an intelligent traffic signal system that leads to collision avoidance and check signal latency while adjusting the system during the attack.

The paper describes the design of an intelligent auto traffic signal control system. Traffic congestion is the major issues that would be considered. At the junctions of the road, vehicular traffic intersects and it can be controlled by the traffic signals. One of the main need of the traffic signal is that good coordination and control among the vehicles. The traffic on the road is very high during the peak hours. This leads to the emergency vehicles to stick in the traffic jam. Dynamic control of traffic is needed during peak hours. Hence we propose an automatic traffic light control for emergency vehicles. The traffic light in the path of any emergency vehicles is controlled. It does not depend on the way of monitoring people to be manual which results in time delay. So, Human life is not affected due to the time delay in the arrival emergency vehicles. To control the traffic, the man power is not required in this method. The emergency vehicles find the shortest path to the location of the accident. If the train crosses the junction, the emergency vehicle gets alerted before five km. In front of the zebra line crossing, a divider can be created for safety crossing of people. The system uses AT mega 8micro controller, IR sensors. This system is used to control the traffic lights. And also save the time in emergency periods.

In today's modern world, communication, transportation and the movement of people and merchandises are important, and doing so in the shortest possible time is also essential and vital. In the past decade, due to the significant increase in the number of passengers and vehicles along with the capacity limitations of communication arrays, it is absolutely necessary to apply new technologies to intelligent traffic control and management. The intelligent transportation system (ITS) utilizes advanced technologies in the fields of information processing, telecommunications and electronic control to meet transportation needs. The purpose of these systems is to streamline traffic in important and sensitive routes, and in addition to providing traffic safety, information, timely traffic control and the use of optimal capacity of transport arteries. This paper presents new method for extracting traffic parameters associated with a signalized highway using image processing and data mining KNN classification algorithm. These parameters include the length of red light LED, the volume of passing vehicles and the volume of pedestrians passing the highways in the green phase. In what follows, a Data Mining Traffic Light Control System is introduced, which by receiving the three traffic parameters mentioned above, proceeds to optimize the traffic signal timing. At the end, a two-phase common highway is simulated in the MATLAB software environment, and the results of the image processing algorithms and the Data Mining Traffic Light Control System designed for it are evaluated.

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.

the population of India is growing, the number of vehicles is also increasing tremendously. This makes it difficult to manage time and priority of emergency vehicles like ambulance, fire brigade, police vehicles etc. The proposed model basically works on properly managing the traffic and clear the congestion of vehicles in order to make clear way for emergency vehicles and reduce their effort and time to reach the destination. Some researchers have already worked on proper traffic management by identifying the priority of the vehicles, still their work is not enough to solve the problem completely. So to address this traffic congestion issue an idea has been proposed in this paper which includes use of IoT which helps to overcome the traffic congestion for emergency vehicles. This proposed model includes managing traffic signals using devices like Raspberry PI, Node MCU, RFID Tag and Reader in such a way that when an emergency vehicle arrives, the signal changes by properly communicating with the sensors in vehicle and signal.

Traffic speed prediction is an emerging paradigm for achieving a better transportation system in smart cities and improving the heavy traffic management in the intelligent transportation system (ITS). The accurate traffic speed prediction is affected by many contextual factors such as abnormal traffic conditions, traffic incidents, lane closures due to construction or events, and traffic congestion. To overcome these problems, we propose a new method named fuzzy optimized long short‐term memory (FOLSTM) neural network for long‐term traffic speed prediction. FOLSTM technique is a hybrid method composed of computational intelligence (CI), machine learning (ML), and metaheuristic techniques, capable of predicting the speed for macroscopic traffic key parameters. First, the proposed hybrid unsupervised learning method, agglomerated hierarchical K‐means (AHK) clustering, divides the input samples into a group of clusters. Second, based on parameters the Gaussian bell‐shaped fuzzy membership function calculates the degree of membership (high, low, and medium) for each cluster using Takagi‐Sugeno fuzzy rules. Finally, the whale optimization algorithm (WOA) is used in LSTM to optimize the parameters obtained by fuzzy rules and calculate the optimal weight value. FOLSTM evaluates the accurate traffic speed from the abnormal traffic data to overcome the nonlinear characteristics. Experimental results demonstrated that our proposed method outperforms the state‐of‐the‐art approaches in terms of metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).

A new method of vehicle identification in intelligent traffic command system is proposed, which is directed to some problems in the vehicle detection method of complex traffic conditions in intelligent traffic systems. The method extracts the vehicle structure sub-features according to the structural characteristics of the vehicle itself, and then combines the sub¬features. The identification of the vehicle is achieved by the identification of the combined features. Through experimental analysis, the method not only has a fast processing speed, but also has a high vehicle recognition accuracy. Therefore, this method has broad application prospects and value in intelligent traffic command system.

This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic Algorithms (GAs) and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC), and up to 31% in the comparison with a traditional logic controller, FLC.

A coordinated adaptive traffic system (CATS) is being intended for controlling traffic flows in certain area by coordinating traffic signals among intersection's traffic controllers. Although intersections take only a relatively small part of the entire road system, the traffic control systems in intersections, is one of the most important problems to be solved with regard to the implementation of Intelligent Transportation Systems (ITS). This paper presents our feasiblity study of the using Vehicular ad-hoc network (VANET) for implementing CATS in the absence of communication infrastructure or in intermittent. For this we examined three different routing protocols for implementing such an infrastructure-less CATS. The results show that all the three examined protocols are feasible to be used to implement the VANET-based CATS. However, it shows that the Epidemic is superiors than the other protocols with regard to the delivery probability when the number of vehicles is only a few. When the number of vehicles is increasing then the Spray and Wait protocol give the best delivery probability of messages for delivering the coordination packets among intersections.

Vision based intelligent traffic management system is a robust framework that manages the on road traffic flow in real time by estimating traffic density near traffic signals. We have proposed a simple yet efficient algorithm to calculate the number of vehicles at various signals on a road to efficiently manage the traffic by controlling traffic signals to avoid congestion and traffic jam. The proposed system works by detection of vehicles in video frames acquired by cameras installed on roads and then perform accurate counting of vehicles at the same time. Dynamic background subtraction technique and morphological operations for vehicle detection have been used to achieve better detection efficiency. In order to attain accurate vehicle count in least possible time, we have used Region of Interest based method for vehicle calculation. The proposed framework is designed and implemented in several simulation test cases. It is expected that this work will provide an insight into the design and development of traffic signaling based system and also serves as a basis for practical implementation of a computer vision technology in real-time environment. Furthermore, this work also contributes to new design schemes to increase traffic signaling system's intelligence.

Although the number of automobiles on the road currently is more than it was a few years ago, the infrastructure of roads and traffic systems has not kept up with this expansion, which makes handling traffic challenging. This mismatch causes hence a lot of traffic congestion, crowding, and pollution. All throughout the globe, handling increasing traffic is a major challenge. Operations depend on traffic management in great part. It has to do with planning, running, and purchasing the necessary transportation services—cars, trucks, railroads, boats—that carry commodities and vehicles. Intelligent Traffic Systems (ITS) might be a fantastic approach to address these types of issues by means of modern technology. Given this, traffic control philosophy has to change to accommodate smart cities—which use computer vision techniques to run an adaptive traffic management system depending on CCTV image stream analytics. The adaptive traffic management system detects and counts vehicles accurately. In smart cities, where traffic lights must be automated based on vehicle density, this technique is particularly helpful. This study investigated intelligent traffic control, gathering and monitoring traffic data through using CCTV images. It embodies possible risks in the implementation state, benefits, challenges, as well as the future prospect of ITS.

Traffic congestion has become a major problem in the cities where the population and the number of vehicles are continuously increasing. To ensure the safety of people, traffic light systems are incorporated at all necessary intersections of roads. Most of the traffic control systems used are now working based on fixed time for vehicular movement. So, a smart traffic control system is built which changes the waiting time of the vehicles based on situations, conditions, and priorities. Smart traffic system considers the vehicular density and special conditions such as the presence of an emergency vehicle at any of the roads and increases the waiting or clearance time accordingly. The system works such that, more the density of traffic at any side of the road, the longer would be the clearance time of that road with an increase in the duration of green light so as to allow more vehicles to pass through and hence reduce the traffic density. This avoids traffic congestion, wastage of time, and makes the system more efficient. A priority is given to the passage of emergency vehicles to prevent delays due to traffic congestion. So, the system is built such that during the presence of an emergency vehicle, the traffic light remains green till the emergency vehicle passes the signal and then returns to normal working. The system also considers the prevention of accidents and safety of the pedestrians during the night, when the traffic signals have only Amber lights, by use of sensors to detect any presence. The traffic lights along with alarms are used to indicate such presence. The system is built using Proteus software as a simulation tool for single-way and four ways traffic signals. Thus, the proposed work aims for a better and efficient traffic control system.