Intelligent traffic control system using GSM technology

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

This paper proposes a new intelligent traffic control (ITC) system which is more efficient than the traffic control system currently used in the state of Kuwait.The proposed ITC system is designed as a dynamic system by using a fuzzy expert system; the fuzzy rules are applied in the visual basic and computer-based program (Excel) to run the validation process.The developed control system applied on five intersections in the grid network at four periods.The results show that the number of vehicles passing the intersection phases is increased in most phases by an average of 12.9% at the first period, 23.3% at the second period, 10.4% at the third period and by 21.2% at the fourth period.For the same periods, the phases green time is increased by an average of 9.1%, 5.8%, 9.9% and 6.3%.And the number of intersection cycles remains constant at the most time which means that the developed control system distributes the phases green time dynamically based on the traffic situation.

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.

To utilize the utmost capacities of junctions with traffic signals, having preemptive and predictive capabilities with integrated traffic sensors are important elements for an intelligent traffic signal control system. Recently, an efficient and preemptive traffic signal control strategy known as iterative tuning (IT) has been developed by exploring repetitive characteristic of traffic demand on working days, whereas on weekends or days with special events, severe nonrepetitive disturbances limit the performance of IT strategy practically. This paper proposes a preemptive and predictive methodology, i.e., IT strategy with reactive compensation (ITRC), to reduce network delay time and eliminate unnecessary stops for vehicles. With anticipation of repetitive traffic flows based on historical data, nominal traffic signals are tuned by IT controller over repetitions. The reactive compensation, which is junction-based model predictive control strategy (JMPC), makes adjustments on nominal traffic signals and compensates the nonrepetitive elements. Rigorous analysis provides sufficient condition for guaranteeing the convergence of ITRC. Case study of Commonwealth Avenue in Singapore with heavy traffic from morning peak to evening peak hours is presented, and the simulation results show the effectiveness, robustness, and convergence of ITRC over pure IT strategy and pure JMPC strategy for scenarios with various traffic variations, respectively.

The existing urban traffic signal control system exhibits certain inefficiencies, including prolonged waiting times and frequent traffic congestion. Research indicates that the integration of neural networks can enhance the effectiveness of intelligent traffic control systems, potentially improving overall traffic flow and reducing delays. Neural network is a highly parallel, nonlinear and highly redundant system. Meanwhile, the self-learning and self-organizing capabilities of neural network have unique advantages for those systems that cannot establish accurate mathematical models. According to the characteristics of complex, variable and random traffic conditions at urban intersections, this paper analyzes the traffic signal control by using neural network control method. This paper introduces a design method of intelligent traffic light based on dynamic time adjustment of traffic flow. By detecting the traffic flow on site, and then allocating the green time of each lane according to the neural network algorithm, the traffic flow dynamic adjustment can be realized, and the traffic capacity of the intersection can be effectively improved. This kind of intelligent control traffic lights can effectively control traffic, and has obvious effects on dreading traffic flow, improving road capacity and reducing traffic accidents.

This research is devoted to the issue of regulating traffic congestion in major cities using light-dependent resistors coupled with the PIC16F877A microcontroller. This study proposes an intelligent traffic control system for T-Junctions, utilizing sensing and control to optimize traffic flow through dynamic phase adjustments and congestion reduction, enabled by a microcontroller-based decision-making system. The proposed system reduces traffic congestion, automates control, and enhances safety, minimizing accidents and lowering infrastructure costs. Under simulated environment, it demonstrates an average response time of 50 ms and achieves 99% accuracy in displaying the correct countdown. Finally, the number of state transitions handled per minute is 60 transitions per minute under normal traffic conditions.

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

A vehicle tracking system is very useful for tracking the movement of a vehicle from any location at any time. An efficient vehicle tracking system is designed and implemented for tracking the movement of any equipped vehicle from any location at any time. The proposed system made good use of popular technology that combines a smartphone with an Arduino UNO. This easy to make and inexpensive compared to others. The designed in vehicle device works using Global Positioning System (GPS) and Global System for Mobile Communication(GSM) technology that is one of the most common ways for vehicle tracking. The device is embedded inside a vehicle those positions is to be determined and tracked in real time. A vehicle tracking system is an electronic device installed in a vehicle to enable the owner or a third party to track the vehicle's location. This paper proposed to design a vehicle tracking system that works using GPS and GSM technology, which would be the cheapest source of vehicle tracking and it would work as anti-theft system. It is an embedded system which is used for tracking and positioning of any vehicle by using Global Positioning System (GPS) and Global system for mobile communication (GSM). An Arduino UNO is used to control the GPS receiver and GSM module. The vehicle tracking system uses the GPS module to get geographic coordinates at regular time interval. The GSM module is used to transmit and update the vehicle location to a database. This paper gives minute by minute update about vehicle location by sending SMS through GSM modem. This SMS contain latitude and longitude of the location of vehicle. Arduino UNO gets the coordinates from GPS modem and then it sends this information to user in text SMS. GSM modem is used to send this information via SMS sent to the owner of the vehicle. Location is displayed on LCD. And then Google map displays location and name of the place on cell phone. Thus, user able to continuously monitor a moving vehicle on demand using smartphone and determine the estimated distance and time for the vehicle to arrive at a given destination.

The Indian traffic control system faces lots of difficulties due to the increasing volume of vehicles, ineffective systems for traffic administration during peak hours, and the frequent need for manual intervention due to the inadequate performance of traffic signals in managing heavy traffic flow. Traditional traffic lights in India have defined timings for each lane, which frequently cause longer traffic jams in lanes with more traffic. This study presents an intelligent traffic control system that incorporates the YOLOv9 model for real-time traffic length prediction and intelligently allocates green, red, and orange signal timings. YOLOv9 builds a bounding box that allows it to compute vehicle density precisely by enclosing the initial and final cars in every frame. As a comparison with traditional fixed-time methods, the proposed approach recalculates traffic signal timings at each cycle based on the recorded length of traffic. In order to ensure that emergency services respond more quickly, the system efficiently prioritizes emergency vehicles by quickly moving them from their lane when they are spotted. This adaptive strategy aligns signal duration with real traffic demand, boosting overall traffic efficiency and regulating traffic flow by reducing unnecessary wait times for low-traffic lanes. In comparison to fixed-timing systems and object detection strategies, our research on adaptive traffic systems reveals a 66% to 77% decrease in vehicle delay. Compared to traditional fixed-timing approaches, the proposed method demonstrates significant enhancements in effectively managing traffic congestion.

This paper presents an intelligent traffic control system to pass emergency vehicles smoothly. Each individual vehicle is equipped with special radio frequency identification (RFID) tag (placed at a strategic location), which makes it impossible to remove or destroy. We use RFID reader, NSK EDK-125-TTL, and PIC16F877A system-on-chip to read the RFID tags attached to the vehicle. It counts number of vehicles that passes on a particular path during a specified duration. It also determines the network congestion, and hence the green light duration for that path. If the RFID-tag-read belongs to the stolen vehicle, then a message is sent using GSM SIM300 to the police control room. In addition, when an ambulance is approaching the junction, it will communicate to the traffic controller in the junction to turn ON the green light. This module uses ZigBee modules on CC2500 and PIC16F877A system-on-chip for wireless communications between the ambulance and traffic controller. The prototype was tested under different combinations of inputs in our wireless communication laboratory and experimental results were found as expected.

Road traffic congestion is a serious problem in today's world and it happens because of urbanization and population growth. The traffic reduces the transport efficiency in the city, increases the waiting time and travel time, and also increases the usage of fuel and air pollution. To overcome these issues this papers propose an intelligent traffic control system using the Internet of Vehicles (IoV). The vehicles or nodes present in the IoV can communicate between themselves. This technique helps in determining the traffic intensity and the best route to reach the destination. The area of study used in this paper is Vellore city in Tamilnadu, India. The city map is separated into many segments of equal size and Ant Colony Algorithm (AOC) is applied to the separated maps to find the optimal route to reach the destination. Further, Support Vector Machine (SVM) is used to calculate the traffic density and to model the heavy traffic. The proposed algorithm performs better in finding the optimal route when compared to that of the existing path selection algorithms. From the results, it is evident that the proposed IoV‐based route selection method provides better performance.

Traffic is a major concern for most of the metropolitan cities of the world. Efficient traffic management can have a major impact on the country's economy. This paper proposes a new digital-logic based system which is more efficient than currently used traffic control systems. The intelligent traffic control system (ITSC) is based on a simple principle; the principle being that "a car can only move ahead if there is space for it" and "the signal remains green until the present cars have passed". By placing sensors at every entry and exit of a junction and monitoring the number of cars present at the junction, it is possible to make traffic very efficient, which is a good application of digital signal processing. However, absolute advantage of such a system will only be felt if every junction in a city is controlled by this system.

The goal of the project is to construct a density-based traffic signal management system that can automatically modify the timing of the signal based on the volume of traffic at the crossing. Traffic signal regulation is becoming a major issue that causes congestion and has a major role in the delay of emergency vehicles because of the rise in automobiles on the road these days. The majority of urban areas in the world have serious traffic problems, thus it's time to switch from more human or fixed modes to automated systems with decision-making skills. The fixed-based traffic signalling system of today may become ineffective if only one lane is used at a time. In this work, Arduino-based intelligent traffic control system framework helps to reduce this issue.

This design is based on single chip microcomputer intelligent traffic control system, function complete two main aspects: normal traffic flow, respectively control the north-south and east-west direction of red, green and yellow lights out, When the north-south intersection traffic is big, can increase the north-south intersection of the green light time, when things intersection traffic is big, can increase the intersection of the green light time, back to normal after the end. This design AT89S51 as the core, to complete the design and implementation of intelligent traffic control system. This design meet the intelligent system, reliability and real-time requirements.

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.