Architecture Design of Q-Learning Accelerator for Intelligent Traffic Control System

The current traffic light system uses a lot of fixed time signals. Fixed time signals cause traffic jams in one or several lanes due to traffic congestion. On the other hand, emergency vehicles cannot get their rights to the maximum due to the density of these vehicles. This study aims to build an intelligent traffic light with a vehicle density sensor device and a smartphone application to increase the accuracy of light timing and parse traffic density. Furthermore, in an emergency, they can still overcome and accelerate the speed of passing vehicles by manually intervening with the traffic light through a smartphone application connected wirelessly to the traffic light. The intelligent traffic light is made with Arduino Bluetooth Control (ABC), Bluetooth HC-05, Arduino Mega 2560, and infrared (IR) sensor E18-D80NK. The results show that the green light setting is based on the input from the IR sensor both near sensor (NS) and far sensor (FS) work well. When NS detects a vehicle with a green light, it increases Y seconds from the default X seconds. If FS detects a vehicle with a green light, it increases by Z seconds. Settings with the ABC application can randomly turn on the green light in four lanes according to the will of the smartphone operator.

The congestion of the urban traffic is becoming one of critical issues with increasing population and automobiles in cities. Traffic jams not only cause extra delay and stress for the drivers, but also increase fuel consumption, add transportation cost, and increase carbon dioxide air pollution. The traffic controller is one of critical factors affecting the traffic flow. The conventional traffic patterns are nonlinear and complex. As a result, the fixed traffic light controller is not optimized to reduce the traffic jam. Moreover, it does not improve the response time for ambulances, fire trucks, police cars and other emergency vehicles. This paper is written with the endeavor to provide the readers an idea of the research that has been carried out in the intelligent traffic light field and the microcontroller based traffic light control system. The historical review of the technology and the recent development of the intelligent traffic light control system are stated in this paper. In addition, the future studies of the related work are also presented.

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

Parking and running red lights are the major problems in traffic violations. This study presents two application systems for finding parking spaces and controlling traffic lights. An intelligent traffic light system adaptively controls the time of traffic lights so that the period of green lights is adjusted according to the real-time traffic flow. The traffic jam is thus effectively reduced. An intelligent parking system is also implemented to help drivers find parking spaces efficiently. Initially, a web camera is utilized to capture images. Hence, vehicles are identified to compute the remaining number of parking spaces. A website shows the remaining parking spaces. This system can help a user to find suitable parking spaces quickly. Therefore, parking violations are reduced. The YOLO (You Only Look Once) neural network is employed to identify vehicles, where the networks are trained through transfer learning. Only a small quantity of training images is required. The experimental results show that the vehicle identification accuracy rate can reach 93%, the recall rate reaches 83%, and the overall F measure reaches 87.7%. Therefore, the vehicle identification system can be applied to intelligent traffic light control and roadside parking spaces.

Deep reinforcement learning based intelligent traffic light is an increasing tendency for improving the traffic condition due to its promising control performance. The current works about intelligent traffic lights tend to control the signal phase or phase time under fixed-lane condition. However, the fixed-lane condition cannot fit the different vehicle flow very well, thus the traffic condition cannot be enhanced further. In this paper, for improving the traffic condition further, we extend the existing intelligent traffic lights to the two-dimension control problem where the agent needs to control not only the traffic light signal, but also the direction of changeable lanes. As a result, the traffic light signal and lane allocation satisfy the need of current vehicle flow simultaneously. Furthermore, for the joint problem of lane allocation and the control of traffic light control, we develop a Q-network structure to build its deep reinforcement learning model. The simulation results demonstrate that the performance of the proposed method outperforms that of the existing intelligent traffic light with fixed lane.

Present Traffic Light Controllers (TLC) are based on microcontroller and microprocessor. These TLC have limitations because it uses the pre-defined hardware, which is functioning according to the program that does not have the flexibility of modification on real time basis. Due to the fixed time intervals of green, orange and red signals the waiting time is more and car uses more fuel. To make traffic light controlling more efficient, we exploit the emergence of new technique called as "Intelligent traffic light controller". This makes the use of Sensor Networks along with Embedded Technology. The timings of Red, Green lights at each crossing of road will be intelligently decided based on the total traffic on all adjacent roads. Thus, optimization of traffic light switching increases road capacity and traffic flow, and can prevent traffic congestions. GSM cell phone interface is also provided for users those who wish to obtain the latest position of traffic on congested roads. This is a unique feature of this project which is very useful to car drivers to take an alternate route in case of congestion. The various performance evaluation criteria are average waiting time, average distance traveled by vehicles, switching frequency of green light at a junction, efficient emergency mode operation and satisfactory operation of SMS using GSM Mobile. The performance of the Intelligent Traffic Light Controller is compared with the Fixed Mode Traffic Light Controller. It is observed that the proposed Intelligent Traffic Light Controller is more efficient than the conventional controller in respect of less waiting time, more distance traveled by average vehicles and efficient operation during emergency mode and GSM interface. Moreover, the designed system has simple architecture, fast response time, user friendliness and scope for further expansion.

The escalating global population and increased vehicle usage have worsened traffic congestion in metropolitan areas, a significant urban challenge. Addressing this, adaptive traffic light control methods, especially at intersections, are being developed to improve traffic flow and reduce waiting times. This study significantly contributes to this field by implementing Fuzzy Logic in intelligent traffic light systems, focusing on Ankara's Polatlı Refik Cesur intersection. Using the SUMO simulation platform and Python programming, it analyzed waiting times and queue lengths. The initial phase used queue length for each intersection arm as an input. Fuzzy logic rules then determined the output, prioritizing street or phase order for optimal flow. The study further proposed an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control plan. ANFIS merges neural network capabilities with fuzzy logic, using waiting time and queue length as inputs to regulate the green light duration. Compared to existing traffic systems, this model showed a substantial improvement. It achieved a 36.5% reduction in waiting times, underlining the efficiency of the Fuzzy Logic-based method. This approach not only enhances traffic management but also contributes significantly to the literature on intelligent traffic light control systems. By addressing key urban traffic issues, the study paves the way for future advancements in traffic management technologies. The findings highlight the potential of combining advanced computational methods, like ANFIS, with traditional traffic control techniques to optimize urban traffic flow, offering a blueprint for similar challenges in other metropolitan areas.

VANET-Based Intelligent Traffic Light Control System by Detecting Congestion Using Fuzzy C-Means Clustering Technique in a Smart City

The traffic lights also referred to as traffic signals are used to provide assistance in controlling city traffics and streamlining the present traffic situations. The traffic lights are used since 1868. They are employed by traffic sergeant to avoid congestion and to concern about the right flow of traffic in cities. As years passed, these lights are evolved with automatic controls, pedestrian supporters and so on. There are several standards followed in implementation and design. The intelligent traffic light controller is designed to develop a traffic signal based on avoidance of traffic congestion and also innovative control to clear the traffic for emergency vehicle. The ambulance would have a transmitter within it, which would transmit a signal to the receiver side, this will enable green signal in the corresponding pole. The light of the signal is controlled by automatic timers which are built in by discrete components. Arduino boards are used to control the automatic timers. This module helps to save the lives of many people as well as to avoid congestion.

Traffic congestion has been causing many setbacks and challenges in the major developing and developed smart cities in India and all over the world. The typical traffic light sequence works on the specific switching of Red, Green and Yellow lights in a particular way with stipulated time irrespective of peak traffic hour and situation, this makes typical traffic light sequence method inefficient for present situation where density of traffic is exponentially increasing over the time. In this work, we propose and develop a design of Intelligent Traffic Light Control System to manage the road traffic according to the real time traffic situation. This work is intended to propose methods of dynamic time allocation for traffic lights according to traffic density and situation. In this paper, we acquired real time traffic density using image sensors and obstacle detector, where according to the traffic density, traffic controller dynamically generates “ON” and “OFF” time of traffic light sequence. The EDGE Spartan 6 FPGA (Field Programmable Gate Array) development board is programmed as controller to implement the proposed work and MATLAB programming in used to capture real time traffic image and generate dynamic time for the traffic light sequence to control the traffic situation in real time.

Unsignalized intersection Jl. Raya Cangkiran - Jl Cangkiran Gunung Pati, Tambangan kec.Mijen, Semarang is a three-way intersection that has access to the Cupan Terminal. Many people pass through this intersection. In conditions at these intersections, traffic jams occur during rush hours, and there is no traffic light which makes traffic conditions even more irregular, the safety of motorists is also threatened. Therefore, this study was carried out to analyze unsignalized intersections, planning the use of traffic lights, and knowing the traffic characteristics of Jl. Raya Cangkiran - Jl. Gunungpati Cup, Semarang. Data collection was carried out for 3 days, namely Saturday 25 February 2023, Monday 27 February 2023, and Wednesday, 1 March 2023 by taking peak hours at 06.00 WIB - 08.00 WIB, 12.00 WIB - 13.00 WIB, and 16.00 WIB - 18.00 WIB. Data collection methods used are capacity, degree of saturation, delays, queue opportunities, and Traffic Signaling Devices (APILL). The performance analysis of this unsignalized intersection uses the 1997 Indonesian Highway Capacity Manual (MKJI) to calculate the degree of saturation. As for the level of service using the Minister of Transportation Regulation No. 96 PM 2015. The results of the research are that in the existing conditions, alternative B is chosen as the best alternative for planning traffic lights with 2 phases of the 1-hour peak scenario on Saturday morning with a degree of saturation of 0.823 pcu/hour, a queue length of 103,045 m on the West approach, and a value delay of 14,784 veh/sec. From the results of the analysis, it can be concluded that the results of calculating the traffic evaluation for the current conditions obtained that the highest vehicle volume occurred on Monday, 27 February 2023. Then Jl. Raya Cangkiran – Boja, Semarang, fulfills the criteria for installing traffic lights and needs to install traffic lights or traffic control lights. As well as from the 3 planned alternatives, alternative B was chosen to install a 2-phase traffic signal light.

Traffic congestion has become a big problem for most people because it increases noise, air pollution, and wasting time. Current normal traffic light system is not enough to manage the traffic problematic congestions because they operate on a fixed-time length plan. In recent years, internet of things led to introducing new models of intelligent traffic light systems; by utilizing different techniques such as predictive-based model, radiofrequency identification, and ultrasonic-based model. The most essential one of these techniques is depends of image processing and microcontroller communications. In this paper, we propose an intelligent, low cost, and efficient microcontroller circuit-based system for controlling cars in traffic light. This system can manage car traffics smarter than traditional approaches, it is capable to dynamically adjust timings of traffic signal. It can rapidly respond to traffic conditions to reduce traffic congestion. For implementing this system, a server, microcontroller board, cameras, as hardware and wireless network between traffic lights as infrastructure for communication are used. The system uses machine learning technique (i.e.,Yolov3 model and OpenCV) for decision depending on existence of emergency cars and number of cars. The experiment results show higher accuracy in managing traffic lights and recognizing the emergency cars.

Using the improved NaSch traffic model, we simulate the traffic flow on the crossroad under open boundary condition. The simulations are controled by ordinary traffic light and intelligent traffic light respectively. The results show that the average speed and the traffic flux under control of the later are greater than that controled by the former. Moreover, the vehicles controled by intelligent traffic light can cut down the unnecessary waiting time.

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.

As urbanization grows and the cost of vehicle production decreases, urban traffic has become a major problem of modern life. Developing intelligent vehicles alongside standardizing inter-vehicle communications promises that this technology will be a part of future life. In this research, we propose a method in which clustering is used to gather vehicles' movements information in a vehicle ad-hoc network. This method is based on extending green wave using road-side units as a fixed agent and on board units (OBU) in vehicles as a mobile agent. This information is then transmitted to traffic lights for decision making. This algorithm evaluated using Monte Carlo simulation. The simulation results show this method has a positive effect on reducing the average waiting time and overall stop of the vehicles behind the traffic lights.