Intelligent Traffic Light Controller: A Solution for Smart City Traffic Problem

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.

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

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

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.

A method is proposed for neural network prediction of congestion on road sections outside the control zones of transport detectors in the systems of “flexible” transport flow control at intersections with the traffic light regulation. The paper describes the operating principle of the original control system based on fuzzy logic. To develop a universal neural network solution that can be used to predict traffic on most road sections with common characteristics without the need for training for each case separately, it is proposed to identify 9 main types of sections and, accordingly, 9 neural networks. Each typical neural network differs in the amount of the input data and parameters. All neural networks are trained on samples obtained from analyzing traffic flow on road sections of each type. Typical characteristics of road sections, architecture, and parameters of neural networks are determined. Neural networks are trained and tested on the model data. Using computer simulation methods, the author assesses the control quality of the original intelligent traffic light control system, which has complete information about the transport flow (however, it is difficult to implement in practice), as well as a control system that uses a set of data obtained using transport detectors and supplemented with a neural network forecast. According to the experiment results, a system with a neural network forecast of congestion on road sections is inferior in quality of control by no more than 1.92% compared to an idealized system, which gives grounds to consider this method applicable in intelligent transport flow control systems at intersections with traffic light regulation.

This paper presents an Ethernet packet transformation and transmission architecture between Modbus transmission control protocol (Modbus/TCP) and universal serial bus (USB) 3.0 developed with a field-programmable gate array (FPGA) development board. The proposed architecture is used to complete packet transformation and transmission between Ethernet and USB 3.0 for application in plant automation. The Ethernet receiver receives and analyzes Modbus/TCP packets and sends the source address, destination address, IP header, and Modbus/TCP header to the register to verify the correctness of the packet. The Modbus/TCP packet is stored in static random access memory and awaits access by a USB 3.0 module. An FPGA development board (Intel DE10-Standard) is used for functional verification. The measured results show that the latency, throughput, and dynamic power are 18.845 ×s, 747.45 Mbps, and 142.17 mW, respectively, at a voltage of 1.8 V and operating frequency of 125 MHz.

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.

In order to quantify the degree of influence of weather on traffic situations in real time, this paper proposes a terminal traffic situation prediction model under the influence of weather (TSPM-W) based on deep learning approaches. First, a feature set for predicting traffic situations is constructed based on data such as weather, traffic demand, delay conditions, and flow control strategies. When constructing weather data, a terminal area weather quantification method (TAWQM) is proposed to quantify various weather feature values. When constructing the traffic situation label, fuzzy C-means clustering (FCM) is used to perform cluster analysis on the traffic situation, and the traffic situation is marked as bad, average, or good. Accordingly, the multi-source data is fused as the input vector, based on the combined prediction model of convolutional neural network (CNN) and gated recurrent unit (GRU), TSPM-W is constructed. Finally, based on the historical operation data of the Guangzhou Baiyun International Airport terminal area, the proposed data set is used to predict the traffic situation time series at intervals of 1 h, 3 h, and 6 h. The comparative experimental results show that the proposed time series prediction model has higher prediction accuracy than other existing prediction methods. The proposed dataset is able to more accurately predict the traffic situation in the terminal area.

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.

Insufficient capacity of the road network is a problem in most cities. This problem can be partially solved by optimizing the operation of the traffic light. Since selecting traffic light settings in real time can lead to a deterioration in the traffic situation, it is advisable to pre-configure the model and work out various traffic light operating modes on it, and select the optimal ones. The purpose of the study was to simulate changes in the average number of cars in a queue before a traffic light and the average travel time of an intersection. To achieve this goal, the following tasks are formulated: creating a model of a road section with real flows at the entrances; determining the average number of cars in a queue under different traffic light operating modes; creating an experiment to find the average time of a left turn with and without the introduction of a specialized left-turn phase of the traffic light. Materials and methods. To solve the tasks set, the method of using the AnyLogic environment for analyzing a traffic section has been tested, and the possibility of using simulation modeling to identify the dependence of the intersection's capacity on the operating mode of the traffic light has been shown. Results. In the course of the research, a model of the existing intersection was created and the dependence of the number of cars in the queue in front of the forbidding traffic light signal on the duration of its full cycle was considered. To conduct the experiment, the existing 84-second traffic light cycle was changed to 30 seconds, one, two, three, and five minutes. To visualize the obtained dependencies, a graph is constructed that reflects the number of cars in a traffic jam under different traffic light operating modes. The created model is also used to determine the dependence of the travel time of an intersection at a left turn on the number of turning cars and the number of cars in the opposite direction. Traffic light options with and without an additional left signal are considered. The results are presented in a table and graphically. Conclusion. The study showed that with a small amount of oncoming traffic, the introduction of a specialized left-turn phase of the traffic light increases the travel time of the intersection, but with an increase in oncoming traffic, the situation changes to the opposite.

Chaotic and periodic motions of two competing vehicles controlled by traffic lights

There is considered a problem of road traffic organization at crossroads under conditions of urban environment and outgoing highways. The urgency of the problem of automated traffic control with the information for vehicle drivers about traffic situation using both navigation systems and warning systems by means of signal transfer to radar-detectors is shown. 
 The investigation purpose consists in the development of automated self-regulated system of traffic lights control on the basis of vehicle number approaching to the traffic lights and vehicle driver warning about traffic jam approaching for the purpose of its going around. 
 In the paper there are solved problems regarding traffic capacity of a megapolis traffic system, driver warning about problem road situations and the automation of these processes. On the basis of the theory of automated control there is shown a developed system allowing the formation of a traffic passage by means of driver warning about traffic jam ahead. There are described thoroughly component parts of the system limiting only with the choice of components among prototypes available in the market. 
 A principle of automated traffic flow control at the crossroads supplied with the traffic lights is shown. There is shown a functional diagram of the interaction of system components for the successful traffic organization. The algorithm of the system automation functioning with its adjustment to certain time intervals of the effect of the enabling traffic light signal at a particular T-shaped or X-shaped intersection is described. The system developed for traffic control at intersections allows relieving considerably city streets. The analysis of the system has shown that traffic lights adaptive control allows decreasing traffic up to 70%. The application of domestic software components decreases system cost by 2-4 times as compared with foreign prototypes which, among other things, do not have the functional offered.

With the rapid increase in ship traffic volume, the traffic situation in some busy waters becomes more complex, bringing great challenges to the development of shipping industry. To fully understand the ship traffic situations and provide decision-making basis for maritime safety management, we propose a ship traffic risk complexity model based on complex network theory. First, a real-time ship-ship collision risk model that combines a dynamic ship domain model is developed to describe the between-ship proximity degree spatially and temporally. Then four complex indicators is selected and the Projection Pursuit Evaluation model is adopted to quantitatively evaluate the real-time traffic situations by synthesizing the multi-dimension problems to the projection value with one dimension. A case study is finally conducted using the AIS data in the outside waters of Ningbo-Zhoushan Port. Results show that the proposed model can help to identify the structural characteristics of ship traffic situations as well as detect the evolutionary characteristics of traffic situations.

In this paper, digital control of a DC-DC switching power converter using a Field Programmable Gate Array (FPGA) is presented. The FPGA system development is carried out using Very High-Speed Integrated Circuit Hardware Description Language (VHDL), whose parameters can be easily changed as per user requirement by editing the predefined generics in the VHDL code. The digital controller comprises of Xilinx Analog to Digital Converter(XADC) and Xilinx Microblaze soft processor core. The digital controller developed using the FPGA development board has been tested on classical synchronous buck and boost converter operating in continuous conduction mode (CCM).

Effect of complex traffic situations on route choice behaviour and driver stress in residential areas