Controlling traffic flow in multilane-isolated intersection using ANFIS approach techniques

Many controllers have applied the Adaptive Neural-Fuzzy Inference System (ANFIS) concept for optimizing the controller performance. However, there are less traffic signal controllers developed using the ANFIS concept. ANFIS traffic signal controller with its fuzzy rule base and its ability to learn from a set of sample data could improve the performance of Existing traffic signal controlling system to reduce traffic congestions at most of the busy traffic intersections in city such as Kuala Lumpur, Malaysia. The aim of this research is to develop an ANFIS traffic signals controller for multilane-isolated four approaches intersections in order to ease traffic congestions at traffic intersections. The new concept to generate sample data for ANFIS training is introduced in this research. The sample data is generated based on fuzzy rules and can be analysed using tree diagram. This controller is simulated on multilane-isolated traffic intersection model developed using M/M/1 queuing theory and its performance in terms of average waiting time, queue length and delay time are compared with traditional controllers and fuzzy controller. Simulation result shows that the average waiting time, queue length, and delay time of ANFIS traffic signal controller are the lowest as compared to the other three controllers. In conclusion, the efficiency and performance of ANFIS controller are much better than that of fuzzy and traditional controllers in different traffic volumes

MRDPGA: a multiple restart dynamic population genetic algorithm for scheduling road traffic

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Computational intelligence-based traffic signal timing optimization

 Traffic congestion has explicit effects on productivity and efficiency, as well as side effects on environmental sustainability and health. Controlling traffic flows at intersections is recognized as a beneficial technique, to decrease daily travel times. This thesis applies computational intelligence to optimize traffic signals\u27 timing and reduce urban traffic

Dynamic Traffic Light System to Reduce The Waiting Time of Emergency Vehicles at Intersections within IoT Environment

Traditional traffic light system, which works based on fixed cycle can be a main reason for traffic jam, due to lack of adaptation to road conditions. Traffic jam has a bad impact on drivers and road users due to the time delay it causes for road users to reach their destinations. This delay can cause a life threat in case of emergency vehicles, such as ambulance vehicles and police cars. One key solution to solve traffic jam on intersections is the dynamic traffic lights, where traffic light operation adapts based on the intersection traffic conditions. Since few of researches projects in the literature interested in solving traffic jam problem for emergency vehicles, the contribution of this paper is to introduces a novel approach to operate traffic light system. The new approach consists of two algorithms which are pure operation mode and hybrid operation mode. These operation modes aim to reduce the waiting time of emergency vehicles on traffic intersections. They assume that there is a smart infrastructure system uses Internet of Things (IoT) that can detect emergency vehicles arrival to an intersection. The smart infrastructure system switches traffic light operation from fixed cycle mode to dynamic mode. The dynamic mode manages traffic lights at intersections to reduce the waiting time of emergency vehicles. The paper presents a simulation of the proposed algorithms, highlights their advantages. In order to evaluate the efficiency of the new technique, we compared our approach with Wen algorithm in the literature and the Traditional traffic light system. Our evaluation study indicated that the proposed algorithms outperformed Wen technique and the Traditional system under different traffic scenario

A smart traffic light using a microcontroller based on the fuzzy logic

Traffic jam that is resulted from the buildup of vehicles on the road has become an important problem, which leads to an interference with drivers. The impacts it has on cost and time effectiveness may take the form of increased fuel consumption, traffic emissions, and noise. This paper offers a solution by creating a smart traffic light using a fuzzy-logic-based microcontroller for a greater adaptability of the traffic light to the dynamics of the vehicles that are to cross the intersection. The ATMega2560 microcontroller-based smart traffic light is designed to create a breakthrough in the breakdown of congestions at road junctions, thereby optimizing the real-time happenings in the road. Ultrasonic, infrared, and light sensors are used in this smart traffic light, resulting in the smart traffic light’s effectiveness in parsing jams. The four sets of sensors that are placed in four sections determine the traffic light timing process. When the length of vehicle queue reaches the sensor, a signal is sent as the microcontroller’s digital input. Ultrasonic and infrared sensors can reduce congestions at traffic lights by giving a green light time when one or all of the sensors are active so that the vehicle congestions can be relieved

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

Intelligent traffic control at signalized intersections in urban areas is vital for mitigating congestion and ensuring sustainable traffic operations. Poor traffic management at road intersections may lead to numerous issues such as increased fuel consumption, high emissions, low travel speeds, excessive delays, and vehicular stops. The methods employed for traffic signal control play a crucial role in evaluating the quality of traffic operations. Existing literature is abundant, with studies focusing on applying regression and probability-based methods for traffic light control. However, these methods have several shortcomings and can not be relied on for heterogeneous traffic conditions in complex urban networks. With rapid advances in communication and information technologies in recent years, various metaheuristics-based techniques have emerged on the horizon of signal control optimization for real-time intelligent traffic management. This study critically reviews the latest advancements in swarm intelligence and evolutionary techniques applied to traffic control and optimization in urban networks. The surveyed literature is classified according to the nature of the metaheuristic used, considered optimization objectives, and signal control parameters. The pros and cons of each method are also highlighted. The study provides current challenges, prospects, and outlook for future research based on gaps identified through a comprehensive literature review

Optimisasi Koloni Semut dan Sistem Fuzzy untuk Kendali Lampu Lalu Lintas Pintar

Pola pengaturan lampu lalu lintas waktu tetap yang tidak mempertimbangkan kondisi aktual persimpangan bisa menimbulkan kemacetan. Kemacetan dapat menyebabkan banyak kerugian, diantaranya yaitu banyaknya waktu terbuang dan bahan bakar yang habis dengan sia- sia.  Masalah ini dapat diatasi dengan  pengatur lampu lalu lintas pintar yaitu sebuah sistem pengatur lampu lalu lintas yang mampu beradaptasi dengan kondisi setiap ruas jalan pada persimpangan. Pada penelitian ini telah dilakukan pengembangan sistem pengatur lampu lalu lintas pintar berbasis pada logika fuzzy bertingkat dan algoritma optimisasi koloni semut (Ant Colony Optimization). Pada Logika Fuzzy Bertingkat, keluaran dari sistem logika fuzzy tahap pertama menjadi masukan ke sistem logika fuzzy tahap berikutnya. Keluaran dari Sistem Fuzzy adalah menentukan skala prioritas untuk fase hijau berikutnya. Selanjutnya algoritma optimisasi koloni semut melakukan perhitungan waktu hijau yang optimal pada fase tersebut. Berdasarkan hasil simulasi yang dilakukan diperoleh bahwa dengan menggunakan sistem pengatur lampu lalu lintas pintar dibanding dengan sistem pengatur lampu lalu lintas waktu tetap terjadi pengurangan panjang antrian kendaraan dan waktu tunggu kendaraan.Fixed time traffic light control is a traffic light control system that does not take into account the actual conditions of the intersection, which can cause congestion. Congestion can cause a lot of losses, including a lot of wasted time and wasted fuel. This problem can be solved with a smart traffic light controller, which is a traffic light control system that is able to adapt to the conditions of each road section at the intersection. In this research, the development of smart traffic light control based on multi stage fuzzy logic and ant colony optimization (ACO) algorithm has been carried out. In multi stage fuzzy logic, the output of the first stage of the fuzzy logic becomes the input to the next stage of the fuzzy logic. The output of the fuzzy system is to determine the priority scale for the next green phase. Furthermore, Ant Colony Optimization calculates the optimal green time in that phase. Based on the simulation result, it is found that by using  smart traffic light control system compared to a fixed time traffic light control system, there  is a reduction in queue length and waiting time

Pengendalian Lampu Lalu Lintas Cerdas di Persimpangan Empat Ruas yang Kompleks Menggunakan Algoritma Adaptive Neuro Fuzzy Inference System

Masalah transportasi masih sering dihadapkan pada fenomena kemacetan arus lalu lintas yang berdampak pada kecelakaan lalu lintas, polusi, dan kerugian ekonomi. Salah satu cara untuk meminimalisir fenomena tersebut melalui pengendalian sistem lampu lalu lintas yang baik terhadap arus lalu lintas jangka pendek di persimpangan jalan. Pengendalian lampu lalu lintas secara statis terbukti belum optimal dalam meminimalisir kemacetan arus lalu lintas, salah satu penyebabnya karena kondisi arus lalu lintas yang bervariasi sehingga tidak mudah diprediksi. Fuzzy Inference System (FIS) sering terbukti mampu menunjukkan hasil yang lebih baik daripada pengendalian lampu lalu lintas secara statis. Namun FIS tidak dapat diterapkan pada kondisi arus lalu lintas yang bervariasi atau di persimpangan jalan yang berbeda karena metode tersebut tidak mampu mempelajari kondisi arus lalu lintas secara real time. Agar FIS mampu melakukan pembelajaran, maka pendekatan machine learning dapat diterapkan pada FIS. Salah satu pengembangannya adalah Adaptive Neuro Fuzzy Inference System (ANFIS) yang dapat mengendalikan lampu lalu lintas cerdas secara dinamis dengan hasil yang lebih baik daripada FIS. Namun umumnya ANFIS diuji coba pada persimpangan jalan yang normal. Bagaimana jika di persimpangan yang kompleks? Persimpangan yang memiliki beberapa ruas/jalur utama yang besar (jalur poros), sementara ruas laiinya kecil, bahkan terdapat ruas yang tidak berpotongan, sehingga ada prioritas untuk setiap ruasnya. Hasilnya, penerapan ANFIS (3 GaussMf) untuk pengendalian lampu lalu lintas cerdas/dinamis di persimpangan empat ruas yang kompleks mampu mereduksi Average Waiting Times (AWT) rata-rata sebesar 3,4071E-05 detik dengan 2,7156 RMSE rata-rata, menggunakan variabel Queues Quantity dan Priority Degree. Sedangkan jika menggunakan variabel Arrival Times, Transportation Type, dan Goal Junction, ANFIS (4 TrapMf) mampu mereduksi AWT sebesar 0,0779 detik dengan 19,7646 RMSE