On tuning the particle swarm optimization for solving the traffic light problem

In everyday routines, there are multiple situations of high traffic congestion, especially in large cities. Traffic light timed regulated intersections are one of the solutions used to improve traffic flow without the need for large-scale and costly infrastructure changes. A specific situation where traffic lights are used is on single-lane roads, often found on roads under maintenance, narrow roads or bridges where it is impossible to have two lanes. In this paper, a simulation-optimization strategy is tested for this scenario. A Particle Swarm Optimization algorithm is used to find the optimal solution to the traffic light timing problem in order to reduce the waiting times for crossing the lane in a simulated vehicle system. To assess vehicle waiting times, a network is implemented using the Simulation of Urban MObility software. The performance of the PSO is analyzed by testing different parameters of the algorithm in solving the optimization problem. The results of the traffic light time optimization show that the proposed methodology is able to obtain a decrease of almost 26% in the average waiting times.This work has been supported by FCT-Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020 and the project “Integrated and Innovative Solutions for the well-being of people in complex urban centers” within the Project Scope NORTE-01-0145-FEDER-000086

A bi-level model of dynamic traffic signal control with continuum approximation

This paper proposes a bi-level model for traffic network signal control, which is formulated as a dynamic Stackelberg game and solved as a mathematical program with equilibrium constraints (MPEC). The lower-level problem is a dynamic user equilibrium (DUE) with embedded dynamic network loading (DNL) sub-problem based on the LWR model (Lighthill and Whitham, 1955; Richards, 1956). The upper-level decision variables are (time-varying) signal green splits with the objective of minimizing network-wide travel cost. Unlike most existing literature which mainly use an on-and-off (binary) representation of the signal controls, we employ a continuum signal model recently proposed and analyzed in Han et al. (2014), which aims at describing and predicting the aggregate behavior that exists at signalized intersections without relying on distinct signal phases. Advantages of this continuum signal model include fewer integer variables, less restrictive constraints on the time steps, and higher decision resolution. It simplifies the modeling representation of large-scale urban traffic networks with the benefit of improved computational efficiency in simulation or optimization. We present, for the LWR-based DNL model that explicitly captures vehicle spillback, an in-depth study on the implementation of the continuum signal model, as its approximation accuracy depends on a number of factors and may deteriorate greatly under certain conditions. The proposed MPEC is solved on two test networks with three metaheuristic methods. Parallel computing is employed to significantly accelerate the solution procedure

Evolutionary design optimization of traffic signals applied to Quito city

This work applies evolutionary computation and machine learning methods to study the transportation system of Quito from a design optimization perspective. It couples an evolutionary algorithm with a microscopic transport simulator and uses the outcome of the optimization process to deepen our understanding of the problem and gain knowledge about the system. The work focuses on the optimization of a large number of traffic lights deployed on a wide area of the city and studies their impact on travel time, emissions and fuel consumption. An evolutionary algorithm with specialized mutation operators is proposed to search effectively in large decision spaces, evolving small populations for a short number of generations. The effects of the operators combined with a varying mutation schedule are studied, and an analysis of the parameters of the algorithm is also included. In addition, hierarchical clustering is performed on the best solutions found in several runs of the algorithm. An analysis of signal clusters and their geolocation, estimation of fuel consumption, spatial analysis of emissions, and an analysis of signal coordination provide an overall picture of the systemic effects of the optimization process

A multi-agent traffic simulation framework for evaluating the impact of traffic lights

This is an electronic version of the paper presented at the 3rd International Conference on Agents and Artificial Intelligence, held in Rome on 2011The growing of the number of vehicles cause serious strains on road infrastructures. Traffic jams inevitably occur, wasting time and money for both cities and their drivers. To mitigate this problem, traffic simulation tools based on multiagent techniques can be used to quickly prototype potentially problematic scenarios to better understand their inherent causes. This work centers around the effects of traffic light configuration on the flow of vehicles in a road network. To do so, a Multi-Agent Traffic Simulation Framework based on Particle Swarm Optimization techniques has been designed and implemented. Experimental results from this framework show an improvement in the average speed obtained by traffic controlled by adaptive over static traffic lights.This work has been supported by the Spanish Ministry of Science and Innovation. Grant TIN2010- 1987

Benchmark for Tuning Metaheuristic Optimization Technique to Optimize Traffic Light Signals Timing

Traffic congestion at intersections is an international problem in the cities. This problem causes more waiting time, air pollution, petrol consumption, stress of people and healthy problems. Against this background, this research presents a benchmark iterative approach for optimal use of the metaheuristic optimization techniques to optimize the traffic light signals timing problem. A good control of the traffic light signals timing on road networks may help in solving the traffic congestion problems. The aim of this research is to identify the most suitable metaheuristic optimization technique to optimize the traffic light signals timing problem, thus reducing average travel time (ATT) for each vehicle, waiting time, petrol consumption by vehicles and air pollution to the lowest possible level/degree. The central part of Nablus road network has a huge traffic congestion at the traffic light signals. It was selected as a research case study and was represented by the SUMO simulator. The researcher used a random algorithm and three different metaheuristic optimization techniques: three types of Genetic Algorithm (GA), Particle Swarm Algorithm (PS) and five types of Tabu Search Algorithm (TS). Parameters in each metaheuristic algorithm affect the efficiency of the algorithm in finding the optimal solutions. The best values of these parameters are difficult to be determined; their values were assumed in the previous traffic light signals timing optimization research. The efficiency of the metaheuristic algorithm cannot be ascertained of being good or bad. Therefore, the values of these parameters need a tuning process but this cannot be done by using SUMO simulator because of its heavy computation. The researcher used a benchmark iterative approach to tune the values of them etaheuristic algorithm parameters by using a benchmark function. The chosen function has similar characteristics to the traffic light signals timing problem. Then, through the use of this approach, the researcher arrived at the optimal use of the metaheuristic optimization algorithms to optimize traffic light signals timing problem. The efficiency of each metaheuristic optimization algorithm, tested in this research, is in finding the optimal or near optimal solution after using the benchmark iterative approach. The results of metaheuristic optimization algorithm improved at some values of the tuned parameters. The researcher validated the research results by comparing average results of the metaheuristic algorithms, used in solving the traffic light signals optimization problem after using benchmark iterative approach, with the average results of the same metaheuristic algorithms used before using the benchmark iterative approach; they were also compared with the results of Webster, HCM methods and SYNCHRO simulator. In the light of these study findings, the researcher recommends trying the benchmark iterative approach to get ore efficient solutions which are very close to the optimal solution for the traffic light signals timing optimization problem and many complex practical optimization problems that we face in real life.الازدحامات المرورية عند التقاطعات هي مشكله عالمية في المدن. هذه المشكلة تسبب المزيد من وقت االنتظار وتلوث الهواء و استهالك الوقود، و توتر الناس و مشاكل صحية. على هذه الخلفية، يقدم هذا البحث نهج المعيار المكرر لالستخدام تقنيات التحسين التخمينية في تحسين مشكلة توقيت اإلشارات الضوئية. التحكم الجيد في توقيت االشارات الضوئية على شبكات الطرق قد يساعد في حل مشاكل االزدحام المروري. يهدف هذا البحث الى تحديد أفضل و أنسب تقنية تحسين تخمينية لتحسين مشكلة توقيت االشارات الضوئية، وبالتالي تقليل متوسط الوقت الذي يستغرقه السفر (ATT(لكل مركبة، و وقت االنتظار، و استهالك الوقود المستخدم في المركبات و تلوث الهواء إلى أدنى مستوى ممكن. يعاني الجزء المركزي من شبكة طرق مدينة نابلس من ازدحام مروري كبير على االشارات الضوئية. و تم اختيار هذا الجزء كحالة البحث الدراسية و التي تم تمثيلها باستخدام برنامج المحاكاة سومو. و استخدم الباحث خوارزمية عشوائية و ثالث تقنيات تحسين تخمينية و هي: ثالث انواع من الخوارزمية الجينية، و خورزمية سرب الجسيمات، و خمسة انواع من خوارزمية التابو. و هناك متغيرات في كل خوارزمية تخمينية تؤثر على فعالية الخوارمية في ايجاد الحلول المثلى. و من الصعب تحديد افضل القيم لهذه المتغيرات؛ و قيم هذه المتغيرات كانت تفترض في ابحات تحسين توقيت االشارات الضوئية السابقة. وفي هذه الحاله فعالية اقتران التحسين التخميني ال يمكن التحقق منها اذا ما كانت جيده او سيئة. ولذلك فان قيم هذه المتغيرات بحاجه لعملية ضبط ، ولكن ال يمكننا ذلك باستخدام برنامج المحاكاه سومو النه حساباته ثقيله و طويله. استخدم الباحث طريقة مقارنة الدوال لضبط قيم متغيرات خوارزمية التحسين التخمينية باستخدام خوارزمية معيار. خوارمية المعيار المختاره لها خصائص شبيهه بمشكلة توقيت االشارات الضوئية. ثم من خالل استخدام هذه الطريقة، وصل الباحث الى افضل استخدام لخوارزميات التحسين التخمينية لتحسين مشكلة توقيت االشارات االضوئية. وفي هذا البحث تم اختبار فعالية كل خوارمية تحسين تخمينية في ايجاد الحل االمثل او حل قريب من الحل االمثل بعد ضبط خوارزمية التحسين التخمينية. لقد تحسنت نتائج خوارزمية التحسين التخمينية عند بعض قيم المتغيرات التي تم ضبطها. قام الباحث بالتحقق من نتائج البحث بمقارنة معدل نتائج خوارزميات التحسين التخمينية التي امستخدمها في تحسين مشكلة توقيت االشارات الضوئية قبل ضبط خوارزمية التحسين التخمينية، مع معدل نتائج نفس الخوارزميات التخمينية التي امستخدمها بعد ضبط خوارزمية التحسين التخمينية؛ وهذه النتائج تمت مقارنتها مع نتائج طريقتي ويبستر و HCM و برنامج السنكرو. في ضوء نتائج هذه الدراسة، يوصي الباحث بتجريب طريقة مقارنة الدوال لضبط خوارزميات التحسين التخمينية للحصول على حلول فعالة اكثر و التي تكون قريبة جدا من الحل االمثل لتحسين مشكلة توقيت االشارات الضوئية و لتحسين المشاكل العملية المعقدة التي تواجهنا في الحياة العملية

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