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

Traffic Signal Controller Optimization Through VISSIM to Minimize Traffic Congestion, CO and NOx Emissions, and Fuel Consumption

In developing countries with heterogeneous traffic, such as Sri Lanka, it is possible to observe severe traffic congestion at intersections and traffic corridors. The main objective of this study was to demonstrate the optimization of traffic signal controllers using VISSIM microsimulation software. It aimed to minimize traffic congestion, emissions, and fuel consumption. This study focused on developing a traffic signal controller optimization program for a congested traffic corridor which consisted of a three-legged signalized intersection, a four-legged unsignalized intersection, and a three-legged unsignalized intersection. The entire corridor was modeled here, and the already signalized three-legged intersection was optimized. Traffic signal controller optimization was done separately through the built-in optimization features in VISSIM and Webster’s Method. The results showed that emissions and fuel consumption were reduced by 14.89 % in VISSIM optimization and 14.11% in optimization using Webster’s Method. Through the comparison between the VISSIM optimized signal timing and manually calculated signal timing, it was found that the signal timing optimization provides much more improved results than the manual signal timing calculations. Using the proposed methodology, the traffic signal controllers can be optimized within a short duration in very few steps without any iterations compared to the existing traffic signal controller optimization techniques. Therefore, the proposed methodology is a good alternative method to optimize the traffic signal controllers

Urban Navigation Handling Openstreetmap Data for an Easy to Drive Route

Atualmente, os cidadãos podem escolher as suas opções de viagem com base no tempo, distância, emissões, consumo, entre outros parâmetros. Não obstante, a literatura indica que os sistemas de planeamento de rotas atuais têm, maioritariamente, por base a distância e o tempo. Com efeito, verificou-se uma falta de sistemas de planeamento de rotas que se preocupem com as preferências dos utilizadores num ponto de vista mais qualitativo. Este projeto de investigação desenvolve um framework de planeamento de rotas com a integração de diferentes atributos da rede rodoviária como semáforos, passadeiras e paragens de autocarro, com o objetivo de providenciar aos utilizadores a opção de evitar estes mesmos atributos, oferecendo uma opção easy drive, nomeadamente em ambiente urbano. O estudo foi conduzido através de dados georreferenciados da cidade de Lisboa, Portugal. No entanto, é transferível para qualquer outra cidade. O algoritmo providencia alternativas para a rota mais curta, easy drive e rota balanceada, considerando apenas um modo de viagem: carro/mota. O modelo foi desenvolvido no PostgreSQL com a extensão PostGIS e PgRouting, e os resultados foram visualizados no software QGIS. O software permite customizar pesos para cada uma das restrições para a escolha das rotas e estes pesos são modificados com o objetivo de encontrar o caminho ótimo consoante as preferências de cada utilizador.Currently, citizens can choose their travel options based on time, distance, consumption, emission, among other parameters. Nevertheless, the literature indicates that current route planning systems are based on distance and time. In fact, there is a lack of route planning systems which are concerned with users' preferences from a more qualitative point of view. This research project develops a route planning framework with the integration of different road network features like traffic lights, pedestrian crossings, and bus stops, to provide users with the option to avoid these features, offering an easy drive option, namely in an urban environment. The study was conducted using georeferenced data from the city of Lisbon, Portugal. However, it is transferable to any other city. The algorithm provides alternatives for the shortest route, easy drive, and balanced route, considering only one travel mode: car/motorbike. The model was developed in PostgreSQL with the PostGIS extension and PgRouting, and the results were visualized in QGIS software. The software allows to custom weights for each of the constraints for route choices, and these weights are modified to find the optimal route according to the preferences of each user

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

Road network equilibrium approaches to environmental sustainability

Environmental sustainability is closely related to transportation, especially to the road network, because vehicle emissions and noise damage the environment and have adverse effects on human health. It is, therefore, important to take their effect into account when designing and managing road networks. Road network equilibrium approaches have been used to estimate this impact and to design and manage road networks accordingly. However, no comprehensive review has summarized the applications of these approaches to the design and management of road networks that explicitly address environmental concerns. More importantly, it is necessary to identify this gap in the literature so that future research can improve the existing methodologies. Hence, this paper summarizes these applications and identifies potential future research directions in terms of theories, modelling approaches, algorithms, analyses, and applications.postprin

Study of Full Controlled Green Time Roundabouts – An Intelligent Approach

When roundabouts face congestion problems, the transition to signalised roundabouts is considered a solution to the problem. The majority of studies have concentrated on how to calculate the optimal cycle length and signal timing to minimise congestion at roundabouts. To date, intelligence algorithms with multi-objectives such as queue length, number of stops, delay time, capacity and so on are widely used for calculating signal timing. Although roundabout congestion can be generated by the weaving zone reducing roundabout capacity, there have been minimal studies which take into account the density in the weaving zone. This study proposed a hybrid gravitational search algorithm – ABFO random forest regression with the following objectives: density, delay time and capacity to find the optimal cycle length and green time in each phase of Changwon city hall roundabout in South Korea as a case study. The optimal cycle length and green time were calculated in MATLAB and microscopic simulation VISSIM sought the effectiveness of a signalised roundabout. The result of the analysis demonstrated that signalised roundabouts with 102 seconds cycle length (phase 1 – 65 seconds of green time and phase 2 – 37 seconds of green time) can reduce density by 46.1%, delays by 32.8% and increase roundabout capacity by 14.8%