Traffic lights synchronization for Bus Rapid Transit using a parallel evolutionary algorithm

This article presents a parallel evolutionary algorithm for public transport optimization by synchronizing traffic lights in the context of Bus Rapid Transit systems. The related optimization problem is NP-hard, so exact computational methods are not useful to solve real-world instances. Our research introduces a parallel evolutionary algorithm to efficiently configure and synchronize traffic lights and improve the average speed of buses and other vehicles. The Bus Rapid Transit on Garzón Avenue (Montevideo, Uruguay) is used as a case study. This is an interesting complex urban scenario due to the number of crossings, streets, and traffic lights in the zone. The experimental analysis compares the numerical results computed by the parallel evolutionary algorithm with a scenario that models the current reality. The results show that the proposed evolutionary algorithm achieves better quality of service when compared with the current reality, improving up to 15.3% the average bus speed and 24.8% the average speed of other vehicles. A multiobjective optimization analysis also demonstrates that additional improvements can be achieved by assigning different priorities to buses and other vehicles. In addition, further improvements can be achieved on a modified scenario simply by deleting a few bus stops and changing some traffic lights rules. The benefits of using a parallel solver are also highlighted, as the parallel version is able to accelerate the execution times up to 26.9× when compared with the sequential version. Keywords: Bus Rapid Transit, Traffic lights synchronization, Evolutionary algorithm Document type: Articl

Traffic lights synchronization for Bus Rapid Transit using a parallel evolutionary algorithm

This article presents a parallel evolutionary algorithm for public transport optimization by synchronizing traffic lights in the context of Bus Rapid Transit systems. The related optimization problem is NP-hard, so exact computational methods are not useful to solve real-world instances. Our research introduces a parallel evolutionary algorithm to efficiently configure and synchronize traffic lights and improve the average speed of buses and other vehicles. The Bus Rapid Transit on Garzón Avenue (Montevideo, Uruguay) is used as a case study. This is an interesting complex urban scenario due to the number of crossings, streets, and traffic lights in the zone. The experimental analysis compares the numerical results computed by the parallel evolutionary algorithm with a scenario that models the current reality. The results show that the proposed evolutionary algorithm achieves better quality of service when compared with the current reality, improving up to 15.3% the average bus speed and 24.8% the average speed of other vehicles. A multiobjective optimization analysis also demonstrates that additional improvements can be achieved by assigning different priorities to buses and other vehicles. In addition, further improvements can be achieved on a modified scenario simply by deleting a few bus stops and changing some traffic lights rules. The benefits of using a parallel solver are also highlighted, as the parallel version is able to accelerate the execution times up to 26.9x when compared with the sequential version

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

Impact of Stoplight Policies on Urban Traffic System Emissions

With increasing urban populations (Boyd 2015), also comes higher land resource demands and more concentrated pollution levels. Increasing the efficiency of transportation systems is key to fostering sustainable urban development because it can decrease urban pollution levels, better meet the social demands of its residents, and decrease vehicle fuel consumption – encompassing the three pillars of sustainability: social, environment, and economic. This study aims to better understand the environmental impact of traffic light coordination and their timing policies in a randomly generated traffic network on an urban area. We focus on the effect of three different stoplight policies on emission and congestion. Transportation is a basic human need and an area of sustainable development with great potential. The United Nations states that achieving sustainable transportation is a key component in the development of sustainable cities across the world (United Nations 2016). The number of vehicles on the roads are increasing, as are their emission levels (Environmental Protection Agency 2017). In the United States alone, over 263 million vehicles were registered by 2015 (Bureau Of Transportation Statistics 2015). The coordination of stoplight timing has the potential to mitigate not only traffic-related congestion, but also traffic-related emissions. An acceleration following either a deceleration or a complete stop due to a red light that has turned green is responsible for significantly more carbon dioxide (CO2) emissions than cruising at a constant speed when approaching a green light (Ericsson 2001). Congestion decreases the fuel efficiency of all vehicle types on the road (Bigazzi, Clifton and Gregor 2014). With decreased fuel efficiency comes increased CO2 emissions (Barth et al. 2007). Stoplights are responsible for much of vehicles’ halts and changes in acceleration. Depending on the sequences of the traffic lights and the flow of traffic, the lights can both cause and relieve congestion, especially in large cities because of the high concentration of intersections with traffic lights. Just in New York City there were 12,460 recorded intersections that were stoplight-regulated (NYC DOT 2012), and out of the 3,360 intersections found in the Manhattan borough (Howe 2010), 2,820 of them are operated by traffic lights (NYC DOT 2012). In this study, three stoplight timing policies are assessed for their effect on vehicle emissions for random traffic scenarios on a given downtown area. Without loss of generality, vehicle emissions are assumed to be primarily caused by acceleration and deceleration. Traffic is modeled using a variation of a cell transition model to capture traffic as density-dependent flows (Daganzo 1995). To block any secondary effects in the system, all vehicles are assumed to have identical characteristics. This study assumes 100% penetration of vehicle autonomy, or in other words driver-less vehicles. It also assumes vehicle-to-infrastructure (V2X) technology, which includes vehicle-to-vehicle (V2V) communication of the vehicles’ locations and speeds as well as communication with other network infrastructure such as traffic lights. These vehicle assumptions aid in the simplicity and accuracy of the cell transmission model application. V2X is also known for its potential to improve traffic safety and decrease idling time (Abboud, Omar and Zhuang 2016). We compare the performance, as captured on the total emission levels of traffic controlled by three stoplight timing policies: a conventionally timed traffic light system, a system with traffic flow-dependent lights, and a light-less system

Reducing vehicle emissions and fuel consumption in the city by using particle swarm optimization

Nowadays, the increasing levels of polluting emissions and fuel consumption of the road traffic in modern cities directly affect air quality, the city economy, and especially the health of citizens. Therefore, improving the efficiency of the traffic flow is a mandatory task in order to mitigate such critical problems. In this article, a Swarm Intelligence approach is proposed for the optimal scheduling of traffic lights timing programs in metropolitan areas. By doing so, the traffic flow of vehicles can be improved with the final goal global target of reducing their fuel consumption and gas emissions (CO and N O x ). In this work we optimize the traffic lights timing programs and analyze their effect in pollution by following the standard HBEFA as the traffic emission model. Specifically, we focus on two large and heterogeneous urban scenarios located in the cities of Malaga and Seville (in Spain). When compared to the traffic lights timing programs designed by experts close to real ones, the proposed strategy obtains significant reductions in terms of the emission rates (23.3 % CO and 29.3 % N O x ) and the total fuel consumption.Web of Science42340538

Dynamics in Logistics

This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions