Applications of multi-agent systems in transportation

Abstract

Traffic density is a growing drawback of the crowding of cities in contemporary societies. As a consequence of financial and technological innovations, the living standards of people are improving yet this increases the number of cars and traffic density accordingly. Thus, the density of traffic is reducing the quality of life for individuals in metropolitan areas in particular. Traffic is an important factor affecting human life quality in crowded cities. The increasing population and increasing individual vehicle ownership lead to an increase in traffic density. This causes an increase in loss of time and pollution. Traffic density in big cities is an important factor that reduces the quality of human life. Due to the growing population in metropolitan areas and the inadequate infrastructure to accommodate this density, traffic problems are on the rise. As a result, passengers waste more time in traffic, and the amount of emissions, and hence air pollution, also increases. The issue of traffic congestion is a significant concern for numerous metropolitan areas across the globe, as it causes delays, increases commuting time, and contributes to air pollution. Controlling the flow of traffic is problematic in terms of many complexities and uncertainties. Despite this situation, this problem needs to be solved as it reduces productivity and living standards. Modern traffic control methods offer a more effective solution, unlike traditional methods. As traffic congestion continues to increase rapidly in the world, the need to research and apply more effective methods of traffic control than the traditional method is increasing. Solving traffic congestion is one of the most important and complex problems, as it causes chaos in metropolitans, especially during heavy traffic hours. Traditional methods that continue to be used have proven to be inadequate, and as a result, the developing technology has affected all areas as well as the solutions to the traffic control problem. With the emergence of Intelligent Transportation Systems (ITS), utilizing artificial intelligence and communication technologies, a more effective and efficient solution to traffic congestion is possible. Transportation techniques are improving day by day with the pace of growing technology. Intelligent Transportation Systems (ITS) provide advanced services such as high-tech traffic controllers and various transportation modes, reducing the burden on drivers and thus enabling them to meet the need for complex decision-making while on the road. Intelligent transportation solutions have enabled an unprecedented level of data collection within the industry, leading to significant advancements in transportation system management and operation. With the increasing demand and rate of data collection, ITS has also been advancing day by day and increasing the speed of progress of smart transportation systems. ITS can be described as systems consisting of technologies such as electronics, data processing and wireless networks that provide security and efficiency in the transportation network. ITS provides communication and information exchange between each transport unit. These units can be centres that provide information to pedestrians, vehicles, infrastructure, transportation and other peripherals such as traffic lights and other communication and control units. The application of MAS (Multi-Agent Systems) techniques, as a new development in information technology, can help to increase interest in traffic and promote energy-efficient transportation. ITS-based multi-agent technology is an important approach to solving complex traffic problems. The complexity of the elements of the traffic makes them convenient for multi-agent structures. ITS-based multi-agent technology provides us with safer controllers and makes us feel more comfortable in our daily lives. It increases the quality of our lives by decreasing the amount of time spent in traffic and by lowering the amount of emission gases released by our vehicles. The structurally dispersed nature of components in heterogeneous environments causes application difficulties, such as interoperability between agents forming a demand for a unified software platform as an underlying infrastructure. Therefore, it is preferable to use centralized solutions for relatively simple problems such as the one considered in this paper. For both transport decision-makers and drivers, ITS have a great potential for efficient and intelligent traffic management, threat identification, driving comfort and safety. ITS can also provide a flexible approach for the effective management of complex networked transportation systems letting traffic management decision-makers to control signal changes, regulate route flows, and broadcast real-time traffic information. In addition to providing route scheduling, weather forecasting, and emergency services for drivers, ITS (Intelligent Transportation Systems) can also help to reduce driving loads and improve safety. The implementation of ITS (Intelligent Transportation Systems) can generate positive outcomes across a range of areas, spanning from environmental and national security issues to emergency management and transportation. ITS applications can reduce time spent on the road. Short travel times provide economic savings for both individual and commercial vehicles and usually mean less environmental pollution. Intelligent Intersection Management (IIM) technology has started to develop in traffic intersections as part of Traffic Light Control (TLC) systems. Intersections are some of the busiest parts of roads. Therefore, the control of traffic lights plays an important role in decreasing the density. In this thesis, particular attention is given to the control of intersections in order to find solutions to decrease traffic density leading to an increased quality of life in big cities. Intelligent traffic control methods, the use of which is increasing with the development of new methods, are used especially in traffic intersections with high traffic density in order to provide efficient solutions. Control of a single intersection with traffic lights is considered first in the thesis. Various methods, including Fuzzy Logic Control (FLC), Proportional Integral (PI) control and State Space Model Control techniques, have been proposed and compared for a better traffic light controller architecture so as to increase the traffic flow and reduce the overall waiting time of the cars and the emissions released by them. It is demonstrated that the proposed architectures give better results compared to the traditional fixed-time traffic light control method. Different types of traffic intersections are considered in the study. Initially, a simple single-lane traffic intersection with no left or right turn is taken into consideration. Later on, intersections on which three-lane (or four-lane) roads meet with vehicles turning left and right are considered. Finally, a realistic case study, in which the Altunizade Region of Istanbul, is examined to demonstrate the efficiency of some of the proposed methods. The results of simulations indicate that the FLC, in which the positions of the vehicles are used as the state variables, gives superior results in comparison to the other classical methods. In order to increase the efficiency of the FLC further, a built-in learning algorithm is proposed to be used in addition to the FLC. A deep Q-learning method is employed for this purpose as a part of the agent-based traffic light controller. Hence, the resulting intelligent traffic light controller is named DQ-FLSI. In this method, a state matrix which divides the arms of the traffic intersection into cells is used. The traffic light durations are determined using fuzzy logic, and traffic light actions are determined by the help of deep Q-learning. A stability analysis is also carried out for this newly proposed method. Another important traffic problem is route planning. This is particularly important in large cities with complex traffic networks. In order to address this problem, an agent-based traffic route planning method has also been proposed as part of this thesis with the motivation of vehicles choosing the fastest route. In this method, route planning is made by deciding at traffic intersection points. Vehicle agents make decisions when they reach traffic intersections. In this way, dynamic route planning becomes possible for the vehicles. Another solution for the traffic intersection problem is multi-agent reservation-based traffic intersection control. With this method, all vehicles (called agents) can pass the intersection without the need for a traffic light thanks to a traffic intersection agent. A platoon method, which can work in harmony with reservation-based traffic intersection management, is proposed as an improvement in this part of the study. The proposed method aims to reduce the slowdowns that occur when approaching the traffic intersection by properly lining up the vehicles approaching the traffic intersection. It is shown by simulations that the proposed platoon method reduces energy consumption and gas emissions while increasing the average speed of the vehicles, especially as the density of the traffic increases. Work environments for all studied traffic problems are designed and simulated using the SUMO program. Simulation of Urban MObility (SUMO) is an open-source simulation package that works on networks imported from maps, provides various workspaces at micro levels, also allows pedestrian simulation, and has a sufficient set of tools that makes it more reachable.