M Akhondi; M Mesgari; M. R Malek; O Askari Sichani
Volume 9, Issue 2 , December 2017, , Pages 1-20
Abstract
Nowadays, heavy traffic is one of the major problems of living in big cities. In recent years, to overcome this problem, various solutions are proposed, many of which have been on the basis of general and comprehensive models. However, because of the essential complexity of urban environment and because ...
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Nowadays, heavy traffic is one of the major problems of living in big cities. In recent years, to overcome this problem, various solutions are proposed, many of which have been on the basis of general and comprehensive models. However, because of the essential complexity of urban environment and because of the diversity of parameters affecting urban traffic, those models cannot represent the dynamic space of urban traffic, properly. In contrast to them, agent based approach is a promising approach for modeling of urban traffic. This is mainly because of its ability in modeling the interactions of traffic components, and in the modeling of the dynamic nature of urban environment. Much research has been made in the field of application of agent technology to the modeling of urban traffic. The majority of these researches are focused on a particular area of traffic phenomenon. Some of them are on providing traffic lights with some levels of intelligence. Others try to simulate the behavior and decision making of the drivers. In other cases, agent based modeling is used for simulation of dynamic vehicle routing systems using real-time traffic information. Nonetheless, less attention is paid to the more comprehensive modeling of traffic using intelligent agents. Therefore, in this research, an agent based model is proposed for improving the navigation of vehicles, on the basis of communicating traffic information amongst traffic components. The urban environment is modeled as a vector space. The model components include the two-way streets, intersections, traffic lights, origin and destination of cars. Environment comprises of intersections, streets between intersections, and streets between intersections and the origin/destination points. Active agents are the cars, traffic lights and traffic control center. In this agent based model, the green-red changing of traffic lights is controlled and programmed based on the traffic jam condition (number of cars) of the streets connected to the light. It is assumed that all vehicles are equipped with GPS and necessary communication media. The system is implemented using JADE platform and its class libraries. The data of a simulated traffic network is entered to the model. The main result of this study is a simple model of the basic part of the urban traffic, in which mobile vehicles and traffic lights have access to online traffic information. In this model, all three types of agents, i.e. cars, traffic lights and traffic control center, can communicate with each other. By defining some criteria, the impact of such communications and access to online information can are assessed. In other words, the results of different scenarios are evaluated using criteria such as traffic jam and average of traveling time. An important aspect of the model is that, although communicating with each other, all agents including drivers and traffic lights act and decide independently, i.e. without any centralized decision-making system. In this study, no GIS software is directly used. However, the behavior of vehicles and traffic lights are modeled on the basis of metric spatial relationships (distance calculations) and topological relations (connections of the street edges with each other and with traffic lights). In other words, in this study, a simple spatial environment and simple spatial behaviors are modeled. Spatial environment of two-way street and moving in them is represented by movements in a set of simple lines in the direction of X and Y axes. This model is the first step towards a more complete modeling of urban traffic. In this model, the spatial movements of vehicles are modeled as vectors. The lengths of these vectors are calculated using the assumed vehicle speeds, the distance between points, and simple estimations of traffic jams.
