Traffic signal using smart agent system

American Journal of Applied Sciences, Nov, 2008 by Cheonshik Kim, You-Sik Hong

INTRODUCTION

Most urban areas nowadays experience severe traffic jams on street networks. As the traffic congestion spreads, there is a need to apply intelligent algorithms to diminish the waste of time, air pollution, and so on. Therefore, a traffic control system seeks to minimize the delay experienced by vehicle travelling through a road network of intersections by manipulating the traffic signal plans. There are various levels of sophistication in traffic signal control system using fuzzy traffic control. Agent-oriented fuzzy traffic control is a useful tool in designing traffic signal timing plans adaptively (1).

In fact, agent technology was begun in the 1950s. Agent is a software that user achieves automatically wanting work. In particular, this is a concept that has been studied for a long time in artificial intelligence. From the late 1980s, a boundary that is an agent has been detached with artificial intelligence and exposed to individual study subject. Agent products have appeared since the early 1990s (2). A multi-agent system consists of multiple agents who are autonomous and make their decisions independently. By this definition, we rule out those systems where a central planner or designer controls the decision processes of local agents. If the agents' actions do not affect each others' outcomes, then we may as well consider the agents' situations independently (3). A multi-agent system offer certain advantages for problem solving: faster response, increased flexibility, robustness, resource sharing, and better adaptability (4), (5).

Traffic signal control is also one of these applications (6), (7). A lot of technical research (8), (9), (10), (11) present fuzzy systems for a multi-way single intersection. In spite of traffic signal using fuzzy system, the control problem for network intersections still is an important issue in the field of traffic engineering (12). Fuzzy logic is derived from fuzzy set theory dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic. It can be thought of as the application side of fuzzy set theory dealing with well thought out real world expert values for a complex problem (14). In 1990's, application of this method are widely used all over the world.

The FLC (fuzzy logic control) uses three linguistic input variables and one linguistic output. The fuzzy input variables are the passed time of the current interval, the number of vehicles crossing an intersection during the green phase, and the length of queuing from the red direction. The extension time calculated using 27 fuzzy rules is the output. This FLC was simulated at less critical intersections. Gomide et al. proposed a FLC with adaptive strategies for fuzzy urban traffic systems (13). The FLC adjusts the membership functions according to the traffic conditions to optimize the controller's performance.

Traffic signals are not a cure-all for every problem intersection. In Korea, traffic congestion is very much so that wrong traffic control signal is one of cause. Since drive time is a non-productive activity, congestion reduces regional economic health by increasing drive times. Traffic congestion's cause is traffic incident. Traffic incidents are events that disrupt the normal flow of traffic, usually by physical impedance in the travel lanes. Events such as vehicular crashes, breakdowns, and debris in travel lanes are the most common form of incidents. With increasing numbers of vehicles on restricted roads, it happens that we have much wasted time and decreased average car speed.

Therefore, in this research, we will analyze traffic circumstance by real-time and solve traffic congestion using smart agent system. In addition, this research proposes a new concept of coordinating green time, which controls 10 traffic intersection systems.

MATERIALS AND METHODS

Let us suppose that we have the best traffic control signal, but it cannot produce the most suitable green time in case of a sudden increase in traffic volume such as created by a traffic accident or by street construction work. In particular, let us suppose that the increase exceeds the capacity of the intersection beyond 100%. Then road conditions occur that fills an intersection with vehicles and creates a blocked phenomenon. As a result, roads become a parking lot and the traffic control signal functions badly in these circumstances. Such a problem is very difficult to correct in creating a traffic cycle. Therefore, we suggest new traffic signal concept that can control 10 intersections.

We defined fuzzy rule base for traffic congestion circumstance and described calculation method. The traffic volume balance is held at each signalized intersection of the traffic network for a certain sampling period. It can be described by the following Eq.

[C.sub.e][(green) = [G.sub.rte](car) [G.sub.rti](car) - [G.sub.rto](car) (1)

where,

[G.sub.te](car) = Excess incoming traffic cars

[G.sub.rti](car) = Incoming traffic cars