26-04-2012, 02:07 PM
An Intelligent Traffic Light Monitor System using an Adaptive Associative Memory
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Introduction
Transportation research’s goal is to optimize transportation flow of people and goods. As the
number of road users constantly increases while resources provided by current Infrastructures are
limited, intelligent control of traffic will become a very important issue. Traffic in the urban areas
system regularized by traffic lights, which is in many cases contribute to the unnecessary long waiting
times for cars if not efficiently configured [13].
The conventional traffic light control methods include fix-time control, time of day control, vehicle
actuated control, semi-actuated control, green wave control, area static control and area dynamic
control. However, there is no system meeting the adaptive characteristic. This is because the traffic
control system is non-linear, fuzzy and nondeterministic, and thus traditional methods of modeling and
control cannot work very well.
Related works
This subsection provide a survey of the literature related to traffic light systems, highlighting most
of the traffic light models (i.e., queue traffic light, fuzzy traffic light, Petri-net traffic light and LED
traffic light) that were developed to improve traffic light efficiency.
Queue traffic light model
The queue traffic light model was developed in traffic engineering studies. Vehicles arrive at an
intersection controlled by a traffic light and form a queue. Many researchers evaluated the queue
lengths in each lane using different techniques depending on street width and the number of vehicles
that are expected at a given time of day. In this model, traffic light efficiency is effected when
unexpected events happen (traffic accidents) causing disruption to the flow of vehicles. For example,
Fathy and Siyal (1995) proposed a queue detection algorithm that consists of motion detection and
vehicle detection operation. Both are based on extracting the edges of the scene to reduce the effects of
variations in lighting conditions [15]. Jin and Ozguner described (1999) a combination of multidestination
routing and real time traffic light control based on a concept of cost-to-go to different
destinations. This traffic light model is also a decentralized control approach [29].
Knowledge based traffic light models
Knowledge based systems are artificial intelligent tools that work in a narrow domain to provide
intelligent decisions with justification. Knowledge is acquired and represented using various
knowledge representation rules, frames and scripts. Many researchers have used knowledge based
systems to developed traffic light systems. For example, Findler and coworkers (1997) described a
distributed, knowledge-based system for real-time and traffic-adaptive control of traffic signals. The
first of a two-stage learning process optimizes the control of steady-state traffic at a single intersection
and over a network of streets. The second stage of learning deals with predictive/reactive control in
responding to sudden changes in traffic patterns [16].
Conclusion
A review and discussion of the research is presented. The developments presented in this thesis are
considered improvements to previous work by adding a component to the main concepts. This study
focused on the development of an intelligent vision traffic light monitoring system via associative
memory in order to demonstrate an improvement in traffic light configurations.