Dynamic traffic signal control using a self-learning, fuzzy-neural intelligent system
Date
1995
Authors
Wu, Jian
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Abstract
Optimal system performance in a traffic system is achieved by planning, control, and scheduling of the system's many traffic movements. This task of optimizing the time and facility conflicting activities is challenging. In the last a few decades, extensive research has been carried out on the planning and control of manufacturing processes to improve productivity and reduce manufacturing costs. In this work, we apply the quantitative intelligent system concept, developed in the optimal planning of manufacturing activities, to the dynamic signal control problem of a corridor traffic system to minimize traffic delays.
Today, the ever-increasing demand for individual mobility and reduced traffic delays, coupled with economic and environmental restrictions on increases in physical road capacity, requires efficient control of signalized intersection traffic. Present traffic signal control is based upon static traffic control strategy using several fixed timing plans. These timing plans contain optimal control parameters for representative traffic patterns manually identified by a traffic engineer. Several timing plans are applied during different time periods according to the traffic demand statistics , regardless the actual traffic flow condition at the instant. To improve the quality of signalized traffic control, and to reduce traffic delay and congestions caused by the inaccurate estimation of traffic demands, the development of a dynamic control strategy based upon the on-line acquired traffic flow condition becomes necessary.
In this work, an approach for dynamic traffic signal control, based upon a fuzzy-neural intelligent system and traffic delay minimization, is introduced. This approach consists of three major parts: (a) automated traffic flow pattern identification using fuzzy pattern clustering, (b) optimization of traffic control parameters (timing plan design) for identified traffic flow patterns, and (c) dynamic traffic signal control by real-time traffic flow monitoring, traffic flow pattern matching using the fuzzy-neural system, and execution of stored optimal control parameters.
The traffic flow pattern identification is carried out using the fuzzy pattern clustering and matching techniques. A mathematical model is first introduced to quantify the fuzzy traffic conditions. The traffic condition at a moment is expressed as a hyper point in a m-dimensional traffic parameter space. Similar traffic conditions show as clouds of hyperpoints. The quantified traffic condition description allows the "characteristic groups" of traffic flow conditions being recognized as traffic patterns, using the fuzzy clustering methods. These traffic patterns are closely studied. The optimal timing plans of these traffic patterns, which contain the optimal signal control parameters, are generated using commercial software through extensive optimization.
Dynamic traffic control is accomplished using fuzzy traffic pattern clustering/matching methods and traffic plan optimization. The task is carried out in two steps: off-line learning and on-line control. The off-line learning part identifies all representative traffic patterns based upon previously collected traffic data, designs a timing plan for each identified traffic pattern using traffic delay minimization, and trains a fuzzy-neural system using the traffic pattern - optimal timing plan pairs generated. The on-line control part senses traffic flow in real-time, matches the sensed traffic flow condition with the best fitted traffic pattern, assigns the optimal timing plan of the matched traffic pattern to related traffic controllers dynamically. A method for short-term traffic flow condition prediction is also developed to offset the short delay in traffic flow condition sensing, and quasi-optimal traffic signal parameter updating at the controller.
The approach makes dynamic traffic signal control of a corridor traffic system with quasi-optimal performance possible. The system is self-adaptive and capable of carrying out self-learning to varying traffic demands. Computer simulation and prototype testing using the real traffic data have demonstrated significant traffic delay reduction.
The research directly contributes to static and dynamic traffic control research and practice. It also extends the research and applications of the quantitative intelligent system approach, and benefits the research on intelligent scheduling and planning for time and facility conflict activities. The research on developing a hybrid fuzzy-neural system combines the reasoning ability of a fuzzy system and the learning ability of a neural network, which is critical for a self learning and self-adaptive, intelligent system.