Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Multi-disciplinary Studies | en_US |
| dc.creator | Leung, Tak-fai | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/4294 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Towards intelligent area traffic control system management | en_US |
| dcterms.abstract | Over the past years, Area Traffic Control (ATC) Systems have been developed for the operation of traffic signals in co-ordinated manner. These systems are very effective in reducing number of vehicular stops and delays at signalized junctions. Management of ATC System requires a team of experts with knowledge in different perspectives. The knowledge required include computing, traffic engineering, ATC philosophy, local road network and traffic conditions. Needless to say, the management of ATC System is not an easy task. There are limitations in the present practice of ATC System Management. In their daily work, the traffic engineers always face with the challenge of making decisions based on incomplete traffic information available. Many of such decisions are based on knowledge which is heuristics in nature. This is an area where conventional algorithmic-based programming techniques cannot provide much assistance. In addition, ATC Systems lack the capability to predict traffic condition and only react to traffic congestion when it has already built up. This often causes delay in restoring traffic to its normal state. Knowledge-based expert systems (KBES) have proved to be effective in heuristics problem solving. They are particular suitable for handling problems that are unstructured and for processing knowledge that can be expressed in symbolic terms. Because of these strengths, KBES technology is attracting much attention within the transport engineering community. Many applications have been developed for various applications in transportation. Road traffic control, like many other branches of transport engineering, is particularly suitable for applying KBES technology. It is for this reason, KBES technology is adopted for developing the framework for Intelligent ATC System Management. The proposed framework is based on blackboard problem solving architecture. It consists of twelve knowledge sources, namely, Static Road Network, ATC System Customization, Traffic Signal Design, Traffic Signal Plan Generation, Dynamic Road Network, External Factors, Data Improvement, Traffic Forecast, Traffic Control, Equipment Fault Analysis and Input/Output. The knowledge sources communicate with each others through the Central Blackboard, which also provides a common representation of the traffic system. In addition, the tasks are co-ordinated by Action Scheduler. The primary objective of these knowledge sources is to provide support for both background and foreground work of the traffic engineers in ATC System Management. These cover knowledge on local road network and traffic condition, inference of additional traffic data from data collected from detectors, analysis and interpretation of collected data to recognise traffic conditions, qualitative prediction of traffic situations and control decision making. Examples of production rules have been formulated to illustrate how KBES technology can be applied in ATC System Management. A mini KBES for ATC System Management was also developed as part of the project to demonstrate how KBES technology can be applied. | en_US |
| dcterms.extent | vi, 112 leaves : ill. ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 1996 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.LCSH | Traffic signs and signals | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b12306708.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.59 MB | Adobe PDF | View/Open |
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