Using Design Patterns to Design and Implement Traffic Analysis and Prediction Systems

碩士 === 逢甲大學 === 資訊工程所 === 92 === As a common and important problem in many urban areas around the world, traffic congestion causes tremendous public concern, Intelligent Transportation Systems (ITS) focus on increasing the efficiency of existing surface transportation systems through the use of adva...

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Bibliographic Details
Main Authors: NAI-WEI CHANG, 張乃偉
Other Authors: none
Format: Others
Language:zh-TW
Published: 2004
Online Access:http://ndltd.ncl.edu.tw/handle/90927273830721352256
Description
Summary:碩士 === 逢甲大學 === 資訊工程所 === 92 === As a common and important problem in many urban areas around the world, traffic congestion causes tremendous public concern, Intelligent Transportation Systems (ITS) focus on increasing the efficiency of existing surface transportation systems through the use of advanced computers, electronics, and communication technologies. Dynamic models have been developed to utilize ITS technologies in evaluating flow distributions as well as predicting traffic conditions. This research aims at developing an integrated simulation-assignment model, in which local traffic characteristics, such as mixed traffic flow situations and tripmaker behavior are considered. The purpose of this thesis is to develop an object-oriented architecture for traffic estimation and prediction systems for Advanced Traffic Management Systems (ATMS). The overall system is named DynaTAIWAN that provides detailed traffic representation and traffic flow simulation capabilities. This architecture, based on the Design Pattern, includes several fundamental characteristics, namely cross-platform consideration, object-oriented design and reusable concept. Thus, the system provides more flexibility and expansibility for operation and maintenance. In order to meet the requirement of extensive computation, the kernel was implemented by using object-oriented programming language - C++. Through the use of DynaTAIWAN, ATMS can estimate traffic flow distributions, predict traffic flow conditions through real-time flow data collections, and then adjust traffic control measures in order to alleviate traffic congestions.