Modelling the Congested Upstream Sections on Freeway Caused by Incidents: A Simulation Study

• Main Authors: Shih-Chieh Hung, 洪士傑
• Other Authors: Chee-Chung Tong
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/25065646188804391806

碩士 === 淡江大學 === 運輸管理學系碩士班 === 93 === Traffic incidents occur in variety of forms in road network and subsequently cause traffic congestion and travel time delays. In such conditions travel times may be increased not only on the incident link, but also on the links which are the upstream links of the incident location. These upstream links can therefore be identified as links affected by the incidents, namely, the “Affected Upstream Sections” and the prediction of how many links being affected is a vital issue to the development of advanced incident management systems in modern Traffic Management System. Unfortunately this particular issue has been long ignored comparing to the other incident detection issues. In this thesis, a new modeling approach originally proposed by Hounsell and Ishtiaq (1997) has been considered in which an “incident data base” was compiled using a simulation tool applied to a range of traffic and incident scenarios. A set of parameters was defined and the associated effects of these parameters were analyzed. Generalized statistical models were then developed to predict the number of links which would be affected by an incident of given characteristics. Two particular models work hand-in-hand for prediction of the number of effected links during the time period of formation (M1) and dissipation (M2) of the congestion caused by incidents respectively. Models performance was evaluated by statistically analyzing the prediction errors. The data base for the statistical modeling was compiled by using Paramics, a microscopic traffic simulation program, applied to a variety of traffic and incident scenarios. This simulation was calibrated carefully utilizing its feature for detailed road section geometric configuration and driving behavior using true real world flow data before its application in this study. Two freeway sections with different characteristics were selected in this thesis. The results show that the proposed modes have demonstrated a reasonable predictive quality where the M1 models performed better than the M2 in general. In addition, models specified for the scenarios with more severe incidents have exhibited better performance.