The analysis of dangerous degrees for railway level crossing accidents

• Main Authors: Chun-fu Huang, 黃俊福
• Other Authors: Jiann-Sheng Wu
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/20509103143372058553

碩士 === 國立中央大學 === 土木工程研究所 === 97 === In all kind of the land transportation, the railway transportation system has the lowest rate of accident. While once an accident is happened, it usually causes life and property loss. Especially the level crossing which is crossed between railway and highway. Slight accident will tie up traffic, and serious accident will cause death and car crash. This research forecasts the possiblity of accident happening by setting a railway crossing accident analysis model. Based on the research, we can realize more about the correlation factor. The research uses data from Crossing Improvement Plan published by Taiwan Provincial Government in 1998 as references. It consists of both historical accident data and railway level crossing related data in Taiwan, such as crossing types, highway geometric characteristics, daily trains and average annual daily traffic (AADT), etc. We try to reorganize the traffic accident data since 2002 to 2007, collected from Transportation Department, Taiwan Railway Administration, to the level crossing accident data. Finally, the research could be applied to Poisson Regression Model, Negative Binomial regression, and Back-Propagation Neural Network for evaluating the prediction of the level crossing accident frequency models, fatalities models, and hurt models then to find the fittest model with the major factor which is relative significantly. It can provide the relation with the TRA for forecasting accident in the future. As a consequence of this research, we can find Poisson Regression Model and Negative Binomial regression are better than Back-Propagation Neural Network. The most significant factor of effect on level crossing model is the number of pass trains.