Time-of-Day Distribution Patterns of Freeway Crash Frequency by Using Functional Data Analysis

• Main Authors: Yu, Sheng-Te, 游聲德
• Other Authors: Chiou, Yu-Chiun
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/m4hx6h

碩士 === 國立交通大學 === 運輸與物流管理學系 === 105 === Previous studies in freeway crash frequency modeling mostly used of count models (e.g. Poisson and Negative Binomial models) to investigate the effect of risk factors (commonly adopted factors including geometric variables, traffic variables, and environment variables) on the annual number of accidents on an analytical road segment. Few of them further considered the effect of these factors on the time-of-day distribution of accidents. The time distribution of accidents is essential for proposing strategies for accident prevention and effective emergency rescue. Based on this, this study uses of functional data analysis (FDA) – functional principal components analysis (FPCA), functional analysis of variance (FANOVA) and functional linear model (FLM)- to model and analyze the time-of-day distribution of accidents of analytical road segments. Where the functional principal component analysis can explain the variations of pattern, functional ANOVA helps to identify the effect of each of key factors and functional linear model can investigate how the functional key factors affect the distribution patterns of accidents. A case study on accidents data in Freeway No.1 with a total of 124 road segments formed by two adjacent interchanges and two directions (northbound and southbound) is conducted. The potential factors includes downward slope, curvature, number of lanes, posted speed camera, yearly rainfall, neighboring to metropolitan, traffic flows of small vehicles, large vehicles and trailer-tractors, and percentage of large vehicles and trailer-tractors. The results of FPCA show that three principal components of pattern variations are Increase in crash frequency in the morning peak hours, Increase in crash frequency in evening peak hours, and Decrease in crash frequency in the daytime off-peak hours. The results of FANOVA indicate that curvature, number of lanes and posted speed camera significantly affect the differences in the distribution patterns. At last, the functional linear modeling between functional traffic flows (small vehicles, large vehicles, trailer-tractors, percentage of large vehicles and trailer-tractors) and time-of-day distribution of crash frequency is analyzed. The results show that small vehicles traffic flow has the best explanatory power, suggesting the distribution patterns of accidents are best fitted to the distribution patterns of small vehicle traffic flow. However, the coefficient of determination is low, implying some missing important factors have not been considered in this study.