分時段高速公路事故頻次模式

Abstract

本研究利用過去較為少見之不同時段所發生事故件數作為反應變數進行研究，並使用道路幾何特性、設施與環境特性及交通特性三類作為解釋變數進行總體事故分析，以建構不同的事故頻次及時間分布整合模式，並分別進行推估及預測各路段下各時段的事故頻次，並藉由兩種預測績效指標平均絕對百分比誤差以及均方根誤差找出最佳之事故頻次及時間分布整合模式，並針對有最佳預測能力之模式，探討其顯著影響事故頻次與事故時間分布的解釋變數以了解事故發生的規律、特性與原因，藉以研擬改善策略。 本研究建立三種事故頻次與時間分布整合模式，分別為整合模式一－單變量事故頻次與離散選擇整合模式、整合模式二－單變量事故頻次與分群整合模式以及整合模式三－分時段之多變量事故頻次模式。其中，在單變量事故頻次模式之建構使用單變量之卜瓦松迴歸(PO)模式、負二項迴歸(NB)模式與廣義卜瓦松迴歸(GPO)模式，而本研究中NB模式擁有較佳之模式配適度與模式解釋績效，因此以NB模式代表單變量事故頻次模式與時間分布模式做結合；分時段之多變量事故頻次模式則使用多變量之多變量卜瓦松迴歸(MPO)模式與多變量廣義卜瓦松迴歸(GPO)模式進行建構，而本研究中MPO模式擁有較佳之模式配適度與模式解釋績效；在時間分布模式上則分別使用離散選擇模式之多項羅吉特(MNL)模式與集群分析之階層式分群進行建構。 研究結果顯示，整合模式三(MPO)在平均預測事故件數上較為接近平均實際事故件數，但在Adj-MAPE值與RMSE值中，整體表現卻是較差的，而整合模式二(NB與分群)在整體表現中則是較佳的。NB模式之顯著變數中，最大下坡度、克羅梭曲線參數、測速照相點數量與重車比例，此四者與事故發生件數呈反向影響；曲率、鄰近都會區與小車車流量，此三者則與事故發生件數呈正向影響。集群分析顯示，各時段下所發生之事故，會特定集中於不同路段群，夜間(20~23)與清晨(23~07)之事故較易發生於郊區路段(集群二)；日間(07~14)之事故較易發生於鄰近系統交流道(集群四)之路段；傍晚(14~20)之事故較易發生於非鄰近系統交流道(集群三)之路段。 關鍵字：事故頻次、時間分布、負二項迴歸、多項羅吉特、集群分析、多變量卜瓦松迴歸

The key factors explaining the spatial and temporal distribution of crash frequency are essential for proposing corresponding countermeasures. However, most of previous studies only focus on the key factors contributing to the spatial distribution, while rather few studies further examine the time-of-day distribution of crash frequency. Based on this, this study aims to not only identify the spatial key factors, but also to examine those affecting the time-of-day distribution of crash frequency. To do so, three freeway crash frequency models under the time-of-day distribution are developed, estimated and compared in this study. Model 1 uses of count models, including Poisson regression (PO), negative binomial regression (NB) and generalized Poisson regression (GPO), to explain the spatial distribution of crash frequency and uses of a ratio model, i.e. multinomial logit model (MNL), to determine the time-of-day distribution probability. Model 2 combines the abovementioned count models and a clustering model which classified freeway segments into different clusters according to their time-of-day distribution of crash frequency. The average time-of-day distributing pattern of each cluster is then use to represent the distribution of freeway segments which belong to the cluster. Model 3 is a multivariate count model by treating crash frequencies by time-of-day periods as target variables and two formulations, multivariate Poisson regression (MPO) and multivariate generalized Poisson regression (MGPO), are attempted to exhibit the spatiotemporal distribution of crash counts simultaneously. The abovementioned count and ratio models are then developed by considering the explanatory variables, including geometrics, facilities, environment condition, and traffic characteristics. Crash datasets of Taiwan Freeway No.1 in 2005 and 2006 are used to estimate and validate the models, respectively. The performances of three models are measured in terms of the Adjusted Mean Absolute Percentage Error (Adj-MAPE) and the Root-Mean-Square Error (RMSE). Four time-of-day periods, Morning (23~07), Afternoon (07~14), Evening (14~20) and Night (20~23), are formed according to the crash frequency distribution. The results show that the NB model performs better than the GPO and PO models, which is then adopted for the univariate count model in both Models 1 and 2. In terms of Adj-MAPE and RMSE, Model 2 performs best, followed by Model 1 and Model 3. According to the estimated parameters in the NB model, four variables of the maximum downward slope, the Clothoid curve value, the number of speeding cameras, and the percentage of heavy trucks exhibit significant negative effects on crash frequency, while the curvature rate, the adjacent to metropolitan and the traffic volume of small vehicles have significant positive effects on crash frequency. Corresponding countermeasures are then proposed. It is interesting to note that according to the clustering results, the freeway segments located in the non-metropolitan area (i.e. Cluster 2) tend to have higher crash frequency in the night (20~23) and morning (23~07) while those located near the system interchange (i.e. Cluster 4) tend to have higher crash frequency in the afternoon (07~14) and do not located near the system interchange (i.e. Cluster 3) tend to have higher crash frequency at night(14~20), suggesting the time-of-day distributions of crash frequency of different segments remarkably differ. Different countermeasures should be proposed for different segments. Keywords：Crash frequency, Time-of-day distribution, Negative binomial regression, Multinomial logit model, Cluster analysis, Multivariate Poisson regression.