Factors influencing the patterns of wrong-way driving crashes on freeway exit ramps and median crossovers: Exploration using ‘Eclat’ association rules to promote safety

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ایمپکت فاکتور

۴٫۲۷۶ در سال ۲۰۲۰

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۲۶ در سال ۲۰۲۱

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۰٫۹۰۱ در سال ۲۰۲۰

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Q1 در سال ۲۰۲۰

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Factors influencing the patterns of wrong-way driving crashes on freeway exit ramps and median crossovers: Exploration using ‘Eclat’ association rules to promote safety

فهرست مطالب مقاله:


Wrong-way driving (WWD) has been a constant traffic safety problem in  certain types of roads. These crashes are  mostly associated with fatal or severe injuries. This  study aims to determine associations between various factors in the WWD crashes. Past studies on WWD crashes used either descriptive statistics or logistic regression to identify the impact of key contributing factors on  frequency and/or severity of  crashes. Machine learning and data mining approaches are   resourceful in  determining interesting and non-trivial patterns from complex datasets. This  study employed association rules ‘Eclat’  algorithm to deter- mine the interactions between different factors that result in  WWD crashes. This  study analyzed five  years (2010–۲۰۱۴) of  Louisiana WWD crash data to perform the analysis. A broad definition of  WWD crashes (both freeway exit ramp WWD crashes and median crossover WWD crashes on low  speed roadways) was used in this study. The results of this study confirmed that WWD fatalities are  more likely to be  associated with head-on colli- sions. Additionally, fatal WWD crashes tend to be  involved with male drivers and off- peak hours. Driver impairment was found as  a critical factor among the top twenty rules. Despite several other studies identifying only the WWD contributing factors, this study determined several influencing patterns  in  WWD crashes.  The  findings can   provide an excellent opportunity for  state departments of  transportation (DOTs)  and local agencies to develop safety strategies and engineering solutions to tackle the issues associated with WWD crashes.

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  1. Introduction

Wrong-way driving (WWD) crashes occur when a driver, intentionally or unintentionally, drives in the opposite direction of traffic flow.  These crashes have a higher probability of fatal consequences (1.34 fatalities per  fatal crash) compared to other types of crashes (1.10 fatalities per  fatal crash) since, being likely  head-on or opposite-direction sideswipe collisions.

According to  the Federal Highway Administration (FHWA),  approximately 300–۴۰۰ people die  each year due to  WWD crashes in the U.S. (Federal Highway Administration, 2017). These values highlight the need for novel preventive approaches to  mitigate the number of WWD  crashes, which we  address in this paper.

The National Transportation Safety Board  (NTSB) defines that ‘WWD is vehicular movement along a travel lane in a direc- tion opposing the legal   flow  of  traffic on  high-speed divided highways or  access ramps’ (NTSB, 2012). This  definition restricted WWD  crashes only  on  controlled-access highways. This  report has  not  included WWD  crashes that result from median crossover encroachments. While the majority of the studies address WWD  crashes on  freeways, the crash analysis on  median crossover WWD  crashes has  not  been conducted in depth. This  study aims to  overcome this gap.

WWD  crashes have been a subject of intense scrutiny over  the past decades. Researchers have studied WWD  crashes in different states in the U.S. including, Texas,  Illinois, North Carolina, Michigan, and New  Mexico (Braam, 2006; Finley  et al.,

2014; Lathrop et al., 2010; Morena and Leix, 2012; Zhou  et al., 2015). Most of the previous studies used the descriptive statis- tics  to  explore the role  of  different factors associated with WWD  crashes on  high speed roadways and freeways. Pour- Rouholamin, Kemel,  and Ponnaluri have employed other techniques such as Firth’s  penalized likelihood logistic regression, logistic regression and generalized order logit  model to analyze the WWD  crash data (Kemel, 2015; Ponnaluri, 2016; Pour- Rouholamin et al., 2016). Based  on several previous studies (Braam, 2006; Cooner et al., 2004; Copelan, 1989; Lathrop et al.,

2010; Morena and Leix, 2012; Scaramuzza and Cavegn, 2007; Zhou  et al., 2015), deadly WWD  crashes appear to  involve intoxicated drivers. Moreover, these studies identified other significant confounding factors associated with WWD  crashes such as driver age,  driver gender, and time of day.  Several previous studies (Morena and Leix, 2012, Zhou  et al., 2015) also found the significant role  of dark roadway conditions on the likelihood of WWD crashes. Studies using parametric techniques such as  logistic regression and Firth’s  Penalized Likelihood Logistic  Regression also  explored that intoxicated drivers and darkness cause WWD  crashes as  significant factors (Kemel, 2015; Ponnaluri, 2016; Pour-Rouholamin et al., 2016). These studies are  pivotal in coming up with countermeasures to reduce WWD crashes. However, these studies either used descrip- tive  statistics alone or employed parametric methods to model the underlying relationships in the data. It should be noted that descriptive statistics might be  insufficient to  untangling complex relationships between different variables, whereas parametric models make assumptions about the distribution of the independent and dependent variables that might not always be  true. In the recent years, machine learning and data mining methods have been widely used in  transportation safety research to  overcome the assumption issues associated with statistical modeling (Chen and Xie, 2015; Sun  et al.,

2014; Dong  et al.,  ۲۰۱۵; Das  et al.,  ۲۰۱۸b,c; Khan  et al.,  ۲۰۱۵; Iranitalab and Khattak, 2017; Das  and Sun,  ۲۰۱۶, ۲۰۱۵). Machine learning models can  detect interactions between different features and mask them if necessary. Data  mining meth- ods  do  not  depend on  any  assumptions because the generated rules and patterns would show either interestingness or redundancy.

Two  recent studies used multiple correspondence analysis (MCA) method (Jalayer et al.,  ۲۰۱۷; Das  et al.,  ۲۰۱۸a) to explore WWD  crashes. Compared to the parametric methods, MCA has  a lower bias  as this method does not  make any  prior assumptions about the variables. One of the limitations of this method is that it does not  enable researchers to conduct sig- nificance test on the clusters. Moreover, this technique only  informs about the correlation between variables but not  about causation. To overcome the limitation of MCA at the same time retaining the low  bias  advantage of MCA, we  analyzed five years (2010–۲۰۱۴) of Louisiana WWD crashes (for the remainder of this paper, Louisiana wrong way  crashes will be referred as WWD  crashes for consistency) using association rules. Frequent pattern mining (FPM) and association rules are  powerful machine learning tools to  identify the relationship between different factors. FPM recognizes the set  of items that tend to occur together and association rules help understand the causal relationship between a set  of antecedent items with the following item. The  researchers are  interested in  finding long  patterns as  a multitude of factors can  cause WWD  crashes. Vertical data mining technique called ‘‘Eclat” performs better when long  patterns are  required thus the researchers used

‘‘Eclat” to  analyze the data. This  study aims to  identify interdependence between various factors leading to  WWD  crashes.


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