Wrong-way driving crashes: A multiple correspondence approach to identify contributing factors

Jalayer, Mohammad; Pour-Rouholamin, Mahdi; Zhou, Huaguo

doi:10.1080/15389588.2017.1347260

Cited by 48 publications

(22 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Filiz and Çemrek (2007) (Filiz & Çemrek, 2007) investigated whether housing status of university students exhibited correspondence with respect to gender and type of faculty. In the study conducted by Jalayer, Pour-Rouholamin and Zhou ( 2018) (Jalayer, Pour-Rouholamin, & Zhou, 2018), traffic accidents in the last 15 years were examined and it was determined that against long odds; the accidents did not originate from "female drivers", "safety belt use" and "vehicle age" variables. It has been determined that the largest contributor to the accidental dimensions in the study is the aged drivers and adverse weather conditions.…”

Section: Results Discussion and Suggestionsmentioning

confidence: 99%

Comparison of Academic Structure of Universities in Turkey by Multiple Correspondence Analysis Method

Sebetci¹,

Aksu²

2019

IJLTER

View full text Add to dashboard Cite

The purpose of this study is to reveal the similarities and differences between the universities in Turkey with regard to the numbers of academicians and departments. Within the scope of this objective, we included the first 100 universities among the total 185 universities in Turkey according the URAP data by the criterion sampling method. Conformity analysis, one of the multivariate statistical methods, was used in the data analysis. A two-dimensional graphic is obtained including the points representing the variable categories in the multi-dimensional space by using mathematical models to determine the structures of crosstabs consisting of the rows and columns through multiple conformity analysis. As a result of the study, it was determined that there was a conformity between the Ankara, Odtü, Marmara, Gazi, Hacettepe, İstanbul, Atatürk and İnönü Universities. In addition, a conformity was determined between the Ankara, İstanbul, İstanbul Teknik, Ege and Başkent universities according to the professor numbers within the academician categories. It was determined that there was no conformity between the Özyeğin, Bilkent, Kadir Has, Sabancı, Bahçeşehir, Koç and İstanbul Ticaret Universities regarding the variables addressed within the study. Based on the study, it is suggested that similar studies of multiple conformity analysis should be repeated with the different data groups.

show abstract

Section: Results Discussion and Suggestionsmentioning

confidence: 99%

Comparison of Academic Structure of Universities in Turkey by Multiple Correspondence Analysis Method

Sebetci¹,

Aksu²

2019

IJLTER

View full text Add to dashboard Cite

show abstract

“…Network analysis shows how key terms in the interview results relate to each other by frequency and has been used to understand communication in crisis events such as Hurricane Sandy (9). Multiple correspondence analysis (MCA) quantifies topical proximity, revealing clusters of similar words in the text corpus, and has been used in environmental, epidemiological, and transportation engineering studies (10)(11)(12)(13)(14). The two methods are focused on bigrams to associate object-subject pairings for meaning, further differentiating this approach from previous textual analytics research (15).…”

Section: Text Mining Methodsmentioning

confidence: 99%

Investigating the Role of Big Data in Transportation Safety

Das

Griffin

2020

Transportation Research Record

View full text Add to dashboard Cite

Big data may offer solutions for many challenges for transportation safety, providing more data faster, with higher spatial and temporal resolution. However, researchers and practitioners identify biases in big data that need to be explored and examined before performing data-driven decision-making. Leveraging semi-structured interviews of big data experts, this study includes a quantified analysis of topic frequency and an evaluation of the reliability of concepts through two independently trained coders. To identify the trends in the unstructured textual contents, the research team developed a text mining pipeline to identify trends, patterns, and biases. The study identifies key terms experts use when describing the role of big data in transportation safety, how the terms relate to the big data experts’ language through network plots, and clustering shows a need to focus on sources, quality, analysis, and implementation of big data. Results show value in maintaining the centrality of transportation experts and the public to determine the proper goals and metrics to evaluate transportation safety. Practitioners and researchers can develop new methods to improve population representation with big data, in addition to addressing difficult transportation safety problems. Working ahead of emerging trends and technologies of big data could support further advancements in transportation safety.

show abstract

“…Moreover, previous research efforts suggested that various factors, especially human factors and environmental conditions, were associated with WWD crashes (2,3,(6)(7)(8)(9)(10)(11)(12)(13). For example, the involvement of alcohol or drugs accounts for a substantial percentage of WWD crashes (11).…”

Section: Datamentioning

confidence: 99%

Factors Affecting Driver Injury Severity in the Wrong-Way Crash: Accounting for Potential Heterogeneity in Means and Variances of Random Parameters

Shen

2021

Transportation Research Record

View full text Add to dashboard Cite

Because of the high percentage of fatalities and severe injuries in wrong-way driving (WWD) crashes, numerous studies have focused on identifying contributing factors to the occurrence of WWD crashes. However, a limited number of research effort has investigated the factors associated with driver injury-severity in WWD crashes. This study intends to bridge the gap using a random parameter logit model with heterogeneity in means and variances approach that can account for the unobserved heterogeneity in the data set. Police-reported crash data collected from 2014 to 2017 in North Carolina are used. Four injury-severity levels are defined: fatal injury, severe injury, possible injury, and no injury. Explanatory variables, including driver characteristics, roadway characteristics, environmental characteristics, and crash characteristics, are used. Estimation results demonstrate that factors, including the involvement of alcohol, rural area, principal arterial, high speed limit (>60 mph), dark-lighted conditions, run-off-road collision, and head-on collision, significantly increase the severity levels in WWD crashes. Several policy implications are designed and recommended based on findings.

show abstract

Wrong-way driving crashes: A multiple correspondence approach to identify contributing factors

Cited by 48 publications

References 25 publications

Comparison of Academic Structure of Universities in Turkey by Multiple Correspondence Analysis Method

Comparison of Academic Structure of Universities in Turkey by Multiple Correspondence Analysis Method

Investigating the Role of Big Data in Transportation Safety

Factors Affecting Driver Injury Severity in the Wrong-Way Crash: Accounting for Potential Heterogeneity in Means and Variances of Random Parameters

Contact Info

Product

Resources

About