Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model

Chang, Li-Yen; Chien, Jui-Tseng

doi:10.1016/j.ssci.2012.06.017

Cited by 209 publications

(126 citation statements)

References 26 publications

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“…Node 9 shows the lowest injury rate of 6.5% under the dark-not-lighted condition, at a speed limit above 40 miles/h without traffic control devices. This is significantly different from the findings from Chang and Chien [35], which concluded that traffic control devices can enhance traffic safety. The reason for the seemingly contradictory findings may be due to the fact that traffic control devices usually appear at crash-prone sites where road conditions are more complex.…”

Section: Decision Trees Under Different Light Conditionscontrasting

confidence: 98%

“…For all three models, the percentage predicted correctly was above 60%. This rate is higher than those reported in previous studies using the same method [35]. All three models are capable of predicting PDO crashes with an accuracy rate above 98%.…”

Section: Decision Trees Under Different Light Conditionscontrasting

confidence: 53%

“…Subsequently Quinlan [31] also presented the algorithm C4.5, which is an advanced version of ID3 with a split criterion, called the Information Gain Ratio (IGR), similar to the one used in the ID3 procedure that penalizes variables with many states. Among these methods, CART is the most widely used [25,28,[32][33][34][35][36]. It should be noted, however, that compared to conventional statistical models, CART still has its limitations such as simplicity and difficulty in interpreting.…”

Section: Decision Tree Methodmentioning

confidence: 99%

“…The detailed calculation method and principles can be found in Montella et al [25], Chang and Chien [35], and J. S. Lee and E. S. Lee [37].…”

Section: Methodsmentioning

confidence: 99%

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Analyzing Traffic Crash Severity in Work Zones under Different Light Conditions

Wei

Shu

Huang

et al. 2017

Journal of Advanced Transportation

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Previous studies have investigated various factors that contribute to the severity of work zone crashes. However, little has been done on the specific effects of light conditions. Using the data from the Enhanced Tennessee Roadway Information Management System (E-TRIMS), crashes that occurred in the Tennessee work zones during 2003-2015 are categorized into three light conditions: daylight, dark-lighted, and dark-not-lighted. One commonly used decision tree method-Classification and Regression Trees (CART)-is adopted to investigate the factors contributing to crash severity in highway work zones under these light conditions. The outcomes from the three decision trees with differing light conditions show significant differences in the ranking and importance of the factors considered in the study, thereby indicating the necessity of examining traffic crashes according to light conditions. By separately considering the crash characteristics under different light conditions, some new findings are obtained from this study. The study shows that an increase in the number of lanes increases the crash severity level in work zones during the day while decreasing the severity at night. Similarly, drugs and alcohol are found to increase the severity level significantly under the dark-not-lighted condition, while they have a limited influence under daylight and dark-lighted conditions.

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Section: Decision Trees Under Different Light Conditionscontrasting

confidence: 98%

Section: Decision Trees Under Different Light Conditionscontrasting

confidence: 53%

Section: Decision Tree Methodmentioning

confidence: 99%

“…The detailed calculation method and principles can be found in Montella et al [25], Chang and Chien [35], and J. S. Lee and E. S. Lee [37].…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Analyzing Traffic Crash Severity in Work Zones under Different Light Conditions

Wei

Shu

Huang

et al. 2017

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…In the traffic field, the application of decision trees is also extensive [10], [11]. However, no study has been conducted to date on the analysis of SFRTAs in China.…”

Section: Decision Tree Analysismentioning

confidence: 99%

The Analysis of Serious Fatal Road Traffic Accidents in China

Liu

Chen

2016

MATEC Web Conf.

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Abstract.A large number of road traffic accidents occur each year in China, which results in huge losses to the safety of people's lives and property. Among all accidents, the accident with over 10 fatalities in one accident is extraordinarily serious, which has immeasurable impact on the security. In this paper, we defined these accidents as serious fatal road traffic accidents (SF-RTAs). The aim of this study was to analyze SF-RTAs in China and its provinces and investigate the causes of SF-RTAs. Firstly, we described and analyzed the annual trend of SF-RTAs based on the data from 1990 to 2014. Then we analyzed region, time and accident type distribution characteristics of SF-RTAs during [2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014]. Then the main factors influencing SF-RTAs were analyzed by linear regression and decision tree methods. Finally, we proposed corresponding control strategies for preventing SF-RTAs according to the analysis results. It is expected that the findings of this study are useful in indentifying effective prevention measures for serious fatal accidents in China, and protecting human lives and properties.

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Differences in single heavy vehicle crashes at intersections and midblocks

Balakrishnan

Moridpour

Tay

2016

J of Advced Transportation

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The objective of this research is to identify the factors differentiating between single heavy vehicle collisions at intersections and midblocks by using a binary logit model. Our results show that single vehicle crashes involving heavy vehicle at intersections are more likely to occur on main roads and highways, whereas crashes at midblocks are more likely to occur on divided two-way roads, roads with special facilities or features (e.g. bridge) and roads with a higher percentage of heavy vehicle traffic. Intersection crashes are also more likely to involve vehicles that are turning left or right, resulting in angle crashes and vehicle overturn, whereas midblock crashes are more likely to involve vehicles on higher posted speed roads.Note: Distribution (%) of collisions reported for categorical variables. Mean and (standard deviation) reported for continuous variables. HEAVY VEHICLE CRASHES

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Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model

Cited by 209 publications

References 26 publications

Analyzing Traffic Crash Severity in Work Zones under Different Light Conditions

Analyzing Traffic Crash Severity in Work Zones under Different Light Conditions

The Analysis of Serious Fatal Road Traffic Accidents in China

Differences in single heavy vehicle crashes at intersections and midblocks

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