Analysis of Factors Influencing the Vehicle Damage Level in Fatal Truck-Related Accidents and Differences in Rural and Urban Areas

Li, Linchao; Fratrović, Tomislav

doi:10.7307/ptt.v28i4.2056

Cited by 5 publications

(2 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To reduce the congestion caused by accidents, one can look for the influencing factors [35] and the severity of the damages. In this paper a different approach is taken.…”

Section: Traffic Speed Prediction For Highway Operations Based On a Smentioning

confidence: 99%

Traffic Speed Prediction for Highway Operations Based on a Symbolic Regression Algorithm

Li¹,

Fratrović

Zhang³

et al. 2017

PROMET

Self Cite

View full text Add to dashboard Cite

show abstract

“…To reduce the congestion caused by accidents, one can look for the influencing factors [35] and the severity of the damages. In this paper a different approach is taken.…”

Section: Traffic Speed Prediction For Highway Operations Based On a Smentioning

confidence: 99%

Traffic Speed Prediction for Highway Operations Based on a Symbolic Regression Algorithm

Li¹,

Fratrović

Zhang³

et al. 2017

PROMET

Self Cite

View full text Add to dashboard Cite

show abstract

“…From a methodological standpoint, parametric model structures have been widely used in crash severity analysis. In particular, a variety of discrete choice and logistic regression models have been formulated in the literature, including multinomial logit models (12)(13)(14)(15), ordered response models (16)(17)(18)(19)(20)(21)(22), nested logit models (23)(24)(25), and random parameter (mixed) logit models (26)(27)(28)(29)(30)(31)(32)(33). While logistic regression models demonstrate an overall satisfactory performance in establishing relationships between input parameters and the target variable, their prediction accuracy might be questionable in the presence of imbalanced data.…”

mentioning

confidence: 99%

Analysis of Fatal Truck-Involved Work Zone Crashes in Florida: Application of Tree-Based Models

Gupta

Asgari

Azimi

et al. 2021

Transportation Research Record

View full text Add to dashboard Cite

This paper presents the results of an analysis focusing on large truck-involved work zone fatal crashes using seven-year crash data in the State of Florida. Decision tree/random forest models were applied to specifically detect critical crash patterns that result in a fatality outcome. Because of the imbalanced nature of crash severity data (very low frequency of fatal crashes compared with property damage only or injury), data were treated using random and systematic over-sampling techniques. Marginal effects were addressed using Shapley values to increase model explainability. From a methodological perspective, results showed that the combination of over-sampling techniques with ensemble random forests could significantly improve model performance in predicting fatal crashes (compared with conventional logistic regression models). Primary contributors included pedestrian involvement, lighting conditions, safety equipment, driver condition, driver age, and work zone locations. For pedestrian crashes, factors such as dark-not lighted conditions, distracted truck driver, and driver’s age (young drivers outside city limits, senior drivers inside city limits) were highly likely to be fatal. For non-pedestrian crashes, the combination of front airbag deployment with any restraint system other than shoulder and belt was quite likely to be fatal. Also, abnormal driver conditions increased the risk of a fatal outcome. Additionally, the presence of female drivers (as the second driver in multiple vehicle crashes) highly decreased crash severity, probably because females typically drive more carefully than males. Interestingly, truck driver actions and maneuvers as well as roadway design and other physical environment features (i.e., number of lanes, median type, roadway grade, and alignment) did not show significant contribution to the model.

show abstract

Ranking contributors to traffic crashes on mountainous freeways from an incomplete dataset: A sequential approach of multivariate imputation by chained equations and random forest classifier

Prato

Wang

2020

Accident Analysis & Prevention

View full text Add to dashboard Cite

Analysis of Factors Influencing the Vehicle Damage Level in Fatal Truck-Related Accidents and Differences in Rural and Urban Areas

Abstract: ABSTRACT

Cited by 5 publications

References 40 publications

Traffic Speed Prediction for Highway Operations Based on a Symbolic Regression Algorithm

Traffic Speed Prediction for Highway Operations Based on a Symbolic Regression Algorithm

Analysis of Fatal Truck-Involved Work Zone Crashes in Florida: Application of Tree-Based Models

Ranking contributors to traffic crashes on mountainous freeways from an incomplete dataset: A sequential approach of multivariate imputation by chained equations and random forest classifier

Contact Info

Product

Resources

About