Analysis of human-factor-caused freight train accidents in the United States

Zhang, Zhipeng; Turla, Tejashree; Liu, Xiang

doi:10.1080/19439962.2019.1697774

Cited by 15 publications

(7 citation statements)

References 46 publications

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“…Such circumstances may be useful for future campaigns, as it is drivers who cause most traffic accidents due to human error [44,45]. However, it should not be forgotten that pedestrians and motorists are the most vulnerable groups, being those who die the most from a traffic accident.…”

Section: Discussionmentioning

confidence: 99%

“Where Have I Heard It?” Assessing the Recall of Traffic Safety Campaigns in the Dominican Republic

et al. 2021

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Although traffic crashes are the eighth leading cause of death in the world, and are linked to vehicle and infrastructure-related factors, crash-related fatality rates are much higher in low-income countries. Particularly, the Dominican Republic is the country with the highest accident rate in the whole American continent. Therefore, in the past few years, public agencies have been developing different measures aimed at reducing traffic fatalities, including road safety campaigns. The aim of the present study was to assess the recalling of such campaigns among the Dominican population, which may serve as an additional indicator to evaluate their effectiveness in this and other countries of the region presenting similar traffic safety issues. For this cross-sectional study, a nationwide sample composed of 1260 people (50% males and 50% females) with a mean age of 39.3 years was used. The data were collected through personal interviews. Overall, the recall of traffic safety campaigns was found to be very low (9%); male drivers who were employed, possessed a driver’s license and habitually drove were the ones who could commonly remember these campaigns. The results of this study suggest that further evaluation and follow-up could help to maximize the impact of future traffic campaigns and advertisements in the Dominican Republic, as well as in other emerging countries of the region with similar characteristics. Further, key segments of the population such as the female, young, less formally educated and non-driving populations should be also targeted for further actions in this regard.

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Section: Discussionmentioning

confidence: 99%

“Where Have I Heard It?” Assessing the Recall of Traffic Safety Campaigns in the Dominican Republic

et al. 2021

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“…Thus, negative binomial regression model is used to model count data in the resilience performance. Negative binomial regression is one type of regression model that is appropriate to use when the response outcome is represented by discrete count variable and has also been employed in the domain of transportation ( Das et al, 2021 ; Zhang et al, 2021 ). Overdispersion occurs when the variance is great than the expected value (i.e., mean value), while negative binomial distribution can be feasible in the presence of overdispersion for count data.…”

Section: Methodsmentioning

confidence: 99%

Quantitative resilience assessment of the network-level metro rail service's responses to the COVID-19 pandemic

Zhang

Chai

Guo

2023

Sustainable Cities and Society

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“…This estimation relied on the assumption of accidents following a Poisson distribution within specified time periods and train kilometers per year. Employing a similar methodology, Zhang et al [10] analyzed the frequency of derailments and collisions in freight train accidents in the United States, attributed to human factors. Their model incorporated yearly train miles and the occurrence of train accidents as explanatory variables, deploying a Negative Binomial (NB) model.…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing the Factors Influencing Time Delays in Korean Railroad Accidents

Kim,

Lim

2024

Applied Sciences

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Railroads play a pivotal role in the Korean national economy, necessitating a thorough understanding of factors influencing accidents for effective mitigation strategies. Unlike prior research focused on accident frequency and severity, this study delves into the often-overlooked aspect of time delays resulting from railroad accidents. Analyzing 15 years of nationwide data (2008–2022), encompassing 3244 human-related and 3350 technical events, this research identifies key factors influencing delay likelihood and duration. Factors considered include event type, season, train type, location, operator size, person type involved, facility type, and causes. Despite an overall decrease in events, variable delay times highlight the need to comprehend specific contributing factors. To address excess zeros, the study employs a two-stage model and a zero-inflated negative binomial (ZINB) model, alongside artificial neural networks (ANNs) for non-linear pattern recognition. Human-related delays are influenced by event types, seasons, and passenger categories, exhibit nuanced impacts. Technical-related delays are influenced by incident types and facility involvement. Regarding model performance, the ANN models outperform regression-based models consistently in all cases. This study emphasizes the importance of considering both human and technical factors in predicting and understanding railroad accident delays, offering valuable insights for formulating strategies to mitigate service disruptions associated with these incidents.

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Analysis of human-factor-caused freight train accidents in the United States

Cited by 15 publications

References 46 publications

“Where Have I Heard It?” Assessing the Recall of Traffic Safety Campaigns in the Dominican Republic

“Where Have I Heard It?” Assessing the Recall of Traffic Safety Campaigns in the Dominican Republic

Quantitative resilience assessment of the network-level metro rail service's responses to the COVID-19 pandemic

Analyzing the Factors Influencing Time Delays in Korean Railroad Accidents

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