Effects of truck traffic on crash injury severity on rural highways in Wyoming using Bayesian binary logit models

Ahmed, Mohamed M.; Franke, Rebecca; Ksaibati, Khaled; Shinstine, Debbie S.

doi:10.1016/j.aap.2018.04.011

Cited by 119 publications

(92 citation statements)

References 12 publications

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“…Among these, several variants of ordered logit and probit regression models are the most common. ese models essentially help to determine the contribution of individual crash-risk factors to injury severities [15,17,18,22,23]. Although parametric models have served 2…”

Section: Methodological Approaches Used In Freight Truck Crashmentioning

confidence: 99%

“…According to them, the severity of injuries also increases in trucks carrying hazardous materials. Speeding has been identified as one of the significant influencers of injury severity [2,21,22]. In the literature concerning occupant injury severity in rear-end truck-involved crashes, Yuan et al [23] demonstrated that injury severity increases as speed increases.…”

Section: Factors Influencing Truckmentioning

confidence: 99%

“…Other factors such as inclement weather [23][24][25], wet road surface [15,23], fatigued driving [24], poor visibility and dark conditions [17,23,24], the weight and number of vehicles involved in the crash [15], and truck driver at-fault crashes [3] increase injury severity probabilities. Concerning roadway features, Ahmed et al [22] found that truck crash severity increased when trucks crash into fixed objects on the highway but reduce when they crash into guardrails. Results also confirmed that truck crash severity increases when it occurs on leveled road surfaces.…”

Section: Factors Influencing Truckmentioning

confidence: 99%

See 2 more Smart Citations

Discovering Insightful Rules among Truck Crash Characteristics using Apriori Algorithm

Hong

Tamakloe

Park

2020

Journal of Advanced Transportation

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This study aims to discover hidden patterns and potential relationships in risk factors in freight truck crash data. Existing studies mainly used parametric models to analyze the causes of freight vehicle crashes. However, predetermined assumptions and underlying relationships between independent and dependent variables have been cited as its limitations. To overcome these limitations and provide a better understanding of factors that lead to truck crashes on the expressways, we applied the Association Rules Mining (ARM) technique, which is a nonparametric method. ARM quantifies the interrelationships between the antecedents and consequents of truck-involved crashes and provides researchers with the most influential set of factors that leads to crashes. We utilized a freight vehicle-involved crash data consisting of 19,038 crashes that occurred on the Korean expressways from 2008 to 2017 for this investigation. From the data, 90,951 association rules were generated through ARM employing the Apriori algorithm. The lift values estimated by the Apriori algorithm showed the strength of association between risk factors, and based on the estimated lift values, we identified key crash contributory factors that lead to truck-involved crashes at various segment types, under different weather conditions, considering the driver’s age, crash type, driver’s faults, vehicle size, and roadway geometry type. From the generated rules, we demonstrated that overspeeding with medium-weight trucks was highly associated with crashes during the rainy weather, whereas drowsy driving during the evening was correlated with crashes during fine weather. Segment-related crashes were mainly associated with driver’s faults and roadway geometry. Our results present useful insights and suggestions that can be used by transport stakeholders, including policymakers and researchers, to create relevant policies that will help reduce freight truck crashes on the expressways.

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Section: Methodological Approaches Used In Freight Truck Crashmentioning

confidence: 99%

Section: Factors Influencing Truckmentioning

confidence: 99%

Section: Factors Influencing Truckmentioning

confidence: 99%

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Discovering Insightful Rules among Truck Crash Characteristics using Apriori Algorithm

Hong

Tamakloe

Park

2020

Journal of Advanced Transportation

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“…Environmental factors were correlated with injury severity ( Ahmed et al, 2018 ; Lemp et al, 2011 ; Lyman and Braver, 2003 ; Naik et al, 2016 ; Uddin and Huynh, 2017 ; Wang and Shi, 2013 ; Zou et al, 2017 ). Bad weather conditions, including fog, ice, and snow, showed positive relationships with severe injuries.…”

Section: Literature Reviewmentioning

confidence: 99%

Examining injury severity in truck-involved collisions using a cumulative link mixed model

Chen

et al. 2020

Journal of Transport & Health

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Background Trucks play a vital role in promoting regional freight transportation and economic development, but truck-involved collisions often have more severe consequences and create greater losses for society. Research purpose This study examined the relationships between injury severity and various explanatory factors in truck-involved collisions to identify preventive countermeasures for safety improvement. Data Los Angeles’ collision records from 2010 to 2018 were analyzed. Method A cumulative link mixed model was applied, where the heterogeneities among drivers were highlighted. Result Our findings confirmed that various driving mistakes, such as speeding, improper driving, and drinking alcohol, contributed to severe injuries. Male drivers were more likely to be severely injured, while female occupants were more likely to be severely injured. The use of safety equipment always helped mitigate injury severity. Collisions at night on dark roads with no streetlights and collisions on slippery road surfaces had higher risks of causing severe injuries. In addition, collisions on ramps were more likely to result in severe injuries. Drivers in old trucks were also at a higher risk of suffering from severe injuries. Conclusions Freight companies are encouraged to monitor drivers’ performance using remote cameras. Policy-wise, local agencies should regulate improper driving behavior and safety equipment use for truck drivers. Improving lighting conditions, periodically testing the skid resistance of road surfaces, adjusting speed limits, and applying weigh-in-motion technologies may greatly help mitigate injury severity. Old trucks should be brought in for frequent tests or abandoned after many years of usage.

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“…In addition, severe accidents occurred more in summer than in winter, but cross-over effects among factors were not considered. Based on a 10-year historical accident data in Wyoming (2002-2011), Ahmed et al [16] used Bayes binary Logit model to perform a classified study on severe and non-severe accidents of heavy trucks. Results showed that if a heavy truck accident occurs on an interstate expressway, the occurrence probability of a severe accident can increase by 4.5 times.…”

Section: State Of the Artmentioning

confidence: 99%

Analysis on the Influence Factors of Accident Severity: Evidence from Urban Rivercrossing Tunnels in Shanghai of China

He¹,

Zhang²,

Lu³

et al. 2018

JESTR

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To analyze various factors influencing the accident severity of urban river-crossing tunnels, 12 influence factors were selected according to the three traffic elements of vehicle, road, and environment, including accident-involved vehicle type, tunnel length, tunnel speed limit, accident occurrence time, and weather. These factors were based on the historical data of 14 urban river-crossing tunnels in Shanghai. A binary logistic regression model was also utilized to identify significant influence factors and analyze the influence degrees of their accident severity. Significance analysis reveals that accident occurrence place, accident-involved vehicle type, tunnel speed limit, number of vehicles involved, and accident type are significantly correlated with accident severity. Moreover, VISSIM traffic simulation model and conflict simulation analysis software called surrogate safety assessment model were utilized to simulate different urban rivercrossing tunnel scenes. The simulation explored the influence degrees of traffic volume, tunnel longitudinal slope, tunnel speed limit, and ratio of heavy trucks, which stand for three traffic elements (i.e., vehicle, road, and environment, respectively) on accident severity. Results show that the factors exerting the greatest influences on accident severity are traffic volume, number of vehicles involved, accident occurrence place, and accident type, followed by tunnel longitudinal slope, tunnel speed limit, and accident-involved vehicle type. Traffic volume, number of vehicles involved, accident type, tunnel longitudinal slope, and accident-involved vehicle type have a significantly positive correlation with accident severity, whereas accident occurrence position and tunnel speed limit have a significantly negative correlation with accident severity. The study results provide a new idea for researches on urban river-crossing tunnels and provide basis and method for tunnel entrance design, speed limit adjustment and regulation of traffic volume.

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Effects of truck traffic on crash injury severity on rural highways in Wyoming using Bayesian binary logit models

Cited by 119 publications

References 12 publications

Discovering Insightful Rules among Truck Crash Characteristics using Apriori Algorithm

Discovering Insightful Rules among Truck Crash Characteristics using Apriori Algorithm

Examining injury severity in truck-involved collisions using a cumulative link mixed model

Analysis on the Influence Factors of Accident Severity: Evidence from Urban Rivercrossing Tunnels in Shanghai of China

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