Estimating Factors Contributing to Frequency and Severity of Large Truck–Involved Crashes

Dong, Chunjiao; Dong, Qiao; Huang, Baoshan; Hu, Wei; Nambisan, Shashi

doi:10.1061/jtepbs.0000060

Cited by 43 publications

(50 citation statements)

References 38 publications

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“…Several recent studies have focused on the multiple occupation-related factors of truck drivers, particularly those who are frequently involved in long-distance transport under the extremely stressful conditions, including commercial transport [1,3], license status [1,[3][4][5], continuous driving hours [6], shift patterns [7], rest-break duration [8], and overloading or improper loading [1,3,4], etc., which may directly or partially affect the probability of being involved in a crash. Additionally, truck drivers' risky driving behaviors, such as speeding [1,3,4,6,[9][10][11][12][13][14], failure to wear a seatbelt [1,3,4,15], following too closely [1,3,4,10,14], improper overtaking or lane changing [4,10,15,16], inattention [10,14], alcohol impaired driving [1,3,4,6,10,12,17], and fatigue driving [1,…”

Section: Introductionmentioning

confidence: 99%

Interpreting risk factors for truck crash severity on mountainous freeways in Jiangxi and Shaanxi, China

Wang

Luo

Chen

2019

Eur. Transp. Res. Rev.

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The occurrence and severity of truck crashes generally involve complex interactions among factors correlated to driver characteristics, vehicle attributes, roadway geometry and environment conditions. Thus, the elucidation of the significance of these potential contributory factors is critical when developing safety improvement countermeasures. To this end, data from a total of 1175 crashes involving at least one large truck and collected between 2010 and 2015 from two typical freeways in mountainous areas in Jiangxi and Shaanxi (China), were analyzed using a partial proportional odds model to determine the significant risk factors for injury severity of these crashes. Fourteen total explanatory variables, including the age of the driver, seatbelt status, number of vehicle involved, type of transport, freight conditions, brake system status, disregarding speed limit or not, following distance, horizontal roadway alignment, vertical roadway alignment, seasons, day of week, time of crash, and weather were found to significantly affect the severities of the truck crashes. In addition, old drivers, involvement of multiple vehicles, failure to wear seatbelts, overloading, speeding, brake failure and risky following behavior, curve section, seasons (summer, autumn and winter), nighttime period, and adverse weather conditions were also found to significantly increase the likelihood of injury and fatality crashes. Taken together, these findings may serve as a useful guide for developing legislation and technical countermeasures to ensure truck safety on freeways in mountainous regions, particularly in the context of a developing country.

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Section: Introductionmentioning

confidence: 99%

Interpreting risk factors for truck crash severity on mountainous freeways in Jiangxi and Shaanxi, China

Wang

Luo

Chen

2019

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

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“…Typically, traffic data from loop detectors has been utilized to predict potential lane-changing related crashes and studies indicated that difference in occupancy of adjacent lanes was significantly associated with the crash potential [29]. Individual vehicular information was extracted for a surrogate index of crash risk and results showed the measure was effective in predicting traffic crash occurrence [30,31]. Thus, naturalistic driving data provide the opportunity for researchers to fully investigate the safety factors in lane-changing.…”

Section: Introductionmentioning

confidence: 99%

Modeling Lane-Changing Behavior in Freeway Off-Ramp Areas from the Shanghai Naturalistic Driving Study

Zhang

Chen

Zhang

et al. 2018

Journal of Advanced Transportation

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The objective of this study is to investigate lane-changing characteristics in freeway off-ramp areas using Shanghai Naturalistic Driving Study (SH-NDS) data, considering a four-lane freeway stretch in various traffic conditions. In SH-NDS, the behavior of drivers is observed unobtrusively in a natural setting for a long period of time. We identified 433 lane-changing events with valid time series data from the whole dataset. Based on the logit model developed to analyze the choice of target lanes, a likelihood analysis of lane-changing behavior was graphed with respect to three traffic conditions: free flow, medium flow, and heavy flow. The results suggested that lane-changing behavior of exiting vehicles is the consequence of the balance between route plan (mandatory incentive) and expectation to improve driving condition (discretionary incentive). In higher traffic density, the latter seems to play a significant role. Furthermore, we found that lane-change from the slow lane to the fast lane would lead to higher speed variance value, which indicates a higher crash risk. The findings contribute to a better understanding on drivers' natural driving behavior in freeway off-ramp areas and can provide important insight into road network design and safety management strategies.

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“…Regarding vehicle maintenance, Cantor et al [12] established that poorly maintained trucks increased crash probability. Detailed analysis by Dong et al [14] demonstrated that the annual average daily traffic significantly affected truck-involved crash frequency and severity. Also, impaired drivers and driving under the influence of alcohol or drugs were observed to lead to a higher crash frequency.…”

Section: Factors Influencing Truckmentioning

confidence: 99%

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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Estimating Factors Contributing to Frequency and Severity of Large Truck–Involved Crashes

Cited by 43 publications

References 38 publications

Interpreting risk factors for truck crash severity on mountainous freeways in Jiangxi and Shaanxi, China

Interpreting risk factors for truck crash severity on mountainous freeways in Jiangxi and Shaanxi, China

Modeling Lane-Changing Behavior in Freeway Off-Ramp Areas from the Shanghai Naturalistic Driving Study

Discovering Insightful Rules among Truck Crash Characteristics using Apriori Algorithm

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