Analysis of Risk Factors Affecting Urban Truck Traffic Accident Severity in Korea

Chen, Maowei; Zhou, Lele; Choo, Sangho; Lee, Hyangsook

doi:10.3390/su14052901

Cited by 19 publications

(7 citation statements)

References 18 publications

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“…e. The model indicates that the accident rate at night is much higher than that during the day, which is also mentioned in the research of Chen et al [44]. This is mainly because the driver's visibility is poor at night, and there are almost no lighting facilities on the highway except in service areas and tunnels.…”

Section: Comparative Analysis Of Factors Affecting Accident Occurrenc...mentioning

confidence: 65%

Division and Analysis of Accident-Prone Areas near Highway Ramps Based on Spatial Autocorrelation

Shen

et al. 2023

Sustainability

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This study focuses on identifying accident-prone areas and analyzing the factors contributing to the distribution of traffic accidents near highway ramps. A combined method of kernel density estimation, spatial autocorrelation analysis, and multivariate logistic regression analysis helped to identify accident hotspots. Through data collection and analysis, the clustering characteristics of traffic accidents in the diversion and merging areas were identified. Four levels of accident-prone areas were divided according to the accident rates. The factors influencing the spatial distribution of accidents were analyzed. The results showed that traffic accidents in the diversion area were concentrated near the exit, but the accidents in merging areas had a wider range of distribution. The analysis of this phenomenon was conducted using the multinomial logit model results. The important factors of different accident-prone areas were clarified. The temperature, the accident lane, weather conditions, and the time of day had significant impacts on the spatial distribution of traffic accidents. The study’s findings provide an important decision-making basis for highway accident prevention management.

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Section: Comparative Analysis Of Factors Affecting Accident Occurrenc...mentioning

confidence: 65%

Division and Analysis of Accident-Prone Areas near Highway Ramps Based on Spatial Autocorrelation

Shen

et al. 2023

Sustainability

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“…The maximum speed, the number of lanes, and the number of road property-changing nodes were the most significant traffic condition factors. The results provided useful data for setting traffic safety policies in urban areas [58].…”

Section: Dhanyamentioning

confidence: 94%

Domain-Driven Actionable Knowledge Discovery for Traffic Accidents Using Rules Induction

Yousif¹,

Agarwal²,

Pareek³

2022

IJRST

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Due to the limitation of the methodologies of traditional data mining to satisfy business expectations, the shift from mining data-centered hidden patterns to domain-driven actionable knowledge discovery has become a significant direction of KDD research [22]. Traditional data mining algorithms and tools face major obstacles and challenges to solve real-life business problems and issues as they fail to provide actions that can be taken by people in business based on generated rules [22]. A small set of rules are generated by standard classification algorithms to form a classifier, but these classification algorithms use domain independent biases and heuristics [2]. This research aimed to propose a new approach to find actionable rules from sets of discovered rules. It focused on how a combination of traditional classification data mining and domain-driven data mining approach could be applied in solving real-life problems related to the field of traffic accidents in UAE. Real-life data were collected and pre-processed using the user’s existing knowledge and needs. Classification using Rules Induction was applied on the domain-driven dataset. The discovered rules from this technique were then summarized, combined, and analyzed. The final set of actionable rules from Classification technique for each class was then generated using a proposed interestingness method. To support such a process, the domain driven in-depth pattern discovery (DDID-PK) framework was followed [9]. Based on experimental results, the extracted domain-driven rules were more interesting and actionable than those produced by the traditional classification technique of data mining. In addition, the integration of data-centered classification technique of data mining to domain-driven approach of data mining and actionable knowledge discovery could help the Dubai police authority to reduce traffic accident severity by formulating new policies and traffic rules based on the domain-driven knowledge extracted from some hidden patterns from real data.

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“…In the logistics influencing area, truck traffic crashes had a higher frequency than those in the non-influencing area. 'Lane violation' and 'Signal violation' were identified as significant truck traffic crash causes [26]. Low visibility due to different reasons, such as fog, rain, and nighttime driving, was the focus of other studies that indicated that driving during the nighttime is associated with a higher crash risk due to fatigue and visibility restriction [27,28].…”

Section: Of 18mentioning

confidence: 99%

Impact of Driver, Vehicle, and Environment on Rural Road Crash Rate

Tajnik

Luin

2022

Sustainability

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There is an abundance of research on road-crash-influencing factors; however, it often relies on a limited subset of variables. The aim of this work was to analyze the significance of road-crash-influencing variables on rural roads and to estimate the crash frequencies during different conditions by introducing a holistic approach and analyzing a wide range of driver–vehicle–road–environment variables. The input data comprised long-term vehicle speed data, obtained using inductive loop traffic counters, and short-term data, obtained using a calibrated police radar. A combination of both was augmented with driver traits and meteorological conditions, gender, age, years possessing a driver’s license, crashes, vehicle, and environmental data. The crash data used for the analysis was based on police records. The results indicate that crash frequencies and driving speed have strong daily and weekly seasonality. The average hourly crash frequencies per kilometer driven during the week varied between 0.2 and 2.2 crashes per million km; the major cause was speeding, which contributed to nearly 32% of fatal crashes. Speed choice could be affected by alcohol-consuming drivers involved in crashes, as the percentage of drivers with any level of alcohol detected expressed daily and weekly patterns similar to those of crash frequencies per kilometer. Contrary to the highest relative crash frequency, which occurred during nighttime, the majority of daily crashes occurred during the afternoon peak hours; thus, the societal impact of crashes is the highest during the day.

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Analysis of Risk Factors Affecting Urban Truck Traffic Accident Severity in Korea

Cited by 19 publications

References 18 publications

Division and Analysis of Accident-Prone Areas near Highway Ramps Based on Spatial Autocorrelation

Division and Analysis of Accident-Prone Areas near Highway Ramps Based on Spatial Autocorrelation

Domain-Driven Actionable Knowledge Discovery for Traffic Accidents Using Rules Induction

Impact of Driver, Vehicle, and Environment on Rural Road Crash Rate

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