Road Traffic Injury Prevention Using DBSCAN Algorithm

Chantamit-o-pas, Pattanapong; Pongpum, Weerakorn; Kongsaksri, Krisakorn

doi:10.1007/978-3-030-63823-8_22

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Since DBSCAN can identify clusters with different shapes, it is suitable for application on road crash data. Multiple studies have applied DBSCAN clustering on road crash data [34,35].…”

Section: Data Descriptionmentioning

confidence: 99%

Analysis of Spatial Concentrations of Large-Truck Crashes Using Data Mining Methods

Tahfim¹,

Chen²

2022

IJSSE

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In recent years, the number of studies on crashes involving large-trucks has increased due to its importance to the economy and the higher chance of fatalities. However, none of the previous studies has given attention to the spatial concentrations of large-truck crashes. Moreover, the literature lacks exploration of granular level land use and urban design factors. The current study used the DBSCAN (Density-Based Spatial Clustering of Application with Noise) method to identify the spatial concentrations of crashes involving large-trucks. Additionally, the study explored housing, population, employment, and road network density attributes along with the crash characteristics, roadway attributes, location type, traffic conditions, driver’s action and behavior, and environmental factors. The association rule analysis was employed to discover the contributory factors that lead to no injury, non-severe and severe injuries at the spatial concentrations of crashes involving large-trucks. The findings indicated that the rear-end collisions involving drunk drivers often lead to severe injuries in large-truck crashes. Non-interstate roads, speed limit from 40 to 80 kilometers per hour, high road network density, medium and high population density are frequent conditions of non-severe injuries. Lastly, collisions between large-trucks and fixed objects, sideswipe same direction collisions, snowy roads, clear weather, medium road network and employment density are likely to facilitate no injury crashes involving large-trucks. Road traffic authorities can use these insights to reduce the frequency and severity of crashes involving large-trucks at their spatial concentrations.

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“…Since DBSCAN can identify clusters with different shapes, it is suitable for application on road crash data. Multiple studies have applied DBSCAN clustering on road crash data [34,35].…”

Section: Data Descriptionmentioning

confidence: 99%

Analysis of Spatial Concentrations of Large-Truck Crashes Using Data Mining Methods

Tahfim¹,

Chen²

2022

IJSSE

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Sensing Accident-Prone Features in Urban Scenes for Proactive Driving and Accident Prevention

Mishra

Rajendran

Vecchietti

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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A Systematic Literature Review of Learning-Based Traffic Accident Prediction Models Based on Heterogeneous Sources

2022

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Statistics affirm that almost half of deaths in traffic accidents were vulnerable road users, such as pedestrians, cyclists, and motorcyclists. Despite the efforts in technological infrastructure and traffic policies, the number of victims remains high and beyond expectation. Recent research establishes that determining the causes of traffic accidents is not an easy task because their occurrence depends on one or many factors. Traffic accidents can be caused by, for instance, mechanical problems, adverse weather conditions, mental and physical fatigue, negligence, potholes in the road, among others. At present, the use of learning-based prediction models as mechanisms to reduce the number of traffic accidents is a reality. In that way, the success of prediction models depends mainly on how data from different sources can be integrated and correlated. This study aims to report models, algorithms, data sources, attributes, data collection services, driving simulators, evaluation metrics, percentages of data for training/validation/testing, and others. We found that the performance of a prediction model depends mainly on the quality of its data and a proper data split configuration. The use of real data predominates over data generated by simulators. This work made it possible to determine that future research must point to developing traffic accident prediction models that use deep learning. It must also focus on exploring and using data sources, such as driver data and light conditions, and solve issues related to this type of solution, such as high dimensionality in data and information imbalance.

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Road Traffic Injury Prevention Using DBSCAN Algorithm

Cited by 3 publications

References 9 publications

Analysis of Spatial Concentrations of Large-Truck Crashes Using Data Mining Methods

Analysis of Spatial Concentrations of Large-Truck Crashes Using Data Mining Methods

Sensing Accident-Prone Features in Urban Scenes for Proactive Driving and Accident Prevention

A Systematic Literature Review of Learning-Based Traffic Accident Prediction Models Based on Heterogeneous Sources

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