Machine Learning Methods to Analyze Injury Severity of Drivers from Different Age and Gender Groups
| Autor: | Ryan Doczy, Somayeh Mafi, Yassir AbdelRazig |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
050210 logistics & transportation
medicine.medical_specialty Injury control business.industry Accident prevention Mechanical Engineering 05 social sciences Human factors and ergonomics Poison control Suicide prevention Occupational safety and health Age and gender Physical medicine and rehabilitation 0502 economics and business Injury prevention medicine 0501 psychology and cognitive sciences business human activities 050107 human factors Civil and Structural Engineering |
| Zdroj: | Transportation Research Record: Journal of the Transportation Research Board. 2672:171-183 |
| ISSN: | 2169-4052 0361-1981 |
| DOI: | 10.1177/0361198118794292 |
| Popis: | Access to non-biased and accurate models capable of predicting driver injury severity of collision events is vital for determining what safety measures should be implemented at intersections. Inadequate models can underestimate the potential for collision events to result in driver fatalities or injuries, which can lead to improperly assessing the safety criteria of an intersection. This study investigates how injury severity differs between drivers of various ages and gender groups using cost-sensitive data-mining models. Previous research efforts have used machine learning methods for predicting injury severity; however, these studies did not consider the consequences (cost) of incorrect predictions. This paper addresses this shortfall by considering the monetary cost of incorrect injury severity predictions when developing C4.5, instance-based (IB), and random forest (RF) machine-learning models. One model of each method was developed for four distinct cohorts of drivers (i.e., younger males, younger females, older males, and older females). Each model considered a selection of driver, vehicular, road/traffic, environmental, and crash parameters for determining if they significantly influenced driver injury severity. A five-year period of two-vehicle crash data collected at signalized intersections in the metropolitan area of Miami, Florida was used in the models. Results indicated that cost-sensitive learning classifiers were superior to regular classifiers at accurately predicting injuries and fatalities of crashes. Among cost-sensitive models, RF outperformed C4.5 and IB models in predicting driver injury severity for four groups of drivers. The models displayed substantial differences in injury severity determinants across the age/gender cohorts. |
| Databáze: | OpenAIRE |
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