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Predicting Motor-Vehicle Deaths Using Machine Learning: Proposed 8E-Model

Saurabh Kumar

Journal of Prediction Markets2025https://doi.org/10.5750/jpm.v19i1.2195article
AJG 1
Weight
0.50

What the paper says

This study conducts a comprehensive time series analysis of motor-vehicle fatalities in the USA spanning from 2019 to 2021, revealing a troubling upward trajectory. Factors such as over-speeding, impaired driving, reduced road traffic enforcement during the pandemic, and instances of driving under the influence have significantly contributed to the surge in fatal crashes during this period. Utilizing the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, this research forecasts the trajectory of motor vehicle deaths in the USA. The forecast suggests a continuation of the upward trend, emphasizing the urgency of addressing the escalating fatalities. In response to the burgeoning global trend of increasing accidents and fatalities, this study advocates for the implementation of broader preventive measures worldwide. Proposed strategies encompass the crucial role of policy implementation and road safety measures in curbing the rising toll of road accidents, particularly in the USA.Furthermore, this study extends the existing 7E model (Education, Engineering, Enforcement, Exposure, Examination of Competence and Fitness, Emergency Response, and Evaluation) by introducing the eighth ‘E’—Empathy—in the context of road safety. This augmentation creates the 8E model, offering a more encompassing framework adaptable on a global scale. The inclusion of empathy underscores the significance of considering human emotions, behaviours, and societal impact in crafting effective road safety initiatives.

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https://doi.org/https://doi.org/10.5750/jpm.v19i1.2195

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@article{saurabh2025,
  title        = {{Predicting Motor-Vehicle Deaths Using Machine Learning: Proposed 8E-Model}},
  author       = {Saurabh Kumar},
  journal      = {Journal of Prediction Markets},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.5750/jpm.v19i1.2195},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.