What question did this study set out to answer?

The aim is to analyze crash trends and predict future crash counts of ADAS and ADS vehicles using time-series forecasting models.

May 20, 2026Future TransportationOpen Access

Seasonal Patterns and Future Projections of ADAS and ADS Crashes: A Time-Series Forecasting Study

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Time-series forecasting reveals crash trends in ADAS and ADS, emphasizing the need for timely safety measures.

The aim is to analyze crash trends and predict future crash counts of ADAS and ADS vehicles using time-series forecasting models.
Developed seasonal autoregressive integrated moving average and Facebook Prophet models.
Trained on 30 months of crash data from July 2021 to December 2023 and validated on 6 months (January–June 2024).
Utilized validation metrics such as root mean square error, mean absolute percentage error, and Theil’s U1 statistic.
Facebook Prophet outperformed SARIMA, achieving an RMSE of 2.71 for ADAS and 2.24 for ADS; MAPE was 6.9% for ADAS and 8.85% for ADS.
ADAS crashes exhibited a bimodal pattern, peaking in January and May–June, with troughs in February–March and August–September.
ADS displays a trimodal pattern with peaks in April–May, August, and October.

Banik et al. (2026) studied this question.

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