Risk Driving Indicator-Based Safety Performance Estimation by Various Aggregation Level Using Hard Braking Event Data

Conventional Safety Performance Functions (SPFs) primarily rely on static exposure measures such as Annual Average Daily Traffic (AADT), often failing to capture real-time, individual-level risky driving behaviors. To address this gap, this study proposes a Risky Driving Indicator (RDI) that integrates large-scale smartphone-based hard braking event data with traffic detector occupancy measures. The RDI was evaluated against traditional models across three specific aggregation levels: AADT, Annual Average Weekday Daily Traffic (AAWDT), and AAWDT excluding the overnight period. A case study was conducted using data from 2021 to 2022, a period coinciding with the COVID-19 pandemic, on South Korea’s busiest freeway to evaluate RDI-based SPFs. The results showed that models using the COM-Poisson framework outperformed traditional volume-based versions, showing superior performance across Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Akaike Information Criterion (AIC) values. These findings confirm that integrating crowdsourced behavioral data enhances predictive accuracy, offering transportation agencies a cost-effective, scalable solution for proactive hotspot identification and dynamic safety monitoring. By improving safety management through scalable and cost-effective mobile sensing, this study contributes to the development of more sustainable highway transportation systems.