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Estimating safety performance trends over time for treatments at intersections in Florida
Jung-Han Wang1, Mohamed A Abdel-Aty1, Juneyoung Park1
1Department of Civil Environmental Construction Engineering, University of Central Florida, Orlando 4000 Central Florida Blvd, Orlando, FL 32816, USA.
Accident; Analysis and Prevention
|April 14, 2015
Summary
Crash Modification Factors (CMFs) for intersection signalization and Red Light Running Cameras (RLCs) show time-dependent safety effects. The ARMA model effectively predicts long-term trends, revealing a lag in safety performance after treatment implementation.
Area of Science:
- Traffic Safety Engineering
- Transportation Planning
- Statistical Modeling
Background:
- Crash Modification Factors (CMFs) are crucial for evaluating safety treatments like intersection signalization and Red Light Running Cameras (RLCs).
- Previous research indicates these treatments can alter crash types, increasing rear-end collisions while decreasing angle crashes.
- Observed variations in CMFs over time necessitate dynamic analysis of treatment effectiveness.
Purpose of the Study:
- To investigate the temporal trends of CMFs for intersection signalization and RLC implementation.
- To assess the long-term safety performance and identify potential lag effects of these traffic safety treatments.
- To evaluate the predictive capability of the ARMA time series model for CMF trends.
Main Methods:
- CMFs were calculated monthly and using 90-day moving windows for signalization and RLC treatments.
- The Autoregressive Moving Average (ARMA) time series model was employed to analyze and predict CMF trends over time.
- Comparative analysis of CMFs in early versus later periods post-treatment was conducted.
Main Results:
- Signalization CMFs for rear-end crashes initially decreased then increased, while angle crash CMFs showed an opposite trend.
- RLC CMFs for angle crashes decreased over time, stabilizing after an initial increase.
- A significant lag effect was observed for RLCs, with higher CMFs for total and fatal/injury crashes in the first 18 months compared to the subsequent 18 months.
- The ARMA model demonstrated better prediction accuracy for CMFs calculated using 90-day moving windows.
Conclusions:
- Traffic safety treatments like signalization and RLCs exhibit time-varying effectiveness, indicating a lag in their full safety impact.
- The ARMA model is a valuable tool for assessing long-term safety performance trends of traffic interventions.
- Long-term evaluation using CMF trends and time series analysis is recommended for accurate safety assessment of traffic treatments.
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