title: Estimating safety performance trends over time for treatments at intersections in Florida.
abstract: Researchers have put great efforts in quantifying Crash Modification Factors (CMFs) for diversified treatment types. In the Highway Safety Manual (HSM), CMFs have been identified to predict safety effectiveness of converting a stop-controlled to a signal-controlled intersection (signalization) and installing Red Light Running Cameras (RLCs). Previous studies showed that both signalization and adding RLCs reduced angle crashes but increased rear-end crashes. However, some studies showed that CMFs varied over time after the treatment was implemented. Thus, the objective of this study is to investigate trends of CMFs for the signalization and adding RLCs over time. CMFs for the two treatments were measured in each month and 90-day moving windows respectively. The ARMA time series model was applied to predict trends of CMFs over time based on monthly variations in CMFs. The results of the signalization show that the CMFs for rear-end crashes were lower at the early phase after the signalization but gradually increased from the 9th month. On the other hand, the CMFs for angle crashes were higher at the early phase after adding RLCs but decreased after the 9th month and then became stable. It was also found that the CMFs for total and fatal/injury crashes after adding RLCs in the first 18 months were significantly greater than the CMFs in the following 18 months. This indicates that there was a lag effect of the treatments on safety performance. The results of the ARMA model show that the model can better predict trends of the CMFs for the signalization and adding RLCs when the CMFs are calculated in 90-day moving windows compared to the CMFs calculated in each month. In particular, the ARMA model predicted a significant safety effect of the signalization on reducing angle and left-turn crashes in the long term. Thus, it is recommended that the safety effects of the treatment be assessed using the ARMA model based on trends of CMFs in the long term after the implementation of the treatment.
authors: Department of Civil Environmental Construction Engineering, University of Central Florida, Orlando 4000 Central Florida Blvd, Orlando, FL 32816, USA. Electronic address: jwang@knights.ucf.edu.
authors: Jung-Han
authors: JH
authors: Wang
authors: Department of Civil Environmental Construction Engineering, University of Central Florida, Orlando 4000 Central Florida Blvd, Orlando, FL 32816, USA. Electronic address: M.Aty@ucf.edu.
authors: Mohamed A
authors: MA
authors: Abdel-Aty
authors: Department of Civil Environmental Construction Engineering, University of Central Florida, Orlando 4000 Central Florida Blvd, Orlando, FL 32816, USA. Electronic address: jypark@knights.ucf.edu.
authors: Juneyoung
authors: J
authors: Park
authors: Department of Civil and Environmental Engineering, University of Windsor, Windsor, 401 Sunset Ave., Windsor, ON N9B 3P4, Canada. Electronic address: cclee@uwindsor.ca.
authors: Chris
authors: C
authors: Lee
authors: Department of Crime Prevention and Corrections, Central Police University, No. 56, Shujen Rd., Takang Village, Kueishan Hsiang, Taoyuan County, 33304, Taiwan. Electronic address: peifenkuo@gmail.com.
authors: Pei-Fen
authors: PF
authors: Kuo
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data; D001334:Automobile Driving / Q000523:psychology*; D004779:Environment Design; D005431:Florida; D006801:Humans; D065186:Interrupted Time Series Analysis; D008962:Models, Theoretical; D010781:Photography / Q000295:instrumentation*; D012449:Safety; D013997:Time Factors
keywords: ARMA; Crash Modification Factor; Crash Modification Function; Intersection; Red light running camera; Signalization; Time series model