Department of Civil Environmental Construction Engineering, University of Central Florida, Orlando 4000 Central Florida Blvd, Orlando, FL 32816, USA.
Department of Civil and Environmental Engineering, University of Windsor, Windsor, 401 Sunset Ave., Windsor, ON N9B 3P4, Canada.
Accid Anal Prev. 2015 Jul;80:37-47. doi: 10.1016/j.aap.2015.03.015. Epub 2015 Apr 8.
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.
研究人员在量化各种治疗类型的碰撞修正系数（CMF）方面付出了巨大努力。在《公路安全手册》（HSM）中，已经确定 CMF 可用于预测将停车控制交叉口转换为信号控制交叉口（信号化）和安装闯红灯摄像机（RLC）的安全效果。先前的研究表明，信号化和安装 RLC 都减少了角度碰撞事故，但增加了追尾碰撞事故。然而，一些研究表明，在治疗实施后，CMF 随时间而变化。因此，本研究的目的是调查信号化和安装 RLC 随时间的 CMF 趋势。分别以每个月和 90 天的移动窗口来测量这两种治疗方法的 CMF。应用 ARMA 时间序列模型来预测基于 CMF 每月变化的 CMF 随时间的趋势。信号化的结果表明，信号化后早期的追尾碰撞 CMF 较低，但从第 9 个月开始逐渐增加。另一方面，安装 RLC 后早期的角度碰撞 CMF 较高，但从第 9 个月开始下降，然后趋于稳定。还发现，安装 RLC 后前 18 个月的总碰撞和致命/伤害碰撞的 CMF 明显大于随后的 18 个月的 CMF。这表明治疗对安全性能有滞后效应。ARMA 模型的结果表明，与每个月计算 CMF 相比，在 90 天移动窗口中计算 CMF 时，该模型可以更好地预测信号化和安装 RLC 的 CMF 趋势。特别是，ARMA 模型预测信号化在长期内对减少角度和左转碰撞具有显著的安全效果。因此，建议在治疗实施后，使用基于治疗实施后长期 CMF 趋势的 ARMA 模型来评估治疗的安全效果。
