GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance

Abstract

Statistical modeling of historical crash data can provide essential insights to safety managers for proactive highway safety management. While numerous studies have contributed to the advancement from the statistical methodological front, minimal research efforts have been dedicated to real-time monitoring of highway safety situations. This study advocates the use of statistical monitoring methods for real-time highway safety surveillance using three years of crash data for rural highways in Saudi Arabia. First, three well-known count data models (Poisson, negative binomial, and Conway–Maxwell–Poisson) are applied to identify the best fit model for the number of crashes. Conway–Maxwell–Poisson was identified as the best fit model, which was used to find the significant explanatory variables for the number of crashes. The results revealed that the road type and road surface conditions significantly contribute to the number of crashes. From the perspective of real-time highway safety monitoring, generalized linear model (GLM)-based exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are proposed using the randomized quantile residuals and deviance residuals of Conway–Maxwell (COM)–Poisson regression. A detailed simulation-based study is designed for predictive performance evaluation of the proposed control charts with existing counterparts (i.e., Shewhart charts) in terms of the run-length properties. The study results showed that the EWMA type control charts have better detection ability compared with the CUSUM type and Shewhart control charts under small and/or moderate shift sizes. Finally, the proposed monitoring methods are successfully implemented on actual traffic crash data to highlight the efficacy of the proposed methods. The outcome of this study could provide the analysts with insights to plan sound policy recommendations for achieving desired safety goals.

Assessment of Significant Factors Affecting Frequent Lane-Changing Related to Road Safety: An Integrated Approach of the AHP–BWM Model

“…Accordingly, the official statistics estimated that at least 33% of all road crashes happen as vehicles alter lanes or turn off the road. Furthermore, crash data recorded from 2010 to 2017 in Middle East countries indicate that sudden lane changes produced about 17.0% of the total serious accidents, followed by speeding (12.8%) [18,19].…”

Efficient GLM‐based control charts for Poisson processes

“…In prior studies, several performance metrics have been proposed to examine the power of control charts. For example, Jamal et al 39 utilized ARL and SDRL to analyse control charts formed on COM-Poisson model. Also, Kinat et al 32 and Mahmood 68 made use of run-length properties like ARL, SDRL and median run length to evaluate the Inverse Gaussian and Zero-inflated Poisson model-based schemes.…”

“…Park et al 38 studied the quantile residuals-based procedure to examine non-normal quality characteristic. Jamal et al 39 suggested EWMA and CUSUM structures originated from randomized quantile residuals and deviance residuals of COM-Poisson regression. For examining Poisson and COM-Poisson profiles, Mammadova and Özkale 40 introduced Shewhart, EWMA and CUSUM structures derived from ridge deviance residuals.…”

On the multivariate progressive control chart for effective monitoring of covariance matrix

“…It is expected to have the lowest EQL for the best chart. For the details on these and some other useful measures, one may be seen in studies [37][38][39][40] and the references therein. The computational algorithm for the proposed MPVC is outlined as:…”