Addressing Driving Actions of At-Fault Older Drivers: Bayesian Bivariate Ordered Probit Analysis

Abstract

This study aimed to examine the driving actions of at-fault older drivers, and investigate the interrelations between the unobservable factors. To reach the goal, a Bayesian bivariate ordered probit model was proposed, which addressed the driving actions of different drivers simultaneously, and accommodated the interrelations between the unobservables by covariance. The data with 27 arterials from 2014 to 2017 were collected from ArcGIS open data site maintained by Nevada Department of Transportation (NDOT). Compared to individual Bayesian random parameter ordered probit model, the proposed model outperformed according to goodness-of-fit. Results revealed that injury severity and total vehicles were potentially significant factors for actions of at-fault older drivers, while total vehicle and vehicle condition were significant for actions of not-at-fault drivers. The findings can provide potential insights for practitioners to apply the new technology and remind the driving actions of older drivers.

Keywords

• Driving action
• older driver
• Bayesian bivariate ordered probit model
• Bayesian seemingly unrelated bivariate ordered probit model
• Bayesian random parameter ordered probit model