Measurement Error Models for Spatial Network Lattice Data: Analysis of Car Crashes in Leeds

TL;DR

This paper introduces a Bayesian model for analyzing car crash data on a spatial network, accounting for measurement error in traffic volume estimates to improve inference accuracy.

Contribution

It develops a novel Bayesian approach that incorporates measurement error correction in spatial network lattice data analysis of road accidents.

Findings

Adjusted traffic volume estimates improve crash risk modeling.

Measurement error correction reduces bias in spatial analysis.

Model applied successfully to Leeds crash data from 2011-2019.

Abstract

Road casualties represent an alarming concern for modern societies. During the last years, several authors proposed sophisticated approaches to help authorities implement new policies. These models were usually developed considering a set of socioeconomic variables and ignoring the measurement error, which can bias the statistical inference. This paper presents a Bayesian model to analyse car crashes occurrences at the network-lattice level, taking into account measurement error in the spatial covariate. The suggested methodology is exemplified by considering the collisions in the road network of Leeds (UK) during 2011-2019. Traffic volumes are approximated using an extensive set of counts obtained from mobile devices and the estimates are adjusted using a spatial measurement error correction.