Modeling Link-Level Crash Frequency Using Integrated Geospatial Land Use Data and On-Network Characteristics

Abstract

The primary focus of this paper is to develop models to estimate link-level crash frequency using land use data extracted and integrated through the use of a distance gradient method. The on-network characteristics were added to the integrated land use characteristics database and were also used in the development and validation of link-level crash frequency estimation models. Both statistical and back-propagation neural network (BPNN)-based approaches were tested and evaluated for modeling. Mean absolute deviation (MAD), median error, 85th percentile error, and root-mean squared error (RMSE) were computed to validate the developed link-level crash frequency estimation models and compare the two approaches. The results obtained from validation of the link-level crash frequency estimation models indicate that the computed errors are low for models based on both statistical and neural network approaches. Both the approaches have reasonably good predictive capability and can be used to estimate crash frequency. The role of predictor (includes integrated land use) variables on crash frequency along links can be easily understood using outputs from the statistical modeling approach. Also, findings indicate that models based on integrated land use and on-network characteristics (excluding traffic volume) have good predictive capability and can be used as surrogate data to estimate crash frequency if traffic volume data are not available.