Predictability and interpretability of hybrid link-level crash frequency models for urban arterials compared to cluster-based and general negative binomial regression models

Abstract

Machine learning (ML) techniques have higher prediction accuracy compared to conventional statistical methods for crash frequency modelling. However, their black-box nature limits the interpretability. The objective of this research is to combine both ML and statistical methods to develop hybrid link-level crash frequency models with high predictability and interpretability. For this purpose, M5' model trees method (M5') is introduced and applied to classify the crash data and then calibrate a model for each homogenous class. The data for 1134 and 345 randomly selected links on urban arterials in the city of Charlotte, North Carolina was used to develop and validate models, respectively. The outputs from the hybrid approach are compared with the outputs from cluster-based negative binomial regression (NBR) and general NBR models. Findings indicate that M5' has high predictability and is very reliable to interpret the role of different attributes on crash frequency compared to other developed models.

Clustering Approach toward Large Truck Crash Analysis

“…Data constraining is a common approach that focuses on a very specific segment of the crash dataset. For examples, Kitali et al focused on multiple-vehicle crashes ( 23 ); Hadi et al and Ghasemzadeh et al analyzed incidents in work zones ( 24 , 25 ); other specific subjects like crashes in rural or urban arterial ( 26 , 27 ) and crashes involving volatile or older adult drivers ( 28 , 29 ) have also been investigated.…”

Design, Development and Validation of an Intelligent Collision Risk Detection System to Improve Transportation Safety: The Case of the City of Popayán, Colombia

“…Other revised works in the literature present different methodologies, techniques and models to efficiently detect driving risks efficiently [16][17][18][26][27][28][29]. Most of these works conduct a Naturalistic Driving Study (NDS) [16][17][18][26][27][28][29].…”

“…Other revised works in the literature present different methodologies, techniques and models to efficiently detect driving risks efficiently [16][17][18][26][27][28][29]. Most of these works conduct a Naturalistic Driving Study (NDS) [16][17][18][26][27][28][29]. Another work does not use any dataset [30], as this one comparatively evaluates ND data collection approaches such as On Board Diagnostics (OBD-II), In-Vehicle Data Recorders (IVDR), smartphone sensor data, among others.…”