Citation

Mehdizadeh A, Cai M, Hu Q, Alamdar Yazdi MA, Mohabbati-Kalejahi N, Vinel A, Rigdon SE, Davis KC, Megahed FM. Sensors (Basel) 2020; 20(4): e1107.

Affiliation

Farmer School of Business, Miami University, Oxford, OH 45056, USA.

Copyright

(Copyright © 2020, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s20041107

PMID

32085599

Abstract

This part of the review aims to reduce the start-up burden of data collection and descriptive analytics for statistical modeling and route optimization of risk associated with motor vehicles. From a data-driven bibliometric analysis, we show that the literature is divided into two disparate research streams: (a) predictive or explanatory models that attempt to understand and quantify crash risk based on different driving conditions, and (b) optimization techniques that focus on minimizing crash risk through route/path-selection and rest-break scheduling. Translation of research outcomes between these two streams is limited. To overcome this issue, we present publicly available high-quality data sources (different study designs, outcome variables, and predictor variables) and descriptive analytic techniques (data summarization, visualization, and dimension reduction) that can be used to achieve safer-routing and provide code to facilitate data collection/exploration by practitioners/researchers. Then, we review the statistical and machine learning models used for crash risk modeling. We show that (near) real-time crash risk is rarely considered, which might explain why the optimization models (reviewed in Part 2) have not capitalized on the research outcomes from the first stream.

Language: en

Keywords

crash risk modeling; data visualization; descriptive analytics; highway safety; predictive analytics