@article{ref1,
title="A review of data analytic applications in road traffic safety. Part 1: descriptive and predictive modeling",
journal="Sensors (Basel)",
year="2020",
author="Mehdizadeh, Amir and Cai, Miao and Hu, Qiong and Alamdar Yazdi, Mohammad Ali and Mohabbati-Kalejahi, Nasrin and Vinel, Alexander and Rigdon, Steven E. and Davis, Karen C. and Megahed, Fadel M.",
volume="20",
number="4",
pages="e1107-e1107",
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.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s20041107",
url="http://dx.doi.org/10.3390/s20041107"
}