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Title: A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling
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.  more » « less
Award ID(s):
1635927
PAR ID:
10212199
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Sensors
Volume:
20
Issue:
4
ISSN:
1424-8220
Page Range / eLocation ID:
1107
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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