TY  - JOUR
PY  - 2020//
TI  - A review of data analytic applications in road traffic safety. Part 1: descriptive and predictive modeling
JO  - Sensors (Basel)
A1  - Mehdizadeh, Amir
A1  - Cai, Miao
A1  - Hu, Qiong
A1  - Alamdar Yazdi, Mohammad Ali
A1  - Mohabbati-Kalejahi, Nasrin
A1  - Vinel, Alexander
A1  - Rigdon, Steven E.
A1  - Davis, Karen C.
A1  - Megahed, Fadel M.
SP  - e1107
EP  - e1107
VL  - 20
IS  - 4
N2  - 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>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s20041107
ID  - ref1
ER  -