**A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling**

**Amir Mehdizadeh 1,†, Miao Cai 2,†, Qiong Hu 1, Mohammad Ali Alamdar Yazdi 3, Nasrin Mohabbati-Kalejahi 4, Alexander Vinel 1, Steven E. Rigdon 2 and Karen C. Davis 5 and Fadel M. Megahed 6,\***


Received: 5 January 2020; Accepted: 12 February 2020; Published: 18 February 2020

**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.

**Keywords:** crash risk modeling; data visualization; descriptive analytics; highway safety; predictive analytics
