Automated Methodological Procedure for Identifying Curve-Related Crashes in CRIS

Xu, Yang; Han, Zhe; Murphy, Michael; Zhang, Zhanmin

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Automated Methodological Procedure for Identifying Curve-Related Crashes in CRIS

2022-09-01
By Xu, Yang ; Han, Zhe ; Murphy, Michael

File Language:

English

Details

Alternative Title:

Improving the Identification of Curve-Related Crashes in the Crash Records Information System (CRIS) Project Title from Cover
Creators:

Xu, Yang ; Han, Zhe ; Murphy, Michael ; Zhang, Zhanmin
Corporate Creators:

University of Texas at Austin. Center for Transportation Research
Corporate Contributors:

Texas. Dept. of Transportation ; United States. Department of Transportation. Federal Highway Administration
Subject/TRT Terms:
Publication/ Report Number:

0-7050-P1
Resource Type:

Manual
Geographical Coverage:

United States
Corporate Publisher:

University of Texas at Austin. Center for Transportation Research
Abstract:

The product developed by Project 0-7050 is an automated methodological procedure for identifying curve-related crashes in CRIS. This document provides detailed information on the implementation of the automated methodological procedure. In order to automatically implement the methodological procedure for identifying curve-related crashes, the CTR research team first performed data cleaning and generated customized CRIS datasets using Python programming language via Jupyter Notebook. After the data preparation, the customized CRIS datasets were automatically imported into ArcGIS Pro for further visualization and analysis using Python libraries including ArcGIS API for Python and ArcPy. To evaluate the accuracy of the automated methodological procedure, a comparative study was performed using CRIS 2019 data. Based on the results, the CTR research team observed that the outputs of the automated procedure were identical to the results of the manual procedure performed in Task 5.
Format:

PDF
Download URL:

https://rosap.ntl.bts.gov/view/dot/64506/dot_64506_DS1.pdf
File Type:

[PDF - 3.03 MB]
Collection(s):

US Transportation Collection
Main Document Checksum:

urn:sha256:e9bf5f34802db2944d42cdf65c06ae6ec6d8ba9098c988bc9433223e288b277b

File Language:

English

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