Novel Machine Learning Methods for Accident Data Analysis

Lin, Lei; Wang, Qian; Sadek, Adel W.; State University of New York at Buffalo. Dept. of Civil, Structural, and Environmental Engineering

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Novel Machine Learning Methods for Accident Data Analysis

2018-01-01
By Lin, Lei ; Wang, Qian ; Sadek, Adel W.

[PDF-1.91 MB]

English

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Creators:

Lin, Lei ; Wang, Qian ; Sadek, Adel W.

Lin, Lei ; Wang, Qian ; Sadek, Adel W. Less -
Corporate Creators:

State University of New York at Buffalo. Dept. of Civil, Structural, and Environmental Engineering
Corporate Contributors:

Transportation Informatics University Transportation Center ; United States. Dept. of Transportation. University Transportation Centers (UTC) Program

Transportation Informatics University Transportation Center ; United States. Dept. of Transportation. University Transportation Centers (UTC) Program Less -
Subject/TRT Terms:

[+]

Algorithms Bayesian Network Complex Network Analysis Crash Analysis Crash Data Crash Risk Forecasting Data Mining Frequent Pattern Tree (FP Tree) Fuzzy C-means Clustering (FCM) Hazard-based Duration Model. M5P Tree Machine Learning Random Forest Traffic Crashes
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final: June 2014 – January 2018
Corporate Publisher:

Transportation Informatics University Transportation Center
Abstract:

The field of traffic accident analysis has long been dominated by traditional statistical analysis. With the recent advances in data collection, storage and archival methods, the size of accident datasets has grown significantly. This in turn has motivated research on applying data mining and Machine Learning algorithms, which are specifically designed to handle datasets with large dimensions, to traffic accident analysis. This project explores three specific applications of Data Mining and Machine Learning algorithms to traffic accident analysis. The first application explores the potential for using a modularity-optimizing community detection algorithm and association rules learning algorithm, to identify important accident characteristics. The second application proposes a novel Frequent Pattern tree (FP tree) based variable selection method, and then develops models for the real-time prediction of traffic accident risk. Finally, the third application proposes a novel approach to developing accident duration prediction models. The approach improves on the original M5P tree algorithm through the construction of a M5P-Hazard-Based Duration Model (HBDM).
Format:

PDF
Funding:

DTRT13-G-UTC48
Collection(s):

University Transportation Centers US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:b2e3a39cc35c24f2525f55369bf84f05896ec031dfd891f6793b28f61dfd8e9c
Download URL:

https://rosap.ntl.bts.gov/view/dot/40076/dot_40076_DS1.pdf
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Novel Machine Learning Methods for Accident Data Analysis

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