Mining and Learning from Railway Safety Data with Graphs and Tensors

Chen, Jia; Papalexakis, Evangelos

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Mining and Learning from Railway Safety Data with Graphs and Tensors

2024-09-15
By Chen, Jia ; Papalexakis, Evangelos

File Language:

English

Details

Creators:

Chen, Jia ; Papalexakis, Evangelos
Corporate Creators:

University Transportation Center for Railway Safety (UTCRS) UTC ; University of California, Riverside ; University of Texas Rio Grande Valley
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:
Publication/ Report Number:

UTCRS-UCR-O2CY23
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

June 1, 2023 – August 31, 2024
Corporate Publisher:

University Transportation Center for Railway Safety (UTCRS) Tier-1 University Transportation Center (UTC)
Abstract:

Railway systems are very complex pieces of cyberinfrastructure, interfacing with a number of transportation agents and other pieces of cyberinfrastructure. For instance, a railway crossing includes interactions between the railway system and a traffic intersection. Such a rich ecosystem of interactions among heterogeneous agents poses fascinating research challenges in modeling railway systems with data and conducting data-driven railway crossing safety assessment. In this project we leverage and extend powerful tensor and graph mining methods which can extract “needles in the haystack” within the abundance of collected data and produce actionable insights to better understand emerging accident patterns from historical data, identify underlying similarities in such patterns.
Format:

PDF
Funding:

69A3552348340
Download URL:

https://rosap.ntl.bts.gov/view/dot/79292/dot_79292_DS1.pdf
File Type:

[PDF - 1.06 MB]
Collection(s):

University Transportation Centers
Main Document Checksum:

urn:sha-512:9af95b49a1df7428ee2266b2bab7a28a350766dfb6ed519c37c4ee56bc36bbc53cbc622516e49df66842aef9bde8a52c215de8b2d1b0a45fba8deea355675941

File Language:

English

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