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Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

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Published on: June 10, 2021

Artificial intelligence-aided railroad trespassing detection and data analytics: Methodology and a case study.

Zhipeng Zhang¹, Asim Zaman², Jinxuan Xu³

¹Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, China; Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, USA.

Accident; Analysis and Prevention

|February 17, 2022

Summary

This summary is machine-generated.

Artificial Intelligence (AI) can now automatically detect railroad trespassing incidents using video data. This AI tool analyzes footage to identify trespassers, aiding in proactive safety measures and reducing rail-related fatalities.

Keywords:

Artificial Intelligence Computer vision Railroad safety Risk management Trespassing

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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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Published on: February 25, 2013

Area of Science:

Transportation Safety
Artificial Intelligence
Computer Vision

Background:

Trespassing is the primary cause of rail-related fatalities in the U.S., with minimal reduction in recent years.
Monitoring extensive railroad video data for trespassing is labor-intensive and inefficient.

Purpose of the Study:

Develop an AI-powered framework for automatic detection of trespassing events.
Leverage big video data for enhanced railroad safety risk analysis.

Main Methods:

A deep learning-based AI tool was developed for automated trespassing detection.
The AI framework identifies trespassers, categorizes violator types, and logs event data.
Over 1,600 hours of archival video footage were analyzed.

Main Results:

Approximately 3,000 trespassing events were detected at a New Jersey grade crossing.
The AI successfully generated video clips and documented trespassing event details.
The study demonstrates the feasibility of AI in analyzing large-scale video data for safety.

Conclusions:

AI offers a robust solution for analyzing surveillance data to mitigate trespassing risks.
The generated data can inform human factors research and proactive safety initiatives.
Improved safety for train crews, passengers, and the public is achievable through AI-driven solutions.