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Can Link Analysis Be Applied to Identify Behavioral Patterns in Train Recorder Data?

Ailsa Strathie¹, Guy H Walker²

¹Institute of Infrastructure and Environment, Heriot-Watt University, Edinburgh, United Kingdom a.strathie@hw.ac.uk.

|December 15, 2015

Summary

This summary is machine-generated.

Link analysis of on-train recorder data reveals driver behavior patterns. These patterns serve as leading indicators for proactive safety monitoring and risk management in transportation.

Keywords:

data recorders driving graph theory leading indicators link analysis

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Area of Science:

Transportation safety
Human factors in driving
Data analytics

Background:

On-train data recorders collect extensive daily driving behavior data.
This data offers a valuable, yet largely untapped, resource for understanding human behavior in operational contexts.

Purpose of the Study:

To determine if link analysis can be applied to on-train recorder data.
To identify behavioral patterns that may serve as leading indicators of potential safety issues.

Main Methods:

Link analysis was applied to data from 17 journeys by six drivers over 16 hours.
Key metrics analyzed included number of links, network density, diameter, and sociometric status.

Main Results:

Link analysis is a viable method for on-vehicle recorder data.
The four metrics identified distinct differences in normal driver behavior.
These differences show promise as valid leading indicators for safety.

Conclusions:

Link analysis effectively utilizes routinely collected on-vehicle data.
This approach enables proactive safety measures through leading indicators.
It enhances understanding of normal driving behavior and identifies human-system interaction trends, a critical risk area.