Abstract
Objective: A proof of concept analysis was conducted to establish whether link analysis could be applied to data from on-train recorders to detect patterns of behaviour that could act as leading indicators of potential safety issues.
Background: On-train data recorders capture data about driving behaviour on thousands of routine journeys every day, and offer a source of untapped data that could be used to offer insights into human behaviour.
Method: Data from seventeen journeys undertaken by six drivers on the same route over a sixteen hour period were analysed using link analysis, and four key metrics were examined: Number of links, Network Density, Diameter, and Sociometric Status.
Results: The results established that link analysis can be usefully applied to data captured from on-vehicle recorders. The four metrics revealed key differences in normal driver behaviour. These differences have promising construct validity as leading indicators.
Conclusion: Link analysis is one method that could be usefully applied to exploit data routinely gathered by on-vehicle data recorders. It facilitates a proactive approach to safety based on leading indicators, offers a clearer understanding of what constitutes normal driving behaviour, and identifies trends at the interface of people and systems, which is currently a key area of strategic risk.
Application: These research findings have direct applications in the field of transport data monitoring. They offer a means of automatically detecting patterns in driver behaviour that could act as leading indicators of problems during operation, and which could be used in the pro-active monitoring of driver competence, risk management and even infrastructure design.
Background: On-train data recorders capture data about driving behaviour on thousands of routine journeys every day, and offer a source of untapped data that could be used to offer insights into human behaviour.
Method: Data from seventeen journeys undertaken by six drivers on the same route over a sixteen hour period were analysed using link analysis, and four key metrics were examined: Number of links, Network Density, Diameter, and Sociometric Status.
Results: The results established that link analysis can be usefully applied to data captured from on-vehicle recorders. The four metrics revealed key differences in normal driver behaviour. These differences have promising construct validity as leading indicators.
Conclusion: Link analysis is one method that could be usefully applied to exploit data routinely gathered by on-vehicle data recorders. It facilitates a proactive approach to safety based on leading indicators, offers a clearer understanding of what constitutes normal driving behaviour, and identifies trends at the interface of people and systems, which is currently a key area of strategic risk.
Application: These research findings have direct applications in the field of transport data monitoring. They offer a means of automatically detecting patterns in driver behaviour that could act as leading indicators of problems during operation, and which could be used in the pro-active monitoring of driver competence, risk management and even infrastructure design.
| Original language | English |
|---|---|
| Pages (from-to) | 205-217 |
| Number of pages | 13 |
| Journal | Human Factors |
| Volume | 58 |
| Issue number | 2 |
| Early online date | 11 Dec 2015 |
| DOIs | |
| Publication status | Published - Mar 2016 |