Toggle Main Menu Toggle Search

Open Access padlockePrints

Local knowledge in rail signalling and balancing trade-offs

Lookup NU author(s): Dr David GolightlyORCiD



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


The control of rail signalling is known to be highly dependent on local knowledge and local factors. It also known to be highly cognitive in its nature involving a constant balancing of system performance within the constraints of safety. In the current paper, data generated through field work with signallers were used to understand the role of local knowledge, set against the background of an existing Local Knowledge Framework (Pickup et al., 2013) that was proposed to help determine the contents and mechanisms behind local knowledge in rail signalling. The field work included interviews with signallers and operations managers along with observations of signaller work. The results showed that the local knowledge framework needs to be expanded to include aspects related to the general public at user worked crossings and level crossings. In addition, the analysis highlights some of the issues with the transmission of local knowledge. The paper then discusses some of the gaps in the current framework, highlighting the importance not only of local knowledge for specific functions of signalling, but how these interact to support trade-offs to balance performance with safety. The implications for the design of signaller work are discussed. Signalling, rail control, local knowledge

Publication metadata

Author(s): Golightly D, Young MS

Publication type: Article

Publication status: Published

Journal: Applied Ergonomics

Year: 2022

Volume: 102

Print publication date: 01/07/2022

Online publication date: 01/03/2022

Acceptance date: 09/02/2022

Date deposited: 06/04/2022

ISSN (print): 0003-6870

ISSN (electronic): 1872-9126

Publisher: Elsevier Ltd


DOI: 10.1016/j.apergo.2022.103714

ePrints DOI: 10.57711/25z8-6h62


Altmetrics provided by Altmetric


Funder referenceFunder name
Rail Accident Investigation Branch