Ordering Networks: Motorways and the Work of Managing Disruption

This thesis contributes to a new understanding of the motorway network and its traffic movements as a problem of practical accomplishment. It is based on a detailed ethnomethodological study of incident management in the Highways Agency’s motorway control room, which observes the methods operators use to detect, diagnose and clear incidents to accomplish safe and reliable traffic. Its main concern is how millions of vehicles can depend on the motorway network to fulfil obligations for travel when it is constantly compromised by disruption from congestion, road accidents and vehicle breakdowns. It argues that transport geography and new mobilities research have overlooked questions of practical accomplishment; they tend to treat movement as an inevitable demand, producing fixed technical solutions to optimise it, or a self-evident phenomenon, made meaningful only through the intensely human experience of mobility. In response, the frame of practical accomplishment is developed to analyse the ways in which traffic is ongoingly organised through the situated and contingent practices that take place in the control room. The point is that traffic does not move by magic; it has to be planned for, produced and persistently worked at. This is coupled with an understanding of network topology that reconsiders the motorway network as always in process by virtue of the materially heterogeneous relations it keeps, drawing attention to the intensely collaborative nature of work between operators and technology that permits the management of disruption at-a-distance and in real time. This work is by no means straightforward – the actions of monitoring, detecting, diagnosing and classifying incidents and managing traffic are revealed to be complexly situated and prone to uncertainty, requiring constant ordering work to accomplish them. In conclusion, this thesis argues for the frame of practical accomplishment to be taken seriously, rendering the work of transport networks available for sustained analysis.