The utility of multilevel models for continuous-time feature selection of spatio-temporal networks

Gadd, S. C., Comber, A., Tennant, P., Gilthorpe, M. S. and Heppenstall, A. J. (2022) The utility of multilevel models for continuous-time feature selection of spatio-temporal networks. Computers, Environment and Urban Systems, 91, 101728. (doi: 10.1016/j.compenvurbsys.2021.101728)

Abstract

Many models for the analysis of spatio-temporal networks specify time as a series of discrete steps. This either requires evenly spaced measurement times or the aggregation of data into measurement windows. This can lead to the introduction of bias. An alternative is to use continuous-time models, for example, multilevel models. Models capturing complex spatio-temporal variation are often difficult to visualise and interpret. This can be addressed by simplifying the results, for example by extracting ‘features’ of interest (such as maxima or minima) of temporal patterns associated with different network connections. This paper uses simulation to evaluate the accuracy and precision with which b-spline-based multilevel models (a flexible form of continuous-time model that can easily capture complex variation associated with a spatio-temporal network structure) capture the timing and extent of maximum delays to journeys made between pairs of stations in a small railway network. On average models captured the timing and extent of maximum delay with small bias, but there was evidence of overestimation and underestimation of low and high values of these features, respectively. This systematic bias may have partially caused the undercoverage of credible intervals for the pattern features. Alternative model specifications – specifically to capture x-axis random variation, for example – should be considered in future work.

• Item Type: Articles
• Additional Information: Sarah Gadd is funded by the Economic and Social Research Council [ES/P000746/1]. Alison Heppenstall is funded by an Economic and Social Research Council-Alan Turing fellowship [ES/R007918/1]. Mark Gilthorpe is funded by the Alan Turing Institute [EP/N510129/1]. Alexis Comber received no specific funding for this work. This work uses data from TFL Open Data.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Heppenstall, Professor Alison
• Authors: Gadd, S. C., Comber, A., Tennant, P., Gilthorpe, M. S., and Heppenstall, A. J.
• College/School: College of Social Sciences > School of Social and Political Sciences; College of Social Sciences > School of Social and Political Sciences > Urban Studies
• Journal Name: Computers, Environment and Urban Systems
• Publisher: Elsevier
• ISSN: 0198-9715
• ISSN (Online): 1873-7587
• Published Online: 28 October 2021
• Copyright Holders: Copyright © 2021 Elsevier Ltd.
• First Published: First published in Computers, Environment and Urban Systems 91: 101728
• Publisher Policy: Reproduced in accordance with the publisher copyright policy