Analysing journey-to-work data using complex networks
- Publication Type:
- Journal Article
- Citation:
- Journal of Transport Geography, 2018, 66 pp. 65 - 79
- Issue Date:
- 2018-01-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1-s2.0-S0966692317303988-main.pdf | Published Version | 1.99 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© 2017 Elsevier Ltd It is well known that journey-to-work (JTW) data can be represented using complex network graphs. What is less evident is the way in which this approach can be used to quantitatively analyse the structure, connectivity and dynamics of commuting behaviour. This paper employs a complex network approach to spatially disaggregated JTW data in order to examine commuting behaviour for six different modes of transport (car, car passengers, train, bus, cycling and walking) within three of the most populous metropolitan areas in Australia. A set of network measures (degree, strength, clustering coefficient, maximum cliques, average shortest path length and betweenness) are computed from both the unweighted and weighted graphs corresponding to JTW data for the Sydney, Melbourne and the South East Queensland regions from the time periods: 2001, 2006 and 2011. Results reveal a number of interesting dynamics, one being that Melbourne exhibits shorter (and presumed to be faster) alternate commuting paths than either Sydney or South East Queensland given its lower betweenness and shortest path values allied with higher clustering coefficients. The interpretation of these metrics demonstrates that complex networks have the capacity to reveal new insights from JTW data, by enabling a more comprehensive, systematic, empirical and fine-grained analysis of changes in commuting behaviour over time.
Please use this identifier to cite or link to this item: