Day-to-day variation in excess commuting: An exploratory study of Brisbane, Australia. (January 2019)

• Record Type: Journal Article
• Authors: Zhou, Jiangping; Murphy, Enda
• Is Part Of: Journal of transport geography. Volume 74(2019)
• Page Start: 223
• Page End: 232
• Publication Date: 2019-01
• Subjects: Excess commuting -- Indicator -- Variation -- Big data -- Temporal -- Brisbane; Transportation -- Periodicals; Telecommunication -- Periodicals; Transport -- Périodiques; Télécommunications -- Périodiques; Telecommunication; Transportation; Periodicals; 388
• DOI: 10.1016/j.jtrangeo.2018.11.014
• Languages: English
• ISSNs: 0966-6923

Abstract

Abstract: Commuting patterns where most if not all 'trips are optimised relative to a given distribution of jobs and housing can result in personal and socio-economic benefits. Excess commuting indicators provide useful information for academics and policy analysts to evaluate how the actual commuting pattern deviates from an optimal pattern where commuting costs are minimised. While actual commuting patterns vary from day-to-day, academic researchers have yet to quantify the temporal variation in these indicators over short time periods. This may be due to the lack of available longitudinal data as input for excess commuting indicators. This study shows that new, open and/or big data (NOBD) (e.g. smartcard data) can be exploited to serve as the input for such analysis. In this regard, our study uses half a year's worth of smartcard data from Brisbane, Australia to first derive/aggregate origins and destinations by small areas of the probable commuting trips by transit on all 122 weekdays over the study period. The study quantifies the day-to-day variation in excess commuting indicators for these trips and finds that excess commuting indicators vary considerably from one day to the next. Nevertheless, daily variations occur within a relatively consistent range which can be planned for. Our research suggests that more conscious and systematic utilisation of NOBD could change how commuting flows in cities are quantified, monitored and planned. In addition, with NOBD, we can more efficiently detect daily outliers in commuting patterns.