Time-geographic relationships between vector fields of activity patterns and transport systems. (January 2015)

• Record Type: Journal Article
• Authors: Liu, Xintao; Yan, Wai Yeung; Chow, Joseph Y.J.
• Is Part Of: Journal of transport geography. Volume 42(2015)
• Page Start: 22
• Page End: 33
• Publication Date: 2015-01
• Subjects: GIS -- Big Data -- Activity travel patterns -- Kernel density -- Vector density -- Transport systems; Transportation -- Periodicals; Telecommunication -- Periodicals; Transport -- Périodiques; Télécommunications -- Périodiques; Telecommunication; Transportation; Periodicals; 388
• DOI: 10.1016/j.jtrangeo.2014.10.005
• Languages: English
• ISSNs: 0966-6923

Abstract

Highlights: Proposed population-based vector field to visualize time-geographic demand momentum. Estimated the field with kernel density from sampled activity trajectories. Defined transport systems as vector fields to quantify demand momentum projections. Illustrated the use of the method to measure field differentials in before-after studies. Abstract: The rise of urban Big Data has made it possible to use demand data at an operational level, which is necessary to directly measure the economic welfare of operational strategies and events. GIS is the primary visualization tool in this regard, but most current methods are based on scalar objects that lack directionality and rate of change – key attributes of travel. The few studies that do consider field-based time geography have largely looked at vector fields for individuals, not populations. A population-based vector field is proposed for visualizing time-geographic demand momentum. The field is estimated using a vector kernel density generated from observed trajectories of a sample population. By representing transport systems as vector fields that share the same time–space domain, demand can be projected onto the systems to visualize relationships between them. This visualization tool offers a powerful approach to visually correlate changes in the systems with changes in demand, as demonstrated in a case study of the Greater Toronto Area using data from the 2006 and 2011 Transportation Tomorrow Surveys. As a result, it is now possible to measure in real time the effects of disasters on the economic welfare of a population, or quantify the effects of operational strategies and designs on the behavioural activity patterns of the population.