Investigating the potential of social network data for transport demand models

TL;DR

This study evaluates the use of geo-referenced Twitter data for modeling transport demand, comparing it with traditional travel surveys and smart card data, and finds it promising despite some limitations.

Contribution

It demonstrates the potential of social network data for activity location detection and mobility analysis, highlighting methods and challenges involved.

Findings

Kernel density estimation effectively detects activity locations.

Twitter data reveals more activity locations per user than surveys.

Spatial separation patterns are similar but show differences at extreme distances.

Abstract

Location-based social network data offers the promise of collecting the data from a large base of users over a longer span of time at negligible cost. While several studies have applied social network data to activity and mobility analysis, a comparison with travel diaries and general statistics has been lacking. In this paper, we analysed geo-referenced Twitter activities from a large number of users in Singapore and neighbouring countries. By combining this data, population statistics and travel diaries and applying clustering techniques, we addressed detection of activity locations, as well as spatial separation and transitions between these locations. Kernel density estimation performs best to detect activity locations due to the scattered nature of the twitter data; more activity locations are detected per user than reported in the travel survey. The descriptive analysis shows that determining home locations is more difficult than detecting work locations for most planning zones. Spatial separations between detected activity locations from Twitter data - as reported in a travel survey and captured by public transport smart card data - are mostly similarly distributed, but also show relevant differences for very short and very long distances. This also holds for the transitions between zones. Whether the differences between Twitter data and other data sources stem from differences in the population sub-sample, clustering methodology, or whether social networks are being used significantly more at specific locations must be determined by further research. Despite these shortcomings, location-based social network data offers a promising data source for insights into activity locations and mobility patterns, especially for regions where travel survey data is not readily available.