Estimating activity patterns using spatio-temporal data of cell phone networks

ABSTRACT

The tendency towards using activity-based models to predict trip demand has increased dramatically over recent years. However, these models have suffered from insufficient data for calibration, and the intrinsic problems of traditional methods impose the need to search for better alternatives. This paper discusses ways to process cell phone spatio-temporal data in a manner that makes it comprehensible for traffic interpretations and proposes methods on how to infer urban mobility and activity patterns from the aforementioned data. The movements of each subscriber are described by a sequence of stops and trips, and each stop is labelled by an activity. The types of activities are estimated using features such as duration of stop, frequency of visit, arrival time to that activity and its departure time. Finally, the chains of the trips are identified, and different patterns that citizens follow to participate in activities are determined. These methods have been implemented on a dataset that consists of 144 million records of the cell phone locations of 300,000 citizens of Shiraz at five-minute intervals.

KEYWORDS:

• Cell phone spatio-temporal data
• activity-based models
• activity patterns
• big data

Disclosure statement

No potential conflict of interest was reported by the authors.