Estimating Activity Patterns Using Spatio-temporal Data of Cell phone Networks

Please enable javascript in your browser.

Zahedi, Mostafa | 2016

401 Viewed

Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 49173 (09)
University: Sharif University of Technology
Department: Civil Engineering
Advisor(s): Shafahi, Yousef
Abstract:
The tendency towards using activity-based models to predict trip demand has increased dramatically over recent years, but these models have suffered insufficient data for calibration and the intrinsic problems of traditional methods imposes the need to search for better alternatives. This paper discusses ways to process the cellphone 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. Movements of each subscriber is described by a sequence of stays and trips and each stay is labelled by an activity. The type of activities are estimated using features such as land use, duration of stay, frequency of visit, arrival time to that activity and its departure time. Finally, the chains of trips are identified and different patterns that citizens follow to participate in activities are determined. These methods have been implemented on a dataset that comprises 144 million records of the cellphone locations of 300,000 citizens of Shiraz at five-minute intervals
Keywords:
Big Data ; Activity-Based Demand Modeling ; Spatiotemporal Dataset ; Mobile Telephony ; Activity-Travel Pattern ; Activity Patterns

No TOC