M.Sc. Kai Zhao will defend his doctoral thesis Understanding Urban Human Mobility for Network Applications on Friday the 6th of November 2015 at 12 o'clock in the University of Helsinki Chemicum Building, Auditorium A129 (A.I. Virtasen aukio 1). His opponent is Professor Vassilis Kostakos (University of Oulu) and custos Professor Sasu Tarkoma (University of Helsinki). The defence will be held in English.
Understanding Urban Human Mobility for Network Applications
Understanding urban human mobility is crucial for various mobile and network applications. This thesis addresses two key challenges presented by mobile applications, namely urban mobility modeling and its applications in Delay Tolerant Networks (DTNs).
First, we model urban human mobility with transportation mode information. Our research is based on two real-life GPS datasets containing approximately 20 and 10 million GPS samples. Previous research has suggested that the trajectories in human mobility have statistically similar features as Lévy Walks. We attempt to explain the Lévy Walks behavior by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bike, Train/ Subway or Car/Taxi/Bus. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal distribution rather than a power-law distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight distributions associated with each modality is a power-law distribution, providing an explanation for the emergence of Lévy Walks patterns that characterize human mobility patterns.
Second, we find that urban human mobility exhibits strong spatial and temporal patterns. We leverage such human mobility patterns to derive an optimal routing algorithm that minimizes the hop count while maximizing the number of needed nodes in DTNs. We propose a solution framework, called Ameba, for timely data delivery in DTNs. Simulation results with experimental traces indicate that Ameba achieves a comparable delivery ratio to a Flooding-based algorithm, but with much lower overhead.
Third, we infer the functions of the sub-areas in three cities by analyzing urban mobility patterns. The analysis is based on three large taxi GPS datasets in Rome, San Francisco and Beijing containing 21, 11 and 17 million GPS points, respectively. We categorize the city regions into four categories, workplaces, entertainment places, residential places and other places. We show that the identification of these functional sub-areas can be utilized to increase the efficiency of urban DTN applications.
The three topics pertaining to urban mobility examined in the thesis support the design and implementation of network applications for urban environments.
Availability of the dissertation
An electronic version of the doctoral dissertation is available on the e-thesis site of the University of Helsinki at http://urn.fi/URN:ISBN:978-951-51-1693-2.
Printed copies are available on request from Kai Zhao: tel. 02941 51372 or kai.zhao@cs.helsinki.fi.