ACM MSWiM 2019 (acceptance rate 29%, A-Rank conference)

Where Are You Going Next? A Practical Multi-dimensional Look at Mobility Prediction


Understanding and predicting mobility are essential for the 
design and evaluation of future mobile edge caching and 
networking. Consequently, research on human mobility prediction 
has drawn significant attention in the last decade. Employing 
information-theoretic concepts and machine learning methods, 
earlier research has shown evidence that human behavior can be 
highly predictable. Whether high predictability manifests 
itself for different modes of device usage, across spatial and 
temporal dimensions is still debatable. Despite existing 
studies, more investigations are needed to capture intrinsic 
mobility characteristics constraining predictability, to 
explore more dimensions (e.g. device types) and spatiotemporal 
granularities, especially with the change in human behavior 
and technology. We investigate practical predictability of next 
location visitation across three different dimensions: device 
type, spatial granularity and temporal spans using an extensive 
longitudinal dataset, with fine spatial granularity (AP level) 
covering 16 months. The study reveals device type as an 
important factor affecting predictability. Ultra-portable 
devices such as smartphones have "on-the-go" mode of usage 
(and hence dubbed "Flutes"), whereas laptops are "sit-to-use" 
(dubbed "Cellos"). The goal of this study is to investigate 
practical prediction mechanisms to quantify predictability as 
an aspect of human mobility modeling, across time, space and 
device types. We apply our systematic analysis to wireless 
traces from a large university campus. We compare several 
algorithms using varying degrees of temporal and spatial 
granularity for the two modes of devices; Flutes vs. Cellos. 
Through our analysis, we quantify how the mobility of Flutes 
is less predictable than the mobility of Cellos. In addition, 
this pattern is consistent across various spatio-temporal 
granularities, and for different methods (Markov chains, 
neural networks/deep learning, entropy-based estimators). 
This work substantiates the importance of predictability as 
an essential aspect of human mobility, with direct 
application in predictive caching, user behavior modeling 
and mobility simulations.

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ACM Library Access

 author = {Alipour, Babak and Tonetto, Leonardo and Ketabi, Roozbeh and Yi Ding, Aaron and Ott, J\"{o}rg and Helmy, Ahmed},
 title = {Where Are You Going Next?: A Practical Multi-dimensional Look at Mobility Prediction},
 booktitle = {Proceedings of the 22Nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems},
 series = {MSWIM '19},
 year = {2019},
 isbn = {978-1-4503-6904-6},
 location = {Miami Beach, FL, USA},
 pages = {5--12},
 numpages = {8},
 url = {},
 doi = {10.1145/3345768.3355923},
 acmid = {3355923},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {devicetypes, markovchain, mobility, neuralnetworks, prediction, wirelessnetworks},
How to cite:

Babak Alipour, Leonardo Tonetto, Roozbeh Ketabi, Aaron Yi Ding, Joerg Ott, and Ahmed Helmy. 2019. Where Are You Going Next?: A Practical Multi-dimensional Look at Mobility Prediction. In Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWIM '19). ACM, New York, NY, USA.