Mobile Sensing for Data-Driven Mobility Modeling
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 1
Abstract
The use of mobile sensed location data for realistic human track generation is privacy sensitive. People are unlikely to share their private mobile phone data if their tracks were to be simulated. However, the ability to realistically generate human mobility in computer simulations is critical for advances in many domains, including urban planning, emergency handling, and epidemiology studies. In this paper, we present a data-driven mobility model to generate human spatial and temporal movement patterns on a real map applied to an agent based setting. We address the privacy aspect by considering collective participant transitions between semantic locations, defined in a privacy preserving way. Our modeling approach considers three cases which decreasingly use real data to assess the value in generating realistic mobility, considering data of 89 participants over 6079 days. First, we consider a dynamic case which uses data on a half-hourly basis. Second, we consider a data-driven case without time of day dynamics. Finally, we consider a homogeneous case where the transitions between locations are uniform, random, and not data-driven. Overall, we find the dynamic data-driven case best generates the semantic transitions of previously unseen participant data.
Authors and Affiliations
Kashif Zia, Arshad Muhammad, Katayoun Farrahi, Dinesh Kumar Saini
Keywords
Related Articles
- EP ID EP250594
- DOI 10.14569/IJACSA.2017.080153
- Views 125
- Downloads 0
IAX-JINGLE Network Architectures Based-One/Two Translation Gateways
Nowadays, Multimedia Communication has improved rapidly to allow people to communicate via the Internet. However, Internet users cannot communicate with each other unless they use the same chatting applications since eac...
Analysis of Spatially Modelled High Temperature Polymer Electrolyte Membrane Fuel Cell under Dynamic Load Conditions
This paper presents an interesting approach to observe the effects of the load variations on the performance of high temperature polymer electrolyte membrane fuel cell system, such as: hydrogen and air flow rate, output...
A New PHP Discoverer for Modisco
MoDisco is an Eclipse Generative Modeling Technologies project (GMT Project) intended to make easier the design and building of model-based solutions that are dedicated to legacy systems Model-Driven Reverse Engineering...
The Analysis of Anticancer Drug Sensitivity of Lung Cancer Cell Lines by using Machine Learning Clustering Techniques
Lung cancer is the commonest type of cancer with the highest fatality rate worldwide. There is continued research that experiments on drug development for lung cancer patients by assessing their responses to chemotherape...
Parallel Architecture for Face Recognition using MPI
The face recognition applications are widely used in different fields like security and computer vision. The recognition process should be done in real time to take fast decisions. Princi-ple Component Analysis (PCA) con...