em-arch: A system architecture for reproducible and extensible collection of human mobility data

Kalyanaraman Shankari, Pavan Yedavalli, Ipsita Banerjee, Taha Rashidi, Randy H. Katz, Paul Waddell and David E. Culler

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2019-88
May 20, 2019

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-88.pdf

Smartphones have revolutionized transportation for travelers by providing mapping services that tell users how to get to a specific destination as well as ride-hailing services that help them get there. However, the data collected from these services are limited in temporal, spatial, or categorical scope. For a variety of solutions in urban planning, transportation, or healthcare, collecting rich and granular data of human mobility is critical. Yet, there are few end-to-end, open-source platforms that allow the development of human mobility systems (HMS) to collect, access, and leverage these data in a seamless and customized fashion.

We present a novel platform for HMS studies and outline an architecture for such platforms generally. The open-source, extensible data collection platform can be customized to address a wide variety of disciplines. It is validated by usage patterns from three use cases from applied projects. The platform architecture defines the structure of the platform, identifies the key modules and classifies them as core or extensible.

Our use cases used an average of 64% of the features of the platform, with approximately 3-4 months of part-time CS undergraduate time for each new case. Every use case contributed at least one extension, primarily client-related, back to the platform.

We hope that the reusability of the platform, combined with the rigor of the architecture will propel the field of human mobility systems (HMS).


BibTeX citation:

@techreport{Shankari:EECS-2019-88,
    Author = {Shankari, Kalyanaraman and Yedavalli, Pavan and Banerjee, Ipsita and Rashidi, Taha and Katz, Randy H. and Waddell, Paul and Culler, David E.},
    Title = {em-arch: A system architecture for reproducible and extensible collection of human mobility data},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2019},
    Month = {May},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-88.html},
    Number = {UCB/EECS-2019-88},
    Abstract = {Smartphones have revolutionized transportation for travelers by providing mapping services that tell users how to get to a specific destination as well as ride-hailing services that help them get there. However, the data collected from these services are limited in temporal, spatial, or categorical scope. For a variety of solutions in urban planning, transportation, or healthcare, collecting rich and granular data of human mobility is critical. Yet, there are few end-to-end, open-source platforms that allow the development of human mobility systems (HMS) to collect, access, and leverage these data in a seamless and customized fashion.

We present a novel platform for HMS studies and outline an architecture for such platforms generally. The open-source, extensible data collection platform can be customized to address a wide variety of disciplines. It is validated by usage patterns from three use cases from applied projects. The platform architecture defines the structure of the platform, identifies the key modules and classifies them as core or extensible.

Our use cases used an average of 64% of the features of the platform, with approximately 3-4 months of part-time CS undergraduate time for each new case. Every use case contributed at least one extension, primarily client-related, back to the platform.

We hope that the reusability of the platform, combined with the rigor of the architecture will propel the field of human mobility systems (HMS).}
}

EndNote citation:

%0 Report
%A Shankari, Kalyanaraman
%A Yedavalli, Pavan
%A Banerjee, Ipsita
%A Rashidi, Taha
%A Katz, Randy H.
%A Waddell, Paul
%A Culler, David E.
%T em-arch: A system architecture for reproducible and extensible collection of human mobility data
%I EECS Department, University of California, Berkeley
%D 2019
%8 May 20
%@ UCB/EECS-2019-88
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-88.html
%F Shankari:EECS-2019-88