IEEE Intelligent Transportation Systems Magazine (IEEE ITSM) 
To appear in 2020

Impact Factor: 3.294


AMSense: How Mobile Sensing Platforms Capture 
Pedestrian/Cyclist Spatiotemporal Properties in Cities

Abstract:

We present a design for a novel mobile sensing system (AMSense) 
that uses vehicles as mobile sensing nodes in a network to 
capture spatiotemporal properties of pedestrians and cyclists 
(active modes) in urban environments. In this dynamic, 
multi-sensor approach, real-time data, algorithms, and models 
are fused to estimate presence, positions and movements of 
active modes with information generated by a fleet of mobile 
sensing platforms. AMSense offers a number of advantages over 
the traditional methods using stationary sensor systems or 
more recently crowd-sourced data from mobile and wearable 
devices, as it represents a scalable system that provides 
answers to spatiotemporal resolution, intrusiveness, and dynamic 
network conditions. In this paper, we motivate the need and show 
the potential of such a sensing paradigm, which supports a host 
of new research and application development, and illustrate 
this with a practical urban sensing example. We propose a first 
design, elaborate on a variety of requirements along with 
functional challenges, and outline the research to be performed 
with the generated data.


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BibTeX:
@article{Vial:ITSM2020,
title = "AMSense: How Mobile Sensing Platforms Capture Pedestrian/Cyclist Spatiotemporal Properties in Cities",
journal = "IEEE Intelligent Transportation Systems Magazine",
year = "2020",
author = "Alphonse Vial and Winnie Daamen and Aaron Yi Ding and Bart van Arem and Serge Hoogendoorn",
}
How to cite:

Alphonse Vial and Winnie Daamen and Aaron Yi Ding and Bart van Arem and Serge Hoogendoorn, "AMSense: How Mobile Sensing Platforms Capture Pedestrian/Cyclist Spatiotemporal Properties in Cities", to appear in IEEE Intelligent Transportation Systems Magazine (ITSM), 2020.