IEEE Intelligent Transportation Systems Magazine (IEEE ITSM) Impact Factor: 4.941 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. Pre-camera PDFBibTeX:
@article{Vial:ITSM2022,
author={Vial, Alphonse and Daamen, Winnie and Ding, Aaron Yi and van Arem, Bart and Hoogendoorn, Serge},
journal={IEEE Intelligent Transportation Systems Magazine},
title={AMSense: How Mobile Sensing Platforms Capture Pedestrian/Cyclist Spatiotemporal Properties in Cities},
year={2022},
volume={14},
number={1},
pages={29-43},
doi={10.1109/MITS.2019.2953509}
}
How to cite:
A. Vial, W. Daamen, A. Y. Ding, B. van Arem and S. Hoogendoorn. "AMSense: How Mobile Sensing Platforms Capture Pedestrian/Cyclist Spatiotemporal Properties in Cities". IEEE Intelligent Transportation Systems Magazine, Vol. 14, No. 1, 2022. doi: 10.1109/MITS.2019.2953509