Large Scale Passenger Detection with Smartphone/Bus Implicit Interaction
and Multisensory Unsupervised Cause-effect Learning
- URL: http://arxiv.org/abs/2202.11962v1
- Date: Thu, 24 Feb 2022 08:50:32 GMT
- Title: Large Scale Passenger Detection with Smartphone/Bus Implicit Interaction
and Multisensory Unsupervised Cause-effect Learning
- Authors: Valentino Servizi, Dan R. Persson, Francisco C. Pereira, Hannah
Villadsen, Per B{\ae}kgaard, Jeppe Rich, Otto A. Nielsen
- Abstract summary: We focus on the concept of implicit Be-in/Be-out (BIBO) smartphone-sensing agnostic and classification.
To enable the training of a model based on GPS features against the BLE pseudo-label, we propose the Cause-Effect Multitask Wasserstein Autoencoder (CEMWA)
- Score: 5.449283796175882
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Intelligent Transportation Systems (ITS) underpin the concept of Mobility as
a Service (MaaS), which requires universal and seamless users' access across
multiple public and private transportation systems while allowing operators'
proportional revenue sharing. Current user sensing technologies such as
Walk-in/Walk-out (WIWO) and Check-in/Check-out (CICO) have limited scalability
for large-scale deployments. These limitations prevent ITS from supporting
analysis, optimization, calculation of revenue sharing, and control of MaaS
comfort, safety, and efficiency. We focus on the concept of implicit
Be-in/Be-out (BIBO) smartphone-sensing and classification.
To close the gap and enhance smartphones towards MaaS, we developed a
proprietary smartphone-sensing platform collecting contemporary Bluetooth Low
Energy (BLE) signals from BLE devices installed on buses and Global Positioning
System (GPS) locations of both buses and smartphones. To enable the training of
a model based on GPS features against the BLE pseudo-label, we propose the
Cause-Effect Multitask Wasserstein Autoencoder (CEMWA). CEMWA combines and
extends several frameworks around Wasserstein autoencoders and neural networks.
As a dimensionality reduction tool, CEMWA obtains an auto-validated
representation of a latent space describing users' smartphones within the
transport system. This representation allows BIBO clustering via DBSCAN.
We perform an ablation study of CEMWA's alternative architectures and
benchmark against the best available supervised methods. We analyze
performance's sensitivity to label quality. Under the na\"ive assumption of
accurate ground truth, XGBoost outperforms CEMWA. Although XGBoost and Random
Forest prove to be tolerant to label noise, CEMWA is agnostic to label noise by
design and provides the best performance with an 88\% F1 score.
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