A low-cost automatic people-counting system at bus stops using Wi-Fi probe requests and deep learning

Cristina Pronello (), Deepan Anbarasan (), Felipe Spoturno and Giulia Terzolo
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Cristina Pronello: Politecnico di Torino
Deepan Anbarasan: Politecnico di Torino
Felipe Spoturno: Politecnico di Torino
Giulia Terzolo: Politecnico di Torino

Public Transport, 2025, vol. 17, issue 1, No 3, 100 pages

Abstract: Abstract Counting people is an important part of people-centric applications, and the increase in the number of IoT devices has allowed the collection of huge amounts of data to facilitate people counting. The present study seeks to provide a novel, low-cost, automatic people-counting system for the use at bus stops, featuring a sniffing device that can capture Wi-Fi probe requests, and overcoming the problem of Media Access Control (MAC) randomization using deep learning. To make manual data collection considerably easier, a “People Counter” app was designed to collect ground truth data in order to train the model with higher accuracy. A user-friendly, operating system-independent dashboard was created to display the most relevant metrics. A two-step methodological approach was followed comprising device choice and data collection; data analysis and algorithm development. For the data analysis, three different approaches were tested, and among these a deep-learning approach using Convolutional Recurrent Neural Network (CRNN) with Long Short-term Memory (LSTM) architecture produced the best results. The optimal deep learning model predicted the number of people at the stop with a mean absolute error of ~ 1.2 persons, which can be considered a good preliminary result, considering that the experiment was done in a very complex open environment. People-counting systems at bus stops can support better bus scheduling, improve the boarding and alighting time of passengers, and aid the planning of integrated multi-modal transport system networks.

Keywords: Deep learning; Automated people counting; Bus stops; Internet of Things; Wi-Fi probe requests; Neural networks (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s12469-023-00349-0

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