A low-cost automatic people-counting system at bus stops using Wi-Fi probe requests and deep learning
Document Type
Journal Article
Publication Date
2025
Subject Area
mode - bus, operations - scheduling, planning - integration, technology - passenger information, infrastructure - stop
Keywords
Deep learning, Automated people counting, Bus stops, Internet of Things, Wi-Fi probe requests, Neural networks
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.
Rights
Permission to publish the abstract has been given by SpringerLink, copyright remains with them.
Recommended Citation
Pronello, C., Anbarasan, D., Spoturno, F., & Terzolo, G. (2025). A low-cost automatic people-counting system at bus stops using Wi-Fi probe requests and deep learning. Public Transport, 17(1), 71-100.
