<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The usage of mobile phones is nowadays reaching full penetration rate in most countries. Smartphones are a valuable source for urban planners to understand and investigate passengers’ behavior and recognize travel patterns more precisely. Different investigations tried to automatically extract transit mode from sensors embedded in the phones such as GPS, accelerometer, and gyroscope. This allows to reduce the resources used in travel diary surveys, which are time-consuming and costly. However, figuring out which mode of transportation individuals use is still challenging. The main limitations include GPS, and mobile sensor data collection, and data labeling errors. First, this paper aims at solving a transport mode classification problem including (still, walking, car, bus, and metro) and then as a first investigation, presents a new algorithm to compute waiting time and access time to public transport stops based on a random forest model. Several public transport trips with different users were saved in Rome to test our access trip phase recognition algorithm. We also used Convolutional Neural Network as a deep learning algorithm to automatically extract features from one sensor (linear accelerometer), obtaining a model that performs well in predicting five modes of transport with the highest accuracy of 0.81%.</p>
</abstract>

The usage of mobile phones is nowadays reaching full penetration rate in most countries. Smartphones are a valuable source for urban planners to understand and investigate passengers’ behavior and recognize travel patterns more precisely. Different investigations tried to automatically extract transit mode from sensors embedded in the phones such as GPS, accelerometer, and gyroscope. This allows to reduce the resources used in travel diary surveys, which are time-consuming and costly. However, figuring out which mode of transportation individuals use is still challenging. The main limitations include GPS, and mobile sensor data collection, and data labeling errors. First, this paper aims at solving a transport mode classification problem including (still, walking, car, bus, and metro) and then as a first investigation, presents a new algorithm to compute waiting time and access time to public transport stops based on a random forest model. Several public transport trips with different users were saved in Rome to test our access trip phase recognition algorithm. We also used Convolutional Neural Network as a deep learning algorithm to automatically extract features from one sensor (linear accelerometer), obtaining a model that performs well in predicting five modes of transport with the highest accuracy of 0.81%.

Smartphone-Based Recognition of Access Trip Phase to Public Transport Stops Via Machine Learning Models

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The usage of mobile phones is nowadays reaching full penetration rate in most countries. Smartphones are a valuable source for urban planners to understand and...

Smartphone-Based Recognition of Access Trip Phase to Public Transport Stops Via Machine Learning Models

Online veröffentlicht: 16. Nov. 2022

Seitenbereich: 273 - 283

DOI: https://doi.org/10.2478/ttj-2022-0022

Schlüsselwörter
transport mode detection, machine learning, trip phase recognition, urban trips on public transport

© 2022 Seyed Hassan Hosseini et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Smartphone-Based Recognition of Access Trip Phase to Public Transport Stops Via Machine Learning Models

Online veröffentlicht: 16. Nov. 2022

Seitenbereich: 273 - 283

DOI: https://doi.org/10.2478/ttj-2022-0022

Schlüsselwörtertransport mode detection, machine learning, trip phase recognition, urban trips on public transport

© 2022 Seyed Hassan Hosseini et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
transport mode detection, machine learning, trip phase recognition, urban trips on public transport