Abstract
In this paper we introduce MOBIUS, a smartphone-based system for remote tracking of citizens’ movements. By collecting smartphone’s sensor data such as accelerometer and gyroscope, along with self-report data, the MOBIUS system allows to classify the users’ mode of transportation. With the MOBIUS app the users can also activate GPS tracking to visualise their journeys and travelling speed on a map. The MOBIUS app is an example of a tracing app which can provide more insights into how people move around in an urban area. In this paper, we introduce the motivation, the architectural design and development of the MOBIUS app. To further test its validity, we run a user study collecting data from multiple users. The collected data are used to train a deep convolutional neural network architecture which classifies the transportation modes using with a mean accuracy of 89%.
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Notes
- 1.
Code available at Mobius_client.
- 2.
Code available at Mobius_server.
- 3.
Code available at Visual Inspection Tool.
- 4.
Code available at Sharpflow.
- 5.
After the collection of the data we found an option to record sensor readings without gravity directly on Android devices.
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Di Mitri, D., Asyraaf Mat Sanusi, K., Trebing, K., Bromuri, S. (2021). MOBIUS: Smart Mobility Tracking with Smartphone Sensors. In: Paiva, S., Lopes, S.I., Zitouni, R., Gupta, N., Lopes, S.F., Yonezawa, T. (eds) Science and Technologies for Smart Cities. SmartCity360° 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 372. Springer, Cham. https://doi.org/10.1007/978-3-030-76063-2_31
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