Modelling cellphone trace travel mode with neural networks using transit smartcard and home interview survey data
This study proposes a framework to impute travel mode for trips identified from cellphone traces by developing a deep neural network model. In our framework, we use the trips from a home interview survey and transit smartcard data, for which the travel mode is known, to create a set of artificial pseudo-cellphone traces. The generated artificial pseudo-cellphone traces with known mode are then used to train a deep neural network classifier. We further apply the trained model to infer travel modes for the cellphone traces from cellular network data. The empirical case study region is Montevideo, Uruguay, where high-quality data are available for all three types of data used in the analysis: a large dataset of cellphone traces, a large dataset of public transit smartcard transactions, and a small household travel survey. The results can be used to create an enhanced representation of origin-destination trip-making in the region by time of day and travel mode.
This work is licensed under a Creative Commons Attribution 4.0 International License.
EJTIR is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence. The license means that anyone is free to share (to copy, distribute, and transmit the work), to remix (to adapt the work) under the following conditions:
- The original authors must be given credit
- For any reuse or distribution, it must be made clear to others what the license terms of this work are
- Any of these conditions can be waived if the copyright holders give permission
- Nothing in this license impairs or restricts the author's moral rights