The task:
Mobility patterns play a crucial role in designing efficient public transportation systems in big cities. Taxicab routes serve as the fundamental basis for uncovering these patterns. The student will develop a link prediction to give a traffic prediction in these temporal networks.
The problem is quite similar to this study [1], however, they use the “classic” New York dataset, that works with a zone-based resolution. In this work, the student will go one step forward, and propose, how the original raw GPS data can be processed to provide meaningful networks and how this data can be used for prediction purposes. The graph convolutional network structure is also used in ref. [2]. However, the structure of the applied datasets was significantly different. The previously applied dataset was recorded using sensor stations and the models concentrated on the prediction of future traffic speed. However, in our case, the data is recorded by the GPS of the taxis, hence, the data is not recorded in fixed points, but in a dynamic way, which we need to process to obtain the spatial and temporal characteristics. Moreover, the aim is the prediction of the traffic intensity and not the speed of the vehicles.
This integrated methodology allowed us to gain valuable insights into the travel behaviors and preferences of commuters, providing a foundation for designing efficient and targeted public transportation services. To validate the effectiveness of the approach, the student will apply it to GPS data obtained from a taxicab company in Budapest.
The thesis must contain:
- Review of literature (network science)
- Data processing (temporal network analyses, link prediction)
- Development of a Python-based algorithm
- A detailed description of the algorithms and framework and a demonstration of its application
[1] https://www.mdpi.com/1424-8220/22/16/5982
[2] https://arxiv.org/abs/1709.04875
[3] https://aaai.org/papers/11836-deep-multi-view-spatial-temporal-network-for-taxi-demand-prediction/
[4] https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0274779