Graph Input Representations for Machine Learning Applications in Urban Network Analysis
This work addresses the challenge of optimizing machine learning models for urban network analysis, which is incremental as it focuses on evaluating input representations rather than introducing a new method.
The paper tackled the problem of how different graph input representations affect predictive model performance in urban network analysis, finding that representations incorporating temporal information achieved the highest accuracy with an RMSE of 1.42 in predicting taxi tips using a New York road network dataset.
Understanding and learning the characteristics of network paths has been of particular interest for decades and has led to several successful applications. Such analysis becomes challenging for urban networks as their size and complexity are significantly higher compared to other networks. The state-of-the-art machine learning (ML) techniques allow us to detect hidden patterns and, thus, infer the features associated with them. However, very little is known about the impact on the performance of such predictive models by the use of different input representations. In this paper, we design and evaluate six different graph input representations (i.e., representations of the network paths), by considering the network's topological and temporal characteristics, for being used as inputs for machine learning models to learn the behavior of urban networks paths. The representations are validated and then tested with a real-world taxi journeys dataset predicting the tips using a road network of New York. Our results demonstrate that the input representations that use temporal information help the model to achieve the highest accuracy (RMSE of 1.42$).