Graph input representations for machine learning applications in urban network analysis
| Autor: | Abhinav Mehrotra, Alessio Pagani, Mirco Musolesi |
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| Přispěvatelé: | Alessio Pagani, Abhinav Mehrotra, Mirco Musolesi |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Theoretical computer science Computer science Computer Science - Artificial Intelligence Geography Planning and Development Machine Learning (stat.ML) Urban network 02 engineering and technology Management Monitoring Policy and Law 01 natural sciences Machine Learning (cs.LG) Urban Studies 010104 statistics & probability Artificial Intelligence (cs.AI) Statistics - Machine Learning Architecture 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) 020201 artificial intelligence & image processing 0101 mathematics Urban networks graph learning path representation Nature and Landscape Conservation |
| Popis: | 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 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 network paths. The representations are validated and then tested with a real-world taxi journeys dataset predicting the tips of 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 (root mean-squared error of 1.42$). |
| Databáze: | OpenAIRE |
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