Graph input representations for machine learning applications in urban network analysis
Bibliographic Data
| ID | 21246894 |
|---|---|
| Authors | Alessio Pagani (0000-0002-8404-8890, The Alan Turing Institute, corresponding author), Abhinav Mehrotra (0000-0003-3146-8426, University College London), Mirco Musolesi (0000-0001-9712-4090, The Alan Turing Institute) |
| Year | 2021 |
| Volume | 48 |
| Issue | 4 |
| Pages | 741-758 |
| Publication date | 2021-05-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning B Urban Analytics and City Science (JOURNAL) |
| Journal identifiers | ISSN: 2399-8083 • E-ISSN: 2399-8091 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/2399808319892599 |
| OpenAlex | W2995018737 |
| Language | EN |
| Citations received | 1 |
| References cited | 27 |
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$)
Graph · Machine learning · Network analysis · Computer Science · Data Management and Algorithms · Data Visualization and Analytics · Engineering · Human Mobility and Location-Based Analysis · Artificial Intelligence · Theoretical Computer Science
| Unique citing works | 1 |
|---|---|
| Citations per year | 0.5 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |