Name
Abstract | ||
|---|---|---|
Collecting huge volumes of trajectories opens up new opportunities to capture time-varying and uncertain travel costs to traverse segments on a network. This kind of analyses happens to be conducted offline, by means of data mining analysis on historical data. However, there is a need to deal with the incremental nature of spatio-temporal data and maintain the travel time estimation functions by regarding the dynamic behavior of the traffic. In this work, we tackle the problem of creating and maintaining travel time estimation functions by means of trajectory data streams. We propose a new scheme for computing temporal functions using a regression tree with a transition function -- which yield updates in the binary tree by keeping the differentiability of the temporal functions. In the experimental evaluation, which was conducted on real-world dataset, we show the validity of our approach in terms of quality of results and performance.
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Year | Venue | DocType |
|---|---|---|
2017 | IWGS@SIGSPATIAL | Conference |
ISBN | Citations | PageRank |
978-1-4503-5492-9 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samara Martins do Nascimento | 1 | 0 | 0.68 |
| José Antônio Fernandes de Macêdo | 2 | 465 | 51.40 |
| Hélio Côrtes Vieira Lopes | 3 | 3 | 0.74 |
| ticiana | 4 | 32 | 14.96 |
| Marco Antonio Casanova | 5 | 178 | 24.18 |
| Javam Machado | 6 | 47 | 20.39 |