Abstract | ||
---|---|---|
We argue that Genetic Improvement can be successfully used for enhancing road traffic data mining. This would support the relevant decision makers with extending the existing network of devices that sense and control city traffic, with the end goal of improving vehicle flow and reducing the frequency of road accidents. Our position results from a set of preliminary observations emerging from the analysis of open access road traffic data collected in real time by the Birmingham City Council. |
Year | DOI | Venue |
---|---|---|
2017 | 10.1145/3067695.3082523 | GECCO (Companion) |
Keywords | Field | DocType |
Genetic Improvement, symbolic regression, data mining | Computer science,Operations research,Road traffic,Symbolic regression | Conference |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Anikó Ekart | 1 | 564 | 62.28 |
Alina Patelli | 2 | 5 | 3.30 |
victoria lush | 3 | 3 | 2.10 |
Elisabeth Ilie-Zudor | 4 | 14 | 4.35 |