Travel Time Forecasting Based on Fuzzy Patterns
Artykuł w czasopiśmie
MNiSW
100
Lista 2021
| Status: | |
| Autorzy: | Kiersztyn Adam, Gandzel Agnieszka, Koczan Leopold, Celiński Maciej |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2021 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 3 |
| Wolumen/Tom: | 15 |
| Strony: | 224 - 232 |
| Web of Science® Times Cited: | 2 |
| Scopus® Cytowania: | 3 |
| Bazy: | Web of Science | Scopus | BazTech |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Otwarte czasopismo |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 1 września 2021 |
| Abstrakty: | angielski |
| Estimating travel time is one of the most important processes in logistics as well as in everyday life. In particular, when it comes to transportation services, efficient time management can be a competitive advantage, not to mention customer satisfaction, which can be easily translated into business success. Therefore, in this study we analyze various travel time estimation methods in combination with a well-known Fuzzy C-Means clustering algorithm. The proposed FCM-based solution has significant advantages, allowing for the determination of the optimal travel time. In an extensive numerical experiment, we present the application of the proposed method to estimate the time of a taxi trip around New York. Due to division of the city area into detailed areas and taking into account information about the travel time in the analysis, a model was obtained, that perfectly forecasts speed of taxi travel. In this study we consider various, competitive approaches to build such a model. |
