Beyond the Limits of Predictability in Human Mobility Prediction: Context-Transition Predictability
Ieee Transactions on Knowledge and Data Engineering
; 35(5):4514-4526, 2023.
Artículo
en Inglés
| Web of Science | ID: covidwho-2328383
ABSTRACT
Urban human mobility prediction is forecasting how people move in cities. It is crucial for many smart city applications including route optimization, preparing for dramatic shifts in modes of transportation, or mitigating the epidemic spread of viruses such as COVID-19. Previous research propose the maximum predictability to derive the theoretical limits of accuracy that any predictive algorithm could achieve on predicting urban human mobility. However, existing maximum predictability only considers the sequential patterns of human movements and neglects the contextual information such as the time or the types of places that people visit, which plays an important role in predicting one's next location. In this paper, we propose new theoretical limits of predictability, namely Context-Transition Predictability, which not only captures the sequential patterns of human mobility, but also considers the contextual information of human behavior. We compare our Context-Transition Predictability with other kinds of predictability and find that it is larger than these existing ones. We also show that our proposed Context-Transition Predictability provides us a better guidance on which predictive algorithm to be used for forecasting the next location when considering the contextual information. Source code is at https//github.com/zcfinal/ContextTransitionPredictability.
Texto completo:
Disponible
Colección:
Bases de datos de organismos internacionales
Base de datos:
Web of Science
Tipo de estudio:
Estudio pronóstico
Idioma:
Inglés
Revista:
Ieee Transactions on Knowledge and Data Engineering
Año:
2023
Tipo del documento:
Artículo
Similares
MEDLINE
...
LILACS
LIS