In an urban area, the demand for taxis is not always matched up with the supply. This paper proposes mining historical data to predict demand distributions with respect to contexts of time, weather, and taxi location. The four-step process consists of data filtering, clustering, semantic annotation, and hotness calculation. The results of three clustering algorithms are compared and demonstrated in a web mash-up application to show that context-aware demand prediction can help improve the management of taxi fleets.
Based on the problem of matching taxi demand in urban area, Chang et al. REF proposed a four-step approach to predict demand distributions with respect to contexts of time, weather, and location.
Context-aware taxi demand hotspots prediction
IJBIDM
IJBIDM
Computer Science