Prediction of urban human mobility using large-scale taxi traces and its applications

355 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Predicting Taxi–Passenger Demand Using Streaming Data (2013, methodology): “8, to predict and/or characterize the pick-up quantity distribution on a short-term time horizon [8]–[10], [17], [19], [27], [28].”
Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction (2018, methodology): “In time series community, autoregressive integrated moving average (ARIMA), Kalman ﬁltering and their variants have been widely used in trafﬁc prediction problem (Shekhar and Williams 2008; Li et al. 2012; Moreira-Matias et al. 2013; Lippi, Bertini, and Frasconi 2013).”
Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions (2021, methodology): “Statistical methods Flow [11], [23], [26], [27] Demand [24], [25] Traditional machine learning methods Feature-based models Flow [30] Demand [28], [29] Gaussian process models Flow [31] Speed [33] Demand [32] Occupancy [32] State space models Flow [34], [35], [38]–[40], [45]–[48] Speed [36], [42],…”
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction (2018, methodology): “Representatively, autoregressive integrated moving average (ARIMA) and its variants have been widely applied for traffic prediction (Li et al. 2012; Moreira-Matias et al. 2013; Shekhar and Williams 2008).”