Predicting Taxi–Passenger Demand Using Streaming Data

By Luís Moreira-Matias, João Gama, Michel Ferreira, João Mendes-Moreira, Luís Damas. IEEE transactions on intelligent transportation systems (Print), 2013

704 citations recorded by the source corpus.

Subfields: Building and Construction, Transportation

View Paper: https://www.semanticscholar.org/paper/ee087bb8348f480ad1c18829aa2bdf6f42520931

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Citations in the corpus

Large-Scale Order Dispatch in On-Demand Ride-Hailing Platforms: A Learning and Planning Approach (2018, background): “Recently, several works on taxi order dispatch integrated various demand prediction [14, 20] components to facilitate dispatch decision making with foresighted considerations.”
Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends (2020, background): “This popularity is partially owed to an abundance of available data [74], [4], as well as having spotlighted in a large-scale data science competition hosted by Kaggle [1].”
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).”
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],…”
STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting (2019, methodology): “Previous work have proposed to use time series models [ Moreira-Matias et al. , 2013 ] , cluster models [ Li et al. , 2015 ] , or hybrid methods [ Zhang et al. , 2016b ] to capture the correlations.”
A Survey on Deep Learning for Human Mobility (2020, methodology): “The model performance is evaluated on the dataset of taxis in Porto [127] in terms of equirectangular distance to the actual visited location.”
A Taxi Order Dispatch Model based On Combinatorial Optimization (2017, background): “e collected data provide us an unprecedented opportunity to automatically discover knowledge on user behavior, which can be used to build real time intelligent decision making systems in dierent applications, such as passenger nding [7, 12, 13, 24], taxi demand predicting [16, 17], route planning”
Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach (2017, background): “However, heterogeneous and exogenous factors in reality, e.g., asymmetric information, short-term ﬂuctuations, may make it diﬃcult to guarantee the spatial distribution of taxis matching the passenger demand all the time (Moreira-Matias et al., 2013).”
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).”