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Short-term demand forecasting for online car-hailing using ConvLSTM networks

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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inferredcites →https://doi.org/10.1016/s0968-090x(02)00009-8openalex-snapshot
Comparison of parametric and nonparametric models for traffic flow forecasting
inferredcites →https://doi.org/10.1016/s0968-090x(97)82903-8openalex-snapshot
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Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results
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Dynamic Wavelet Neural Network Model for Traffic Flow Forecasting
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Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models
inferredcites →https://doi.org/10.1080/23249935.2020.1711543openalex-snapshot
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inferredcites →https://doi.org/10.1109/icme44524.2019openalex-snapshot
2019 IEEE International Conference on Multimedia and Expo (ICME)
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STCNN: A Spatio-Temporal Convolutional Neural Network for Long-Term Traffic Prediction
inferredcites →https://doi.org/10.1109/mdm42716.2018openalex-snapshot
2018 19th IEEE International Conference on Mobile Data Management (MDM)
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Predicting Bus Passenger Flow and Prioritizing Influential Factors Using Multi-Source Data: Scaled Stacking Gradient Boosting Decision Trees
inferredcites →https://doi.org/10.1142/s0217984904007633openalex-snapshot
A MOVING RUBIDIUM ATOM INTERACTING WITH A SINGLE MODE CAVITY FIELD IN THE PRESENCE OF KERR MEDIUM
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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