# Short term prediction of demand for ride hailing services: A deep learning approach

arXiv: https://arxiv.org/abs/2212.03956 · HTML: https://mlsanity.com/paper/2212.03956/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 72 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 19 of 19)

- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Conditional Image Generation with PixelCNN Decoders](https://mlsanity.com/paper/1606.05328/) (2016)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Learning Convolutional Neural Networks for Graphs](https://mlsanity.com/paper/1605.05273/) (2016)
- [Professor Forcing: A New Algorithm for Training Recurrent Networks](https://mlsanity.com/paper/1610.09038/) (2016)
- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [Learning to Diagnose with LSTM Recurrent Neural Networks](https://mlsanity.com/paper/1511.03677/) (2015)
- [Generative Image Modeling Using Spatial LSTMs](https://mlsanity.com/paper/1506.03478/) (2015)
- [Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach](https://mlsanity.com/paper/1712.04997/) (2017)
- [Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach](https://mlsanity.com/paper/1706.06279/) (2017)
- [Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/1709.07032/) (2017)
- [Fast and Flexible ADMM Algorithms for Trend Filtering](https://mlsanity.com/paper/1406.2082/) (2014)
- [Multivariate Time Series Classification using Dilated Convolutional Neural Network](https://mlsanity.com/paper/1905.01697/) (2019)
- [Incremental Skip-gram Model with Negative Sampling](https://mlsanity.com/paper/1704.03956/) (2017)
- [Sales forecasting using WaveNet within the framework of the Kaggle competition](https://mlsanity.com/paper/1803.04037/) (2018)
- [BB-Graph: A Subgraph Isomorphism Algorithm for Efficiently Querying Big Graph Databases](https://mlsanity.com/paper/1706.06654/) (2017)
- [A deep learning approach to real-time parking occupancy prediction in spatio-temporal networks incorporating multiple spatio-temporal data sources](https://mlsanity.com/paper/1901.06758/) (2019)
- [Regulating TNCs: Should Uber and Lyft Set Their Own Rules?](https://mlsanity.com/paper/1902.01076/) (2019)

## Built on by (most cited first, 4 of 4)

- [Fairness-enhancing deep learning for ride-hailing demand prediction](https://mlsanity.com/paper/2303.05698/) (2023)
- [Surge Routing: Event-informed Multiagent Reinforcement Learning for Autonomous Rideshare](https://mlsanity.com/paper/2307.02637/) (2023)
- [EXHOLD: Experience-Aware Real-Time Hold Control for Large-Scale Ride-Hailing Matching at DiDi](https://mlsanity.com/paper/2607.09090/) (2026)
- [The Cost of Learning Under Multiple Change Points](https://mlsanity.com/paper/2602.11406/) (2026)

## Abstract (arXiv)

As ride-hailing services become increasingly popular, being able to accurately predict demand for such services can help operators efficiently allocate drivers to customers, and reduce idle time, improve congestion, and enhance the passenger experience. This paper proposes UberNet, a deep learning Convolutional Neural Network for short-term prediction of demand for ride-hailing services. UberNet empploys a multivariate framework that utilises a number of temporal and spatial features that have been found in the literature to explain demand for ride-hailing services. The proposed model includes two sub-networks that aim to encode the source series of various features and decode the predicting series, respectively. To assess the performance and effectiveness of UberNet, we use 9 months of Uber pickup data in 2014 and 28 spatial and temporal features from New York City. By comparing the performance of UberNet with several other approaches, we show that the prediction quality of the model is highly competitive. Further, Ubernet's prediction performance is better when using economic, social and built environment features. This suggests that Ubernet is more naturally suited to including complex motivators in making real-time passenger demand predictions for ride-hailing services.