# Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach

arXiv: https://arxiv.org/abs/1706.06279 · HTML: https://mlsanity.com/paper/1706.06279/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2017.

## Built on by (most cited first, 30 of 34)

- [Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks](https://mlsanity.com/paper/2208.05908/) (2022)
- [Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks](https://mlsanity.com/paper/2303.04040/) (2023)
- [Joint Demand Prediction for Multimodal Systems: A Multi-task Multi-relational Spatiotemporal Graph Neural Network Approach](https://mlsanity.com/paper/2112.08078/) (2021)
- [Fairness-enhancing deep learning for ride-hailing demand prediction](https://mlsanity.com/paper/2303.05698/) (2023)
- [A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management](https://mlsanity.com/paper/2306.02806/) (2023)
- [Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow](https://mlsanity.com/paper/2107.13226/) (2021)
- [Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach](https://mlsanity.com/paper/2303.00524/) (2023)
- [CCDSReFormer: Traffic Flow Prediction with a Criss-Crossed Dual-Stream Enhanced Rectified Transformer Model](https://mlsanity.com/paper/2403.17753/) (2024)
- [Short term prediction of demand for ride hailing services: A deep learning approach](https://mlsanity.com/paper/2212.03956/) (2022)
- [Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference](https://mlsanity.com/paper/2102.03980/) (2021)
- [XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting with High Volatility](https://mlsanity.com/paper/2310.09847/) (2023)
- [Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System](https://mlsanity.com/paper/2401.00093/) (2023)
- [MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model](https://mlsanity.com/paper/2405.15598/) (2024)
- [Fairness in Federated Learning for Spatial-Temporal Applications](https://mlsanity.com/paper/2201.06598/) (2022)
- [Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach](https://mlsanity.com/paper/2307.06742/) (2023)
- [ST-Mamba: Spatial-Temporal Selective State Space Model for Traffic Flow Prediction](https://mlsanity.com/paper/2404.13257/) (2024)
- [Exploring Context Generalizability in Citywide Crowd Mobility Prediction: An Analytic Framework and Benchmark](https://mlsanity.com/paper/2106.16046/) (2021)
- [PSTN: Periodic Spatial-temporal Deep Neural Network for Traffic Condition Prediction](https://mlsanity.com/paper/2108.02424/) (2021)
- [Exploring the impact of spatiotemporal granularity on the demand prediction of dynamic ride-hailing](https://mlsanity.com/paper/2203.10301/) (2022)
- [STGIN: A Spatial Temporal Graph-Informer Network for Long Sequence Traffic Speed Forecasting](https://mlsanity.com/paper/2210.01799/) (2022)
- [Multi-task Learning for Sparse Traffic Forecasting](https://mlsanity.com/paper/2211.09984/) (2022)
- [Online Relocating and Matching of Ride-Hailing Services: A Model-Based Modular Approach](https://mlsanity.com/paper/2310.09071/) (2023)
- [Predicting the Skies: A Novel Model for Flight-Level Passenger Traffic Forecasting](https://mlsanity.com/paper/2401.03397/) (2024)
- [DRL4AOI: A DRL Framework for Semantic-aware AOI Segmentation in Location-Based Services](https://mlsanity.com/paper/2412.05437/) (2024)
- [STContext: A Multifaceted Dataset for Developing Context-aware Spatio-temporal Crowd Mobility Prediction Models](https://mlsanity.com/paper/2501.03583/) (2025)
- [ProSTformer: Pre-trained Progressive Space-Time Self-attention Model for Traffic Flow Forecasting](https://mlsanity.com/paper/2111.03459/) (2021)
- [STG-GAN: A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems](https://mlsanity.com/paper/2202.06727/) (2022)
- [Attention-based Contextual Multi-View Graph Convolutional Networks for Short-term Population Prediction](https://mlsanity.com/paper/2203.00489/) (2022)
- [A Spatial-Temporal Attention Multi-Graph Convolution Network for Ride-Hailing Demand Prediction Based on Periodicity with Offset](https://mlsanity.com/paper/2203.12505/) (2022)
- [Dynamic Adjustment of Matching Radii under the Broadcasting Mode: A Novel Multitask Learning Strategy and Temporal Modeling Approach](https://mlsanity.com/paper/2312.05576/) (2023)

## Abstract (arXiv)

Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependences, temporal dependences, and exogenous dependences need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependences within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. Experimental results, validated on real-world data provided by DiDi Chuxing, show that the FCL-Net achieves better predictive performance than traditional approaches including both classical time-series prediction models and neural network based algorithms (e.g., artificial neural network and LSTM). This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.