# DeepSTCL: A Deep Spatio-temporal ConvLSTM for Travel Demand Prediction

arXiv: https://arxiv.org/abs/2009.00096 · HTML: https://mlsanity.com/paper/2009.00096/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2020.

## Built on by (most cited first, 10 of 10)

- [On the Limitations of Physics-informed Deep Learning: Illustrations Using First Order Hyperbolic Conservation Law-based Traffic Flow Models](https://mlsanity.com/paper/2302.12337/) (2023)
- [Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation](https://mlsanity.com/paper/2112.14699/) (2021)
- [NeuralPDE: Modelling Dynamical Systems from Data](https://mlsanity.com/paper/2111.07671/) (2021)
- [Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach](https://mlsanity.com/paper/2303.00524/) (2023)
- [MM-Tracker: Motion Mamba with Margin Loss for UAV-platform Multiple Object Tracking](https://mlsanity.com/paper/2407.10485/) (2024)
- [Constraint-Aware Route Recommendation from Natural Language via Hierarchical LLM Agents](https://mlsanity.com/paper/2510.06078/) (2025)
- [Exploring the impact of spatiotemporal granularity on the demand prediction of dynamic ride-hailing](https://mlsanity.com/paper/2203.10301/) (2022)
- [Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks](https://mlsanity.com/paper/2502.01680/) (2025)
- [A Novel Prediction Approach for Exploring PM2.5 Spatiotemporal Propagation Based on Convolutional Recursive Neural Networks](https://mlsanity.com/paper/2101.06213/) (2021)
- [Ambulance Demand Prediction via Convolutional Neural Networks](https://mlsanity.com/paper/2306.04994/) (2023)

## Abstract (arXiv)

Urban resource scheduling is an important part of the development of a smart city, and transportation resources are the main components of urban resources. Currently, a series of problems with transportation resources such as unbalanced distribution and road congestion disrupt the scheduling discipline. Therefore, it is significant to predict travel demand for urban resource dispatching. Previously, the traditional time series models were used to forecast travel demand, such as AR, ARIMA and so on. However, the prediction efficiency of these methods is poor and the training time is too long. In order to improve the performance, deep learning is used to assist prediction. But most of the deep learning methods only utilize temporal dependence or spatial dependence of data in the forecasting process. To address these limitations, a novel deep learning traffic demand forecasting framework which based on Deep Spatio-Temporal ConvLSTM is proposed in this paper. In order to evaluate the performance of the framework, an end-to-end deep learning system is designed and a real dataset is used. Furthermore, the proposed method can capture temporal dependence and spatial dependence simultaneously. The closeness, period and trend components of spatio-temporal data are used in three predicted branches. These branches have the same network structures, but do not share weights. Then a linear fusion method is used to get the final result. Finally, the experimental results on DIDI order dataset of Chengdu demonstrate that our method outperforms traditional models with accuracy and speed.