# STEF-DHNet: Spatiotemporal External Factors Based Deep Hybrid Network for Enhanced Long-Term Taxi Demand Prediction

arXiv: https://arxiv.org/abs/2306.14476 · HTML: https://mlsanity.com/paper/2306.14476/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 26 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction](https://mlsanity.com/paper/1803.01254/) (2018)
- [DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction](https://mlsanity.com/paper/2108.09091/) (2021)
- [A Clockwork RNN](https://mlsanity.com/paper/1402.3511/) (2014)
- [Understanding Traffic Density from Large-Scale Web Camera Data](https://mlsanity.com/paper/1703.05868/) (2017)
- [Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery](https://mlsanity.com/paper/1804.08562/) (2018)
- [Boosted Embeddings for Time Series Forecasting](https://mlsanity.com/paper/2104.04781/) (2021)
- [Predicting Demand for Air Taxi Urban Aviation Services using Machine Learning Algorithms](https://mlsanity.com/paper/2103.14604/) (2021)

## Abstract (arXiv)

Accurately predicting the demand for ride-hailing services can result in significant benefits such as more effective surge pricing strategies, improved driver positioning, and enhanced customer service. By understanding the demand fluctuations, companies can anticipate and respond to consumer requirements more efficiently, leading to increased efficiency and revenue. However, forecasting demand in a particular region can be challenging, as it is influenced by several external factors, such as time of day, weather conditions, and location. Thus, understanding and evaluating these factors is essential for predicting consumer behavior and adapting to their needs effectively. Grid-based deep learning approaches have proven effective in predicting regional taxi demand. However, these models have limitations in integrating external factors in their spatiotemporal complexity and maintaining high accuracy over extended time horizons without continuous retraining, which makes them less suitable for practical and commercial applications. To address these limitations, this paper introduces STEF-DHNet, a demand prediction model that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to integrate external features as spatiotemporal information and capture their influence on ride-hailing demand. The proposed model is evaluated using a long-term performance metric called the rolling error, which assesses its ability to maintain high accuracy over long periods without retraining. The results show that STEF-DHNet outperforms existing state-of-the-art methods on three diverse datasets, demonstrating its potential for practical use in real-world scenarios.