  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.
