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

TL;DR

This paper introduces UberNet, a deep learning convolutional neural network designed for short-term demand prediction in ride-hailing services, utilizing spatial, temporal, and socioeconomic features to improve accuracy.

Contribution

UberNet is a novel deep learning model that effectively integrates diverse features for accurate short-term ride demand prediction, outperforming existing approaches.

Findings

UberNet achieves highly competitive prediction accuracy.

Including economic, social, and built environment features improves performance.

Model demonstrates effectiveness using 9 months of NYC Uber data.

Abstract

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.