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 traffic congestion, and enhance the passenger experience. This paper proposes UBERNET, a deep learning convolutional neural network for short-time prediction of demand for ride-hailing services. Exploiting traditional time series approaches for this problem is challenging due to strong surges and declines in pickups, as well as spatial concentrations of demand. This leads to pickup patterns that are unevenly distributed over time and space. UBERNET employs 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. Specifically, 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. We use a number of features suggested by the transport operations and travel behaviour research areas as being relevant to passenger demand prediction, e.g., weather, temporal factors, socioeconomic and demographics characteristics, as well as travel-to-work, built environment and social factors such as crime level, within a multivariate framework, that leads to operational and policy insights for multiple communities: the ride-hailing operator, passengers, third-part location-based service providers and revenue opportunities to drivers, and transport operators such as road traffic authorities, and public transport agencies. 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 of travel behavior in making real-time demand predictions for ride-hailing services.
| Original language | English |
|---|---|
| Pages (from-to) | 175-195 |
| Number of pages | 21 |
| Journal | Data Science for Transportation |
| Volume | 3 |
| Early online date | 21 Apr 2021 |
| DOIs | |
| Publication status | Published - Aug 2021 |
| Externally published | Yes |
Bibliographical note
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Keywords
- Ride-hailing services
- Socio-economic variables
- Spatio-temporal features
- Deep learning
- Convolutional Neural Networks (CNN)
