An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service

TL;DR

This study compares various machine learning models, including decision trees and neural networks, for short-term demand forecasting in ride-hailing services, using real-world data from China to identify the most accurate approach.

Contribution

It systematically evaluates multiple machine learning methods for spatio-temporal demand forecasting in ride-hailing, highlighting boosted decision trees as the most effective model.

Findings

Boosted decision trees achieved the lowest RMSE of 16.41.

Neural networks performed better than single decision trees.

All models were validated on real ride-hailing data from China.

Abstract

In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the methodology, a single decision tree, bootstrap-aggregated (bagged) decision trees, random forest, boosted decision trees, and artificial neural network for regression have been adapted and systematically compared using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and slope. To better assess the quality of the models, they have been tested on a real case study using the data of DiDi Chuxing, the main on-demand ride hailing service provider in China. In the current study, 199,584 time-slots describing the spatio-temporal ride-hailing demand has been extracted with an aggregated-time interval of 10 mins. All the methods are trained and validated on the basis of two independent samples from this dataset. The results revealed that boosted decision trees provide the best prediction accuracy (RMSE=16.41), while avoiding the risk of over-fitting, followed by artificial neural network (20.09), random forest (23.50), bagged decision trees (24.29) and single decision tree (33.55).