A Clustering-aided Ensemble Method for Predicting Ridesourcing Demand in Chicago

TL;DR

This paper introduces a clustering-aided ensemble machine learning approach to improve ridesourcing demand prediction accuracy by accounting for spatial heterogeneity, demonstrated with Chicago data.

Contribution

The study proposes a novel clustering-aided ensemble method that enhances prediction accuracy by modeling spatial heterogeneity in ridesourcing demand forecasting.

Findings

CEM outperforms benchmark models in prediction accuracy.

Clustering improves model transparency and flexibility.

Method applicable to emerging travel modes like micromobility.

Abstract

Accurately forecasting ridesourcing demand is important for effective transportation planning and policy-making. With the rise of Artificial Intelligence (AI), researchers have started to utilize machine learning models to forecast travel demand, which, in many cases, can produce higher prediction accuracy than statistical models. However, most existing machine-learning studies used a global model to predict the demand and ignored the influence of spatial heterogeneity (i.e., the spatial variations in the impacts of explanatory variables). Spatial heterogeneity can drive the parameter estimations varying over space; failing to consider the spatial variations may limit the model's prediction performance. To account for spatial heterogeneity, this study proposes a Clustering-aided Ensemble Method (CEM) to forecast the zone-to-zone (census-tract-to-census-tract) travel demand for ridesourcing services. Specifically, we develop a clustering framework to split the origin-destination pairs into different clusters and ensemble the cluster-specific machine learning models for prediction. We implement and test the proposed methodology by using the ridesourcing-trip data in Chicago. The results show that, with a more transparent and flexible model structure, the CEM significantly improves the prediction accuracy than the benchmark models (i.e., global machine-learning and statistical models directly trained on all observations). This study offers transportation researchers and practitioners a new methodology of travel demand forecasting, especially for new travel modes like ridesourcing and micromobility.