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

arXiv: https://arxiv.org/abs/2109.03433 · HTML: https://mlsanity.com/paper/2109.03433/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 79 references that are in the map.
- First posted on arXiv 8 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [Explainable k-Means and k-Medians Clustering](https://mlsanity.com/paper/2002.12538/) (2020)
- [Predicting the Computational Cost of Deep Learning Models](https://mlsanity.com/paper/1811.11880/) (2018)
- [Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey](https://mlsanity.com/paper/2005.10691/) (2020)
- [The Utility of Clustering in Prediction Tasks](https://mlsanity.com/paper/1509.06163/) (2015)
- [K-Prototype Segmentation Analysis on Large-scale Ridesourcing Trip Data](https://mlsanity.com/paper/2006.13924/) (2020)
- [An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service](https://mlsanity.com/paper/1703.02433/) (2017)

## Abstract (arXiv)

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.