  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.
