Machine learning approach for spatial modeling of ridesourcing demand
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, the authors develop an interactive clustering approach (powered by human-in-the-loop AI) to split the origin-destination pairs into different clusters and ensemble the cluster-specific machine learning models for prediction. They 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.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/09666923
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Supplemental Notes:
- © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Zhang, Xiaojian
- Zhao, Xilei
- Publication Date: 2022-4
Language
- English
Media Info
- Media Type: Web
- Features: Appendices; Figures; References; Tables;
- Pagination: 103310
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Serial:
- Journal of Transport Geography
- Volume: 100
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0966-6923
- Serial URL: http://www.elsevier.com/locate/jtrangeo
Subject/Index Terms
- TRT Terms: Artificial intelligence; Demand; Machine learning; Predictive models; Ridesourcing; Spatial analysis
- Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;
Filing Info
- Accession Number: 01847146
- Record Type: Publication
- Files: TRIS
- Created Date: May 25 2022 9:40AM