Journal article
Estimating latent demand of shared mobility through censored Gaussian Processes
Transport, Department of Technology, Management and Economics, Technical University of Denmark1
Machine Learning, Transport, Department of Technology, Management and Economics, Technical University of Denmark2
Department of Technology, Management and Economics, Technical University of Denmark3
Management Science, Department of Technology, Management and Economics, Technical University of Denmark4
Operations Research, Management Science, Department of Technology, Management and Economics, Technical University of Denmark5
Donkey Republic6
Transport demand is highly dependent on supply, especially for shared transport services where availability is often limited. As observed demand cannot be higher than available supply, historical transport data typically represents a biased, or censored, version of the true underlying demand pattern.
Without explicitly accounting for this inherent distinction, predictive models of demand would necessarily represent a biased version of true demand, thus less effectively predicting the needs of service users. To counter this problem, we propose a general method for censorship-aware demand modeling, for which we derive a censored likelihood function capable of handling time-varying supply.
We apply this method to the task of shared mobility demand prediction by incorporating the censored likelihood within a Gaussian Process model, which can flexibly approximate arbitrary functional forms. Experiments on artificial and real-world datasets show how taking into account the limiting effect of supply on demand is essential in the process of obtaining an unbiased predictive model of user demand behavior.
Language: | English |
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Year: | 2020 |
Pages: | 102775 |
ISSN: | 18792359 and 0968090x |
Types: | Journal article |
DOI: | 10.1016/j.trc.2020.102775 |
ORCIDs: | Peled, Inon , Rodrigues, Filipe , Pacino, Dario and Pereira, Francisco C. |