Estimating latent demand of shared mobility through censored Gaussian Processes. (November 2020)

• Record Type: Journal Article
• Authors: Gammelli, Daniele; Peled, Inon; Rodrigues, Filipe; Pacino, Dario; Kurtaran, Haci A.; Pereira, Francisco C.
• Is Part Of: Transportation research. Volume 120(2020)
• Publication Date: 2020-11
• Subjects: Demand modeling -- Censoring -- Gaussian Processes -- Bayesian inference -- Shared mobility; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2020.102775
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Observability of mobility demand is inherently limited by supply. Censored regression applied to mobility demand to mitigate bias. Censored Gaussian Process formulated for time-varying censorship. Experiments with synthetic and real-world data demonstrate solution approach. Benefit of preserving the censored information is measured. Abstract: 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.