TY - JOUR
T1 - Estimating latent demand of shared mobility through censored Gaussian Processes
AU - Gammelli, Daniele
AU - Peled, Inon
AU - Rodrigues, Filipe
AU - Pacino, Dario
AU - Kurtaran, Haci A.
AU - Pereira, Francisco C.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Bayesian inference
KW - Censoring
KW - Demand modeling
KW - Gaussian Processes
KW - Shared mobility
U2 - 10.1016/j.trc.2020.102775
DO - 10.1016/j.trc.2020.102775
M3 - Journal article
AN - SCOPUS:85091198868
SN - 0968-090X
VL - 120
JO - Transportation Research Part C: Emerging Technologies
JF - Transportation Research Part C: Emerging Technologies
M1 - 102775
ER -