Modeling Censored Mobility Demand through Quantile Regression Neural Networks

TL;DR

This paper develops a neural network-based censored quantile regression model that estimates multiple demand quantiles simultaneously, reducing computational costs and quantile crossings for shared mobility demand prediction.

Contribution

It extends existing censored quantile regression models by enabling joint learning of multiple quantiles using neural networks, improving efficiency and accuracy.

Findings

Fewer quantile crossings achieved with the new model

Reduced computational overhead compared to individual quantile estimation

Maintained or improved demand prediction performance

Abstract

Shared mobility services require accurate demand models for effective service planning. On the one hand, modeling the full probability distribution of demand is advantageous because the entire uncertainty structure preserves valuable information for decision-making. On the other hand, demand is often observed through the usage of the service itself, so that the observations are censored, as they are inherently limited by available supply. Since the 1980s, various works on Censored Quantile Regression models have performed well under such conditions. Further, in the last two decades, several papers have proposed to implement these models flexibly through Neural Networks. However, the models in current works estimate the quantiles individually, thus incurring a computational overhead and ignoring valuable relationships between the quantiles. We address this gap by extending current Censored Quantile Regression models to learn multiple quantiles at once and apply these to synthetic baseline datasets and datasets from two shared mobility providers in the Copenhagen metropolitan area in Denmark. The results show that our extended models yield fewer quantile crossings and less computational overhead without compromising model performance.