# Modeling Censored Mobility Demand through Quantile Regression Neural Networks

arXiv: https://arxiv.org/abs/2104.01214 · HTML: https://mlsanity.com/paper/2104.01214/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 63 references that are in the map.
- First posted on arXiv 2 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problems](https://mlsanity.com/paper/1808.08798/) (2018)
- [Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes](https://mlsanity.com/paper/2001.07402/) (2020)
- [Censored Quantile Regression Forest](https://mlsanity.com/paper/2001.03458/) (2020)
- [Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services](https://mlsanity.com/paper/1902.09745/) (2019)
- [Deep Learning for Quantile Regression under Right Censoring: DeepQuantreg](https://mlsanity.com/paper/2007.07056/) (2020)

## Built on by (most cited first, 5 of 5)

- [Deep Evidential Learning for Bayesian Quantile Regression](https://mlsanity.com/paper/2308.10650/) (2023)
- [Mind the Gap: Modelling Difference Between Censored and Uncensored Electric Vehicle Charging Demand](https://mlsanity.com/paper/2301.06418/) (2023)
- [Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis](https://mlsanity.com/paper/2205.13496/) (2022)
- [Bayesian Active Learning for Censored Regression](https://mlsanity.com/paper/2402.11973/) (2024)
- [Diffusion-aware Censored Gaussian Processes for Demand Modelling](https://mlsanity.com/paper/2501.12354/) (2025)

## Abstract (arXiv)

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.