Preserving Uncertainty in Demand Prediction for Autonomous Mobility Services

Peled, Inon; Lee, Kelvin; Jiang, Yu; Dauwels, Justin; Pereira, Francisco C.

doi:10.1109/itsc.2019.8916878

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Importantly, the fully estimated distribution preserves useful information, which might otherwise be lost through the more common practice of estimating only a few central moments, such as mean and standard deviation [21]. In turn, the preserved information allows for better informed decisions, e.g., service operators can use the full uncertainty structure of future demand to decide whether to balance the fleet conservatively or more opportunistically.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Censored Mobility Demand Through Censored Quantile Regression Neural Networks

Hüttel

Peled

Rodrigues

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Modeling Censored Mobility Demand Through Censored Quantile Regression Neural Networks

Hüttel

Peled

Rodrigues

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Section: Literature Reviewmentioning

confidence: 99%

“…Eventually though, the optimization results did not demonstrate sufficient benefits for using the entire framework. We thus decided that I present only the demand modeling portion of the work in the 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (Peled et al, 2019a).…”

Section: Predictive Optimization Framework For Demand-responsive Publ...mentioning

confidence: 99%

Machine learning methods for transportation under uncertainty

Peled¹

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The goal of the thesis is to develop and study effective modeling methods for Transportation under uncertainty scenarios. This is motivated by both the prevalence of uncertainty in Transportation and the widespread use of Transportation models in practice, e.g., for traffic management, planning of mobility services and operation of Public Transport. We approach this goal through Machine Learning, namely, our proposed methods extract patterns from data and leverage them for better modeling.general approach, which does not involve specific prediction models, by sampling prediction errors from various distributions. We apply this approach to a case study of demand-responsive Public Transport in the Copenhagen metropolitan area, through which we quantify the relationship between prediction errors and subsequent performance of dynamic routing.In conclusion, this thesis offers several useful findings for Transportation practice and theory. We find that recent technological advances can alleviate the degradation of data-driven prediction models under road incidents, for which we offer a dedicated framework. We also advise to explicitly model the inherent censorship in Transportation demand, for which we offer two non-parametric alternatives. For dynamic operation of shared mobility services, we demonstrate the benefits of preserving a full uncertainty structure of demand, and we also quantify the relationship between predictive quality and subsequent service performance.To my dear father, mother, brother and sister.

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