Machine learning methods for transportation under uncertainty

Abstract

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.