We design a survey-and-scenario-based method for investigating the demand of real people for ride-pooling services in Berlin. We deploy it among public transport users to study where, why, how, and when they would use ride-pooling. In particular, we learn about their socio-demographic features, trip purposes, and deciding factors, as well as their preferences for service waiting times and fares. Our results inform service operators about dispatching decisions, allow for encoding user behavior in utility functions for reasoning, and enable the design of fair pricing schemes to improve service accessibility.

We propose a solution for intelligent household garbage collection in smart cities. Garbage containers are assumed to be digitalized with Internet-of-Things sensors that are capable of sensing the fill levels of containers and transmitting this data through LoRaWAN networks to a central server. Data is used for dynamic fleet management and household feedback. We give a number of algorithms for these tasks. Fleet management requires scheduling containers for collections and assigning containers to trucks, as well as routing the trucks. Drivers receive such navigations via pervasive computing devices such as tablets, phones, or watches. Household feedback consists of information about the levels of generated garbage and the associated costs. Households receive this information on their home devices. Thus, unlike present solutions, our solution involves households in the intelligent collection of their garbage.

We propose a new model for fair division and vehicle routing, where drivers have monotone profit preferences, and their vehicles have feasibility constraints, for customer requests. For this model, we design two new axiomatic notions for fairness for drivers: FEQ1 and FEF1. FEQ1 encodes driver pairwise bounded equitability. FEF1 encodes driver pairwise bounded envy freeness. We compare FEQ1 and FEF1 with popular fair division notions such as EQ1 and EF1. We also give algorithms for guaranteeing FEQ1 and FEF1, respectively.

In 2016, the German Ministry for Traffic and Digital Infrastructure has granted 100 million euros on autonomous and connected vehicles. Soon after, in 2017, the German Ministry appointed Ethics Commission to regulate the use of such vehicles for social good. They identified transparency, trust, and safety as vital features for this purpose. Transparency requires that information about vehicles is available to customers, e.g. locations, fees, etc. Trust requires that vehicles are used in a fair and efficient manner. Safety requires that vehicles follow all road regulations and customers have top priority in cases of potential accidents. In this paper, we give scientific guarantees that respond to these requirements by providing preliminary fairness and efficiency analyses of the fleet of vehicles.

We survey a burgeoning and promising new research area that considers the online nature of many practical fair division problems. We identify wide variety of such online fair division problems, as well as discuss new mechanisms and normative properties that apply to this online setting. The online nature of such fair division problems provides both opportunities and challenges such as the possibility to develop new online mechanisms as well as the difficulty of dealing with an uncertain future.

We consider a fair division setting in which items arrive one by one and are allocated to agents via two existing mechanisms: LIKE and BALANCED LIKE. The LIKE mechanism is strategy-proof whereas the BALANCED LIKE mechanism is not. Whilst LIKE is strategy-proof, we show that it is not group strategy-proof. Indeed, our first main result is that no online mechanism is group strategy-proof. We then focus on pure Nash equilibria of these two mechanisms. Our second main result is that computing a pure Nash equilibrium is tractable for LIKE and intractable for BALANCED LIKE. Our third main result is that there could be multiple such profiles and counting them is also intractable even when we restrict our attention to equilibria with a specific property (e.g. envy-freeness, Pareto efficiency).