Smart Sustainable Mobility: Analytics and Algorithms for Next-Generation Mobility Systems

To this date, mobility ecosystems around the world operate on an uncoordinated, inefficient and unsustainable basis. Yet, many technology-enabled solutions that have the potential to remedy these societal negatives are already at our disposal or just around the corner. Innovations in vehicle technology, IoT devices, mobile connectivity and AI-powered information systems are expected to bring about a mobility system that is connected, autonomous, shared and electric (CASE). In order to fully leverage the sustainability opportunities afforded by CASE, system-level coordination and management approaches are needed. This Thesis sets out an agenda for Information Systems research to shape the future of CASE mobility through data, analytics and algorithms (Chapter 1). Drawing on causal inference, (spatial) machine learning, mathematical programming and reinforcement learning, three concrete contributions toward this agenda are developed. Chapter 2 demonstrates the potential of pervasive and inexpensive sensor technology for policy analysis. Connected sensing devices have significantly reduced the cost and complexity of acquiring high-resolution, high-frequency data in the physical world. This affords researchers the opportunity to track temporal and spatial patterns of offline phenomena. Drawing on a case from the bikesharing sector, we demonstrate how geo-tagged IoT data streams can be used for tracing out highly localized causal effects of large-scale mobility policy interventions while offering actionable insights for policy makers and practitioners. Chapter 3 sets out a solution approach to a novel decision problem faced by operators of shared mobility fleets: allocating vehicle inventory optimally across a network when competition is present. The proposed three-stage model combines real-time data analytics, machine learning and mixed integer non-linear programming into an integrated framework. It provides operational decision support for fleet managers in contested shared mobility markets by generating optimal vehicle re-positioning schedules in real time. Chapter 4 proposes a method for leveraging data-driven digital twin (DT) frameworks for large multi-stage stochastic design problems. Such problem classes are notoriously difficult to solve with traditional stochastic optimization. Drawing on the case of Electric Vehicle Charging Hubs (EVCHs), we show how high-fidelity, data-driven DT simulation environments fused with reinforcement learning (DT-RL) can achieve (close-to) arbitrary scalability and high modeling flexibility. In benchmark experiments we demonstrate that DT-RL-derived designs result in superior cost and service-level performance under real-world operating conditions

To Differentiate Or Not To Differentiate? The Role of Product Characteristics in the Sharing Economy

An important ambiguity in the sharing economy literature concerns the role of product variety. Do physical product characteristics provide scope for differentiation in a sharing economy context, and if so, under which circumstances? Resolving this ambiguity is important as it can have large operational and strategic implications for sharing economy businesses. We use discrete choice modeling on a unique carsharing dataset and behavioral online experiments to study how users select between product options of varying quality and brand in a shared consumption context. We find that, in general, there is a trend towards utilitarian access-based consumption in which product characteristics and product brand matter less. However, we observe that hedonistic use cases tend to shift preferences significantly toward more premium products. Our results highlight the need for a more nuanced consideration of product differentiation in sharing economy research

An Online Learning and Optimization Approach for Competitor-Aware Management of Shared Mobility Systems

An important trend in mobility is the consumption of mobility as-a-service heralding in the age of freefloating vehicle sharing (FFVS) systems. In many markets such fleets compete. We investigate how realtime competitor information can create value for operators in this context. We focus on the vehicle supply decision which is a large operational concern. We show empirically that local market shares directly depend on the share of available vehicles in a location, which underlines the value potential of competitor awareness. We leverage this insight by proposing a novel decision support system for optimal management of FFVS systems under competition. We proceed in two phases, (1) a predictive phase and (2) a prescriptive phase. In phase (1), we compile a spatio-temporal dataset based on Car2Go and DriveNow transactions in Berlin, which we supplement with temporal, geographical and weather data. We partition the city into hexagonal tiles and observe vehicle supply per tile at the start of each period. We train machine learning models to predict vehicle inflows and vehicle outflows during the next period to derive total supply and demand. We find that inflows and outflows can be predicted with high accuracy using similar models. We test different temporal and spatial resolutions and find that spatial resolution incurs larger performance penalties. In phase (2), we formulate a myopic mixed integer non-linear programming model with a margin-maximizing objective function. The model trades off additional market share gains against the cost of re-locating vehicles, which enables operators to assign vehicles optimally across the service network. Our numerical studies on the case of Car2Go and DriveNow demonstrate that this competitor-aware model is capable of profitably improving market share by up to 1.4% or 3.4% for human-based and autonomous relocation respectively in a prefect foresight scenario and by up to 0.8% and 1.8% respectively when using predicted values

To Substitute or to Supplement? - Investigating the Heterogeneous Effects of Electric Scooter Platform Introduction on Micromobility

As a healthy and pollution-free form of micromobility, bikesharing is an important sharing economy intervention. We study whether the market entry of electric scooter platforms, a less effortful and convenience-focused genre of micromobility, caters to users which are currently untapped by incumbent bike-based micromobility services, or whether it has detrimental effects by significantly substituting from an otherwise preferred service. Our identification strategy exploits the natural experiment of staggered scooter introduction in Europe. Using trip-level data of a leading bikesharing operator we observe a drop in bicycle fleet utilization of 17.1% that is moderated by service level, population characteristics and weather-related factors. Substitution stems mostly from leisure-related usage, while utilitarian trips during rush hours are not affected. We contribute to the understanding of possible unintended consequences associated with the sharing economy and shed light on consumer choice in micromobility. We also offer practical insights for platform operators and policy makers

Information Systems Research for Smart Sustainable Mobility:A Framework and Call for Action

Transportation is a backbone of modern globalized societies. It also causes approximately one third of all European Union and U.S. greenhouse gas emissions, represents a major health hazard for global populations, and poses significant economic costs (e.g., due to traffic congestion). However, rapid innovation in vehicle technology, mobile connectivity, computing hardware, and artificial intelligence-powered information systems heralds a deep socio-technical transformation of the sector. The emergence of connected, autonomous, shared, and electric vehicle technology has created a digital layer that resides on top of the traditional physical mobility system. The resulting layered modular architecture is similar to that seen in other cyber-physical systems. Yet, it also comes with several characteristics and challenges that are unique to the domain of mobility and require entirely new solution approaches. Although other management and domain-specific research disciplines have started to embrace the new opportunities for research resulting from this deep structural change, the information systems (IS) community's involvement in smart mobility research has been marginal. Yet, we argue that our field's uniquely multidisciplinary, data-driven, and socio-technical research lens puts it in a strong position to address many of the large-scale societal challenges encountered in the mobility sector. Therefore, we make the case for IS research to play an active role in delivering a smart sustainable mobility ecosystem that is beneficial to users, mobility providers and the environment. We contribute a research framework to direct IS research efforts while providing a shared understanding of the smart sustainable mobility domain. We also present seven IS research opportunities along the dimensions of this framework and propose concrete angles of attack which we hope will spur an impactful and structured research agenda in the area.</p

Information Systems Research for Smart Sustainable Mobility: A Framework and Call for Action

Transportation is a backbone of modern globalized societies. It also causes approximately one third of all European Union and U.S. greenhouse gas emissions, represents a major health hazard for global populations, and poses significant economic costs (e.g., due to traffic congestion). However, rapid innovation in vehicle technology, mobile connectivity, computing hardware, and artificial intelligence-powered information systems heralds a deep socio-technical transformation of the sector. The emergence of connected, autonomous, shared, and electric vehicle technology has created a digital layer that resides on top of the traditional physical mobility system. The resulting layered modular architecture is similar to that seen in other cyber-physical systems. Yet, it also comes with several characteristics and challenges that are unique to the domain of mobility and require entirely new solution approaches. Although other management and domain-specific research disciplines have started to embrace the new opportunities for research resulting from this deep structural change, the information systems (IS) community's involvement in smart mobility research has been marginal. Yet, we argue that our field's uniquely multidisciplinary, data-driven, and socio-technical research lens puts it in a strong position to address many of the large-scale societal challenges encountered in the mobility sector. Therefore, we make the case for IS research to play an active role in delivering a smart sustainable mobility ecosystem that is beneficial to users, mobility providers and the environment. We contribute a research framework to direct IS research efforts while providing a shared understanding of the smart sustainable mobility domain. We also present seven IS research opportunities along the dimensions of this framework and propose concrete angles of attack which we hope will spur an impactful and structured research agenda in the area

Data-Driven Competitor-Aware Positioning in On-Demand Vehicle Rental Networks

We study a novel operational problem that considers vehicle positioning in on-demand rental networks, such as car sharing in the wider context of a competitive market in which users select vehicles based on access. Existing approaches consider networks in isolation; our competitor-aware model takes supply situations of competing networks into account. We combine online machine learning to predict market-level demand and supply with dynamic mixed integer nonlinear programming. For evaluation, we use discrete event simulation based on real-world data from Car2Go and DriveNow. Our model outperforms conventional models that consider the fleet in isolation by a factor of two in terms of profit improvements. In the case we study, the highest theoretical profit improvements of 7.5% are achieved with a dynamic model. Operators of on-demand rental networks can use our model under existing market conditions to build a profitable competitive advantage by optimizing access for consumers without the need for fleet expansion. Model effectiveness increases further in realistic scenarios of fleet expansion and demand growth. Our model accommodates rising demand, defends against competitors' fleet expansion, and enhances the profitability of own fleet expansions

Liquid Metals as Efficient High-Temperature Heat-Transport Fluids

Liquid metals appear to be attractive heat-transport fluids, in particular if looking at their high thermal conductivities and low viscosities. Despite some pioneering technical applications in the past, complex handling, special requirements, safety concerns, and structural degradation of the materials have prevented their widespread application. However, progress in research and development on liquid-metal science and technology has advanced considerably in the last decade, and this has opened the gate to their broader use in the short term. This requires a more differentiated view on liquid metals, particularly on the specific properties of individual fluids within the context of specific applications. By doing so, many commonly mentioned prejudices vanish or are of minor significance. At the Karlsruhe Institute of Technology, a comprehensive research program on liquid-metal technology has been pursued for more than 50years, and some of the advances in different applications will be outlined in this article

Pre-Surgery Patient Health Contributes to Aggravated Sino-Nasal Outcome and Quality of Life after Pituitary Adenomectomy

Objectives: The transphenoidal bi-nostril endoscopic resection of pituitary adenomas is regarded as a minimally invasive treatment nowadays. However, sino-nasal outcome and health-related quality of life (HRQoL) might still be impaired after the adenomectomy, depending on patients’ prior medical history and health status. A systematic postoperative comparison is required to assess differences in perceived sino-nasal outcome and HRQoL. Methods: In this single-center observational study, we collected data from 81 patients, operated between August 2016 and August 2021, at a 3–6-month follow-up after adenomectomy. We employed the sino-nasal outcome test for neurosurgery (SNOT-NC) and the HRQoL inventory Short Form (SF)-36 to compare sino-nasal and HRQoL outcome in patients with or without allergies, previous nose surgeries, presence of pain, snoring, sleep apnea, usage of continuous positive airway pressure (cpap), and nose drop usage. Results: At the 3–6-month follow-up, patients with previous nasal surgery showed overall reduced subjective sino-nasal health, increased nasal and ear/head discomfort, increased visual impairment, and decreased psychological HRQoL (all p ≤ 0.026) after pituitary adenomectomy. Patients with pain before surgery showed a trend-level aggravated physical HRQoL (p = 0.084). Conclusion: Our data show that patients with previous nasal surgery have an increased risk of an aggravated sino-nasal and HRQoL outcome after pituitary adenomectomy. These patients should be thoroughly informed about potential consequences to induce realistic patient expectations. Moreover, the study shows that patients with moderately severe allergies, snoring, and sleep apnea (± cpap) usually do not have to expect a worsened sino-nasal health and HRQoL outcome