A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage

This study analyzes the temporally resolved location and trip data of shared e-scooters over nine months in Berlin from one of Europe’s most widespread operators. We apply time, distance, and energy consumption filters on approximately 1.25 million trips for outlier detection and trip categorization. Using temporally and spatially resolved trip pattern analyses, we investigate how the built environment and land use affect e-scooter trips. Further, we apply a density-based clustering algorithm to examine point of interest-specific patterns in trip generation. Our results suggest that e-scooter usage has point of interest related characteristics. Temporal peaks in e-scooter usage differ by point of interest category and indicate work-related trips at public transport stations. We prove these characteristic patterns with the statistical metric of cosine similarity. Considering average cluster velocities, we observe limited time-saving potential of e-scooter trips in congested areas near the city center

A Systematic Literature Review on Machine Learning in Shared Mobility

Shared mobility has emerged as a sustainable alternative to both private transportation and traditional public transport, promising to reduce the number of private vehicles on roads while offering users greater flexibility. Today, urban areas are home to a myriad of innovative services, including car-sharing, ride-sharing, and micromobility solutions like moped-sharing, bike-sharing, and e-scooter-sharing. Given the intense competition and the inherent operational complexities of shared mobility systems, providers are increasingly seeking specialized decision-support methodologies to boost operational efficiency. While recent research indicates that advanced machine learning methods can tackle the intricate challenges in shared mobility management decisions, a thorough evaluation of existing research is essential to fully grasp its potential and pinpoint areas needing further exploration. This paper presents a systematic literature review that specifically targets the application of Machine Learning for decision-making in Shared Mobility Systems. Our review underscores that Machine Learning offers methodological solutions to specific management challenges crucial for the effective operation of Shared Mobility Systems. We delve into the methods and datasets employed, spotlight research trends, and pinpoint research gaps. Our findings culminate in a comprehensive framework of Machine Learning techniques designed to bolster managerial decision-making in addressing challenges specific to Shared Mobility across various levels

On the adoption of e-moped sharing systems

AbstractRecent years have witnessed the emerging of novel shared mobility solutions that provide diffused on-demand access to transportation. The widespread adoption of these solutions, particularly electric mopeds (e-mopeds), is expected to bring important benefits such as the reduction of noise and atmospheric pollution, and road congestion, with extensive repercussions on liveability and quality of life in urban areas. Currently, almost no effort has been devoted to exploring the adoption patterns of e-moped sharing services, therefore, optimal management and allocation of vehicles appears to be a problem for service managers. In this study, we tried to demonstrate the validity of the hypothesis that the adoption of electric mopeds depends on the built environment and demographic aspects of each neighbourhood. In detail, we singled out three features concerning the area characteristics (distance from centre, walkability, concentration of places) and one about the population (education index). The results obtained on a real world case study show the strong impact these factors have in determining the adoption of e-moped sharing services. Finally, an analysis was conducted on the possible role that the electric moped sharing can play in social equalization by studying the interactions between rich and poor neighbourhoods. The results of the analyses conducted indicate that communities within a city tend to aggregate by wealth and isolate themselves from one another (social isolation): very few interactions, in terms of trajectories, have been observed between the richest and poorest areas of the city under study

Equity in Transportation: Data Driven Analysis of Transportation Services and Infrastructures

Achieving equity in transportation is an ongoing challenge, as transportation options still vary tremendously when it comes to marginalized populations. This dissertation addresses this challenge by conducting a comprehensive review of existing transportation equity literature and identifying two critical gaps: the lack of data-driven approaches to studying spatial mismatch between transportation supply and demand, and limited information on women\u27s perceptions and expectations towards emerging transportation services. Chapter two introduces the concept of transportation deserts, specifically transit deserts and walking deserts, and develops data-driven frameworks to identify and investigate neighborhoods with limited transportation service supply but high demand. The frameworks compare mobility demand and supply for active transportation modes and utilize statistical modeling techniques to reveal the inequitable distribution of transportation services. The identification of transportation deserts provides valuable insights for investment and redevelopment, highlighting areas of underinvestment. Chapter three focuses on gender equity and the lack of understanding about transportation user preferences, particularly for women. Through a gender-sensitive analysis of online reviews using text-mining techniques, the chapter presents an empirical analysis of rider satisfaction with scooter services. The study utilizes online data from app store reviews and employs machine learning techniques to uncover factors that influence overall satisfaction across genders. The findings enhance our understanding of gendered differences in micromobility rider sentiment and satisfaction. In conclusion, this dissertation offers a comprehensive examination of transportation equity from multiple perspectives. It identifies critical gaps in existing literature and employs innovative analytical methodologies to address these gaps. The research findings have important policy implications for city planners, transportation managers, urban authorities, and decision-makers striving to create inclusive and vibrant urban spaces that benefit all members of society. By addressing these gaps, policymakers can promote equitable transportation services and ensure access to safe, reliable, and affordable transportation options for all individuals

A review on the factors influencing the adoption of new mobility technologies and services: autonomous vehicle, drone, micromobility and mobility as a service

New mobility technologies and services could address a series of transport-related problems such as pollution, congestion, unpleasant travel experiences, as well as first- and last-mile in-connectivity. Understanding the key factors influencing adoption and enablers is critical to the rollout of the new mobility technologies and services. The objective of this paper is to conduct a systematic review of the new mobility technologies and services, especially on autonomous vehicles, drones, micromobility and Mobility as a Service (MaaS). The ultimate goal is to gain a deeper insight into the factors that affect the adoption or preferences of these technologies and services and thus provide policy implications at the strategic level. The results of the review identified several (1) shared, (2) exclusive, (3) opposing and (4) mixed impacts factors that strongly influence the uptake of new mobilities. The synthesised finding will contribute to policy decisions, particularly regarding the sequencing of the launch and development priorities of new mobility technologies and services. To encourage the uptake of new mobility technologies and services, further promotion would benefit from (1) embedding a spatio-temporal perspective, (2) undertaking a careful market segmentation and (3) a careful segmentation of technology and services based on features, application contexts and purposes

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

A Review of Business Models for Shared Mobility and Mobility-as-a-Service (MaaS):A Research Report

The mobility solutions that currently dominate the mobility market have raised global challenges. Specifically, mass car ownership has led to traffic congestion, shortage of parking spaces, and sustainability issues. Recently, mobility solutions driven by technological advancements have emerged to address these issues via more efficient and sustainable use of resources. However, the wide range of mobility offerings has led to a scattered mobility market, and oversight is hard to grasp for travelers. Mobility-as-a-Service (MaaS) platforms aim to address this issue by integrating mobility services into a single platform. However, MaaS providers (operators) struggle to find sustainable business models. Additionally, research on shared mobility business models is limited, and there is little oversight in the scattered business model landscape. This report addresses this issue by summarizing the dominant business models in the mobility market through a systematic review of current initiatives and literature. It provides an overview of active MaaS business models and challenges and opportunities to integrate mobility services into MaaS. The types of mobility services reviewed in this study include bike-sharing, scooter-sharing, car-sharing, e-hailing, and MaaS platform providers. For each mobility service, the dominant operating mode and the main business model actors are identified and represented using the Service-Dominant Business Model Radar (SDBM/R). Furthermore, the value exchanges between the actors are mapped in Value Capture Diagrams. The report concludes with a discussion on the challenges and opportunities related to synthesizing shared mobility modes into MaaS and the expectations for its future