Bicycle Sharing Systems: Fast and Slow Urban Mobility Dynamics

In cities all around the world, new forms of urban micromobility have observed rapid and wide-scale adoption due to their benefits as a shared mode that are environmentally friendly, convenient and accessible. Bicycle sharing systems are the most established among these modes, facilitating complete end-to-end journeys as well as forming a solution for the first/last mile issue that public transportation users face in getting to and from transit stations. They mark the beginnings of a gradual transition towards a more sustainable transportation model that include greater use of shared and active modes. As such, understanding the way in which these systems are used is essential in order to improve their management and efficiency. Given the lack of operator published data, this thesis aims to explore the utility of open bicycle sharing system data standards that are intended for real-time dissemination of bicycle locations in uncovering novel insights into their activity dynamics over varying temporal and geographical scales. The thesis starts by exploring bicycle sharing systems at a global-scale, uncovering their long-term growth and evolution through the development of data cleaning and metric creation heuristics that also form the foundations of the most comprehensive classification of systems. Having established the values of these metrics in conducting comparisons at scale, the thesis then analyses the medium-term impacts of mobility interventions in the context of the COVID-19 pandemic, employing spatio-temporal and network analysis methods that highlight their adaptability and resilience. Finally, the thesis closes with the analysis of granular spatial and temporal dynamics within a dockless system in London that enable the identification of the variations in journey locations throughout different times of the day. In each of these cases, the research highlights the indispensable value of open data and the important role that bicycle sharing systems play in urban mobility

Toward Sustainability: Bike-Sharing Systems Design, Simulation and Management

The goal of this Special Issue is to discuss new challenges in the simulation and management problems of both traditional and innovative bike-sharing systems, to ultimately encourage the competitiveness and attractiveness of BSSs, and contribute to the further promotion of sustainable mobility. We have selected thirteen papers for publication in this Special Issue

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

Social Innovation in Sustainable Urban Development

How can a city advance from social invention to social innovation, to attain sustainable urban development (SUD)? Many new ideas, initiatives, and showcases for social innovation have been introduced; however, project-based forms of experimentation are often just part of the ongoing urban politics (or governmentality), and consequently somewhat ephemeral, with traditional siloed city administrations remaining a central obstacle to SUD. Our Special Issue presents twelve papers that address the question of social innovation in sustainable urban development from very different angles. The contributions span issues concerning smart cities, innovation in the adaptive reuse of urban heritage, as well as policy options for regions in transition. In terms of social innovation for SUD purposes, the presented solutions range from transferable legal formalizations to the creation of urban ecosystems whose institutional structures ensure the inclusion of the civil society. Instead of a comprehensive, integrative SUD, robust sectoral solutions, or even phased solutions, are more likely to be sought

Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications

[EN] The need for effective freight and human transportation systems has consistently increased during the last decades, mainly due to factors such as globalization, e-commerce activities, and mobility requirements. Traditionally, transportation systems have been designed with the main goal of reducing their monetary cost while offering a specified quality of service. During the last decade, however, sustainability concepts are also being considered as a critical component of transportation systems, i.e., the environmental and social impact of transportation activities have to be taken into account when managers and policy makers design and operate modern transportation systems, whether these refer to long-distance carriers or to metropolitan areas. This paper reviews the existing work on different scientific methodologies that are being used to promote Sustainable Transportation Systems (STS), including simulation, optimization, machine learning, and fuzzy sets. This paper discusses how each of these methodologies have been employed to design and efficiently operate STS. In addition, the paper also provides a classification of common challenges, best practices, future trends, and open research lines that might be useful for both researchers and practitioners.This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033, RED2018-102642-T) and the SEPIE Erasmus+ Program (2019-I-ES01-KA103-062602), and the IoF2020-H2020 (731884) project.Torre-Martínez, MRDL.; Corlu, CG.; Faulin, J.; Onggo, BS.; Juan-Pérez, ÁA. (2021). Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications. Sustainability. 13(3):1-21. https://doi.org/10.3390/su1303155112113

Revealing the physics of movement: comparing the similarity of movement characteristics of different types of moving objects

We propose a segmentation and feature extraction method for trajectories of moving objects. The methodology consists of three stages: trajectory data preparation; global descriptors computation; and local feature extraction. The key element is an algorithm that decomposes the profiles generated for different movement parameters (velocity, acceleration, etc.) using variations in sinuosity and deviation from the median line. Hence, the methodology enables the extraction of local movement features in addition to global ones that are essential for modeling and analyzing moving objects in applications such as trajectory classification, simulation and extraction of movement patterns. As a case study, we show how the method can be employed in classifying trajectory data generated by unknown moving objects and assigning them to known types of moving objects, whose movement characteristics have been previously learned. We have conducted a series of experiments that provide evidence about the similarities and differences that exist among different types of moving objects. The experiments show that the methodology can be successfully applied in automatic transport mode detection. It is also shown that eye-movement data cannot be successfully used as a proxy of full-body movement of humans, or vehicles

MODELING AND ANALYSIS OF AN AUTONOMOUS MOBILITY ON DEMAND SYSTEM

Ph.DDOCTOR OF PHILOSOPH