Unveiling E-bike potential for commuting trips from GPS traces

Common goals of sustainable mobility approaches are to reduce the need for travel, to facilitate modal shifts, to decrease trip distances and to improve energy efficiency in the transportation systems. Among these issues, modal shift plays an important role for the adoption of vehicles with fewer or zero emissions. Nowadays, the electric bike (e-bike) is becoming a valid alternative to cars in urban areas. However, to promote modal shift, a better understanding of the mobility behaviour of e-bike users is required. In this paper, we investigate the mobility habits of e-bikers using GPS data collected in Belgium from 2014 to 2015. By analysing more than 10,000 trips, we provide insights about e-bike trip features such as: distance, duration and speed. In addition, we offer a deep look into which routes are preferred by bike owners in terms of their physical characteristics and how weather influences e-bike usage. Results show that trips with higher travel distances are performed during working days and are correlated with higher average speeds. Usage patterns extracted from our data set also indicate that e-bikes are preferred for commuting (home-work) and business (work related) trips rather than for recreational trips

A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile

The recent emergence of dockless bike sharing systems has resulted in new patterns of urban transport. Users can begin and end trips from their origin and destination locations rather than docking stations. Analysis of changes in the spatiotemporal availability of such bikes has the ability to provide insights into urban dynamics at a finer granularity than is possible through analysis of travel card or dock-based bike scheme data. This study analyses dockless bike sharing in Nanchang, China over a period when a new metro line came into operation. It uses spatial statistics and graph-based approaches to quantify changes in travel behaviours and generates previously unobtainable insights about urban flow structures. Geostatistical analyses support understanding of large-scale changes in spatiotemporal travel behaviours and graph-based approaches allow changes in local travel flows between individual locations to be quantified and characterized. The results show how the new metro service boosted nearby bike demand, but with considerable spatial variation, and changed the spatiotemporal patterns of bike travel behaviour. The analysis also quantifies the evolution of travel flow structures, indicating the resilience of dockless bike schemes and their ability to adapt to changes in travel behaviours. More widely, this study demonstrates how an enhanced understanding of urban dynamics over the “last-mile” is supported by the analyses of dockless bike data. These allow changes in local spatiotemporal interdependencies between different transport systems to be evaluated, and support spatially detailed urban and transport planning. A number of areas of further work are identified to better to understand interdependencies between different transit system components

Profiling the Dynamic Pattern of Bike-sharing Stations: a case study of Citi Bike in New York City

This research applies a hierarchical k-means clustering method on the TF-IDF weighted 2019 cycling transactions from the Citi Bike bike-sharing system operating in New York City, with the primary goal of investigating the spatiotemporal usage pattern of its docking points. With a particular focus on bike-sharing stations in Manhattan, we classify 504 stations into four main clusters featuring heterogeneous dynamic usages, including leisure-oriented, residentialoriented, workplace-oriented, and off-peak oriented. We interpret each cluster based on their salient characteristics and anticipate possible future directions of this work

Geographic Data Informs Funding and Management of Metro Bike Share System

Geographic data is often used to supplement business data, but geographic location (GeoLocation) data has business value in its own right. This geo-spatial study presents a midwestern town’s use of location analytics to infer the purpose for bike trips (usage purpose) and perform what-if analysis to enhance transportation options. The study applies spatial data analysis of bikeshare within transit management and public planning to address funding sources and public good. This case includes GeoExtension of the Metro Bike Share source data by utilizing U. S. Census data. The overall Metro Transit operational goal is to effectively manage the rideshare program to maximize community benefits, particularly in offering optimal transit options. Analysis to inform business operations are 1) inferring likely purpose for bike rides to differentiate between transportation and leisure; 2) determine if bike use integrates with other transit offerings, and 3) to provide transportation options to residents in low-income areas

Temporal decomposition and semantic enrichment of mobility flows

Mobility data has increasingly grown in volume over the past decade as loc- alisation technologies for capturing mobility ows have become ubiquitous. Novel analytical approaches for understanding and structuring mobility data are now required to support the back end of a new generation of space-time GIS systems. This data has become increasingly important as GIS is now an essen- tial decision support platform in many domains that use mobility data, such as eet management, accessibility analysis and urban transportation planning. This thesis applies the machine learning method of probabilistic topic mod- elling to decompose and semantically enrich mobility ow data. This process annotates mobility ows with semantic meaning by fusing them with geograph- ically referenced social media data. This thesis also explores the relationship between causality and correlation, as well as the predictability of semantic decompositions obtained during a case study using a real mobility dataset

A spatiotemporal analysis of the impact of lockdown and coronavirus on London’s bicycle hire scheme: from response to recovery to a new normal

The coronavirus pandemic that started in 2019 has had wide-ranging impacts on many aspects of people’s daily lives. At the peak of the outbreak, lockdown measures and social distancing changed the ways in which cities function. In particular, they had profound impacts on urban transportation systems, with public transport being shut down in many cities. Bike share systems (BSS) were widely reported as having experienced an increase in demand during the early stages of the pandemic before returning to pre-pandemic levels. However, the studies published to date focus mainly on the first year of the pandemic, when various waves saw continual relaxing and reintroductions of restrictions. Therefore, they fall short of exploring the role of BSS as we move to the post-pandemic period. To address this gap, this study uses origin-destination (O-D) flow data from London’s Santander Cycle Hire Scheme from 2019–2021 to analyze the changing use of BSS throughout the first two years of the pandemic, from lockdown to recovery. A Gaussian mixture model (GMM) is used to cluster 2019 BSS trips into three distinct clusters based on their duration and distance. The clusters are used as a reference from which to measure spatial and temporal change in 2020 and 2021. In agreement with previous research, BSS usage was found to have declined by nearly 30% during the first lockdown. Usage then saw a sharp increase as restrictions were lifted, characterized by longer, less direct trips throughout the afternoon rather than typical peak commuting trips. Although the aggregate number of BSS trips appeared to return to normal by October 2020, this was against the backdrop of continuing restrictions on international travel and work from home orders. The period between July and December 2021 was the first period that all government restrictions were lifted. During this time, BSS trips reached higher levels than in 2019. Spatio-temporal analysis indicates a shift away from the traditional morning and evening peak to a more diffuse pattern of working hours. The results indicate that the pandemic may have had sustained impacts on travel behavior, leading to a “new normal” that reflects different ways of working

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

Time-delayed collective flow diffusion models for inferring latent people flow from aggregated data at limited locations

The rapid adoption of wireless sensor devices has made it easier to record location information of people in a variety of spaces (e.g., exhibition halls). Location information is often aggregated due to privacy and/or cost concerns. The aggregated data we use as input consist of the numbers of incoming and outgoing people at each location and at each time step. Since the aggregated data lack tracking information of individuals, determining the flow of people between locations is not straightforward. In this article, we address the problem of inferring latent people flows, that is, transition populations between locations, from just aggregated population data gathered from observed locations. Existing models assume that everyone is always in one of the observed locations at every time step; this, however, is an unrealistic assumption, because we do not always have a large enough number of sensor devices to cover the large-scale spaces targeted. To overcome this drawback, we propose a probabilistic model with flow conservation constraints that incorporate travel duration distributions between observed locations. To handle noisy settings, we adopt noisy observation models for the numbers of incoming and outgoing people, where the noise is regarded as a factor that may disturb flow conservation, e.g., people may appear in or disappear from the predefined space of interest. We develop an approximate expectation-maximization (EM) algorithm that simultaneously estimates transition populations and model parameters. Our experiments demonstrate the effectiveness of the proposed model on real-world datasets of pedestrian data in exhibition halls, bike trip data and taxi trip data in New York City