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

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

Analysing ride behaviours of shared e-scooter users – a case study of Liverpool

[EN] The shared e-scooter is a relatively new form of Micromobility service in urban transit. A better understanding of the use of the scheme will help operators and stakeholders promote this travel mode, contributing to a more sustainable, resilient, environmentally friendly and inclusive transportation system. The availability of high resolution sensor-based location data, when co-analysed with socio-demographic survey data allows insights on where, how, and by whom the service is used. This study focuses on analysing the usage pattern of a recently introduced shared e-scooter scheme in Liverpool, UK, combining survey data of users’ sociodemographic attributes and their full trip records at a fine spatiotemporal granularity. Recency-Frequency (RF) segmentation is used to categorise user behaviour based on their frequency and recency of usage, and a Functional Signatures (FS) dataset is used to enrich contextual information on the origin and destination of e-scooter trips. Overall, this study provides insights into the behaviour of users of shared e-scooters and how the behaviours might vary in different user groups regarding sociodemographic characteristics. The developed analysis framework is also readily transferable to other cities.This research has been sponsored by the Alan Turing Institute under grant number R-LEE006.Yang, Y.; Grant-Muller, S. (2023). Analysing ride behaviours of shared e-scooter users – a case study of Liverpool. Editorial Universitat Politècnica de València. 289-296. https://doi.org/10.4995/CARMA2023.2023.1642228929

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

Exploring informants’ perspectives on the role of crowdsourced active travel data

Peer reviewedPostprin

Stations as mobility hubs: Impact of transforming public spaces on the adoption of sustainable modes of transportation and promotion of intermodality

El transport públic és el mode de transport sostenible per excel·lència per a les masses. Però sovint es passa per alt el paper de les infraestructures de transport públic a l'adopció d'altres mitjans de desplaçament sostenibles, com els compartits. De fet, tenen el potencial de ser centres d'intermodalitat per als mitjans de transport compartits, com ara la micromobilitat. S'han realitzat estudis per analitzar l'impacte de les solucions de micromobilitat en els patrons de mobilitat dels usuaris, especialment per complementar el transport públic a la primera i última milla. No obstant això, a causa d'una possible llacuna en la recerca i d'una implementació ineficient d'aquestes noves solucions de micromobilitat, el potencial més gran de l'ús dels espais associats al transport públic per a la intermodalitat encara està per descobrir. Aquesta tesi té com a objectiu comprendre la intermodalitat a les estacions de ferrocarril, que s'indueix en proporcionar espais per a solucions de micromobilitat. Es fa una àmplia revisió de la literatura per identificar els diversos factors que faciliten la integració del transport públic i la micromobilitat. S'han examinat els suggeriments i les recomanacions formulats a la bibliografia per millorar i continuar promovent l'èxit de la integració de la micromobilitat i el transport públic. A més, s'estudien els impactes de les solucions de micromobilitat a les estacions de transport públic mitjançant l'anàlisi d'una implementació passada i els canvis que va induir en el comportament de viatge dels viatgers en adoptar la micromobilitat per a la primera i la darrera milla de la seva viatge. Els resultats revelen que la provisió de solucions de micromobilitat als centres de transport públic indueix els viatges intermodals, però la seva influència és limitada a causa de la manca d'infraestructura de suport.El transporte público es el modo de transporte sostenible por excelencia para las masas. Pero a menudo se pasa por alto el papel de las infraestructuras de transporte público en la adopción de otros medios de desplazamiento sostenibles, como los compartidos. En realidad, tienen el potencial de ser centros de intermodalidad para los medios de transporte compartidos, como la micromovilidad. Se han realizado estudios para analizar el impacto de las soluciones de micromovilidad en los patrones de movilidad de los usuarios, especialmente para complementar el transporte público en la primera y última milla. Sin embargo, debido a una posible laguna en la investigación y a una implementación ineficiente de estas novedosas soluciones de micro-movilidad, el mayor potencial del uso de los espacios asociados al transporte público para la intermodalidad está aún por descubrir. Esta tesis tiene como objetivo comprender la intermodalidad en las estaciones de ferrocarril, que se induce al proporcionar espacios para soluciones de micro-movilidad. Se realiza una amplia revisión de la literatura para identificar los diversos factores que facilitan la integración del transporte público y la micromovilidad. Se han examinado las sugerencias y recomendaciones formuladas en la bibliografía para mejorar y seguir promoviendo el éxito de la integración de la micromovilidad y el transporte público. Además, se estudian los impactos de las soluciones de micromovilidad en las estaciones de transporte público mediante el análisis de una implementación pasada y los cambios que indujo en el comportamiento de viaje de los viajeros al adoptar la micromovilidad para la primera y la última milla de su viaje. Los resultados revelan que la provisión de soluciones de micromovilidad en los centros de transporte público induce a los viajes intermodales, pero su influencia es limitada debido a la falta de infraestructura de apoyo.Public transportation is the ultimate sustainable mode of transportation for the masses. But the role of public transportation infrastructure is often overlooked in the adoption of other sustainable means of commute, such as shared. In reality, they have the potential to be hubs of intermodality for shared means of transport such as micro-mobility. There have been studies done to analyse the impact of micro-mobility solutions on the mobility patterns of users, especially for complementing public transportation in the first and last mile. But due to a possible research gap and an inefficient implementation of these novelty micro-mobility solutions, the larger potential of using the spaces associated with public transportation for inter-modality is yet to be uncovered. This thesis aims to understand the intermodality in railway stations, that are in-duced by providing spaces for micro-mobility solutions. An extensive review of the literature is done to identify the various factors that facilitate the integration of public transport and micromobility. Suggestions and recommendations made in the literature to enhance and further promote the successful integration of micromobility and public transportation have been examined. Furthermore, the impacts of in-stalling micromobility solutions in public transport stations are studied by analysing a past implementation and the changes it induced in the travel behaviour of the commuters in adopting micromobility for the first and last mile of their journey. The findings reveal that providing micromobility solutions in public transport hubs does induce intermodal travel, but its influence is limited due to a lack of supporting infrastructure

Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems

Short-term demand prediction is important for managing transportation infrastructure, particularly in times of disruption, or around new developments. Many bike-sharing schemes face the challenges of managing service provision and bike fleet rebalancing due to the “tidal flows” of travel and use. For them, it is crucial to have precise predictions of travel demand at a fine spatiotemporal granularities. Despite recent advances in machine learning approaches (e.g. deep neural networks) and in short-term traffic demand predictions, relatively few studies have examined this issue using a feature engineering approach to inform model selection. This research extracts novel time-lagged variables describing graph structures and flow interactions from real-world bike usage datasets, including graph node Out-strength, In-strength, Out-degree, In-degree and PageRank. These are used as inputs to different machine learning algorithms to predict short-term bike demand. The results of the experiments indicate the graph-based attributes to be more important in demand prediction than more commonly used meteorological information. The results from the different machine learning approaches (XGBoost, MLP, LSTM) improve when time-lagged graph information is included. Deep neural networks were found to be better able to handle the sequences of the time-lagged graph variables than other approaches, resulting in more accurate forecasting. Thus incorporating graph-based features can improve understanding and modelling of demand patterns in urban areas, supporting bike-sharing schemes and promoting sustainable transport. The proposed approach can be extended into many existing models using spatial data and can be readily transferred to other applications for predicting dynamics in mass transit systems. A number of limitations and areas of further work are discussed

Passively generated big data for micro-mobility: state-of-the-art and future research directions

The sharp rise in popularity of micro-mobility poses significant challenges in terms of ensuring its safety, addressing its social impacts, mitigating its environmental effects, and designing its systems. Meanwhile, micro-mobility is characterised by its richness in passively generated big data that has considerable potential to address the challenges. Despite an increase in recent literature utilising passively generated micro-mobility data, knowledge and findings are fragmented, limiting the value of the data collected. To fill this gap, this article provides a timely review of how micro-mobility research and practice have exploited passively generated big data and its applications to address major challenges of micro-mobility. Despite its clear advantages in coverage, resolution, and the removal of human errors, passively generated big data needs to be handled with consideration of bias, inaccuracies, and privacy concerns. The paper also highlights areas requiring further research and provides new insights for safe, efficient, sustainable, and equitable micro-mobility

Data towards city bike mobility patterns

New technologies applied to transportation services and the shifting to sustainable modes of transportation turned bike-sharing systems more relevant in the urban mobility scenario. This thesis aims to understand the spatiotemporal station and trip activity patterns in Lisbon bike-sharing system in 2018 and understand trip rate changes in Lisbon bike-sharing system in 2019 and 2020 compared to 2018. By analyzing the spatiotemporal distribution of trips through stations and the weather factors combined with the usage rate throughout the years, it is possible to improve and make the system more suitable to the users’ demand. In this research work, we used large open datasets made available by the Lisbon City Hall, that are deployed by using the CRISP-DM. Our major work contribution was the development of a data analytics process for urban data, specifically bike-sharing data, that helps to understand how people move in the city using bikes. Moreover, we aimed to understand how mobility patterns change over time and the impact of pandemic events. Major findings show that most bike-sharing happens on weekdays, with no precipitation and mild temperature. Additionally, there was an exponential increase in the number of trips, cut short by COVID-19 pandemics. The current approach can be applied to any city with digital data available.As novas tecnologias aplicadas aos serviços de transporte e a transição para meios de transporte sustentáveis tornaram os sistemas de bicicletas partilhadas mais relevantes no cenário da mobilidade urbana. O objetivo deste estudo é compreender os padrões de mobilidade de espaço e tempo das estações e viagens neste sistema de Lisboa em 2018, e também compreender as mudanças na taxa de viagens nos sistemas de Lisboa em 2019 e 2020 em comparação com 2018. Analisando a distribuição de espaço e tempo das viagens através das estações e, os fatores climáticos juntamente com a taxa de utilização ao longo dos anos, é possível melhorar e tornar o sistema mais adequado à procura dos utilizadores. Usamos um grande conjunto de dados com implementação do CRISP-DM. A principal contribuição do trabalho foi o desenvolvimento de um processo de análise e visualização de dados urbanos, especificamente dados de sistemas de bicicletas partilhadas, que permite assim, a melhor compreensão de como as pessoas se movem na cidade usando bicicletas. Além disso, é importante identificar os padrões de mobilidade que mudam com o tempo e o impacto dos eventos pandémicos. Os resultados mostram que a maior parte do uso de bicicletas partilhadas é efetuado durante a semana, sem precipitação e com temperatura amena. Houve um aumento exponencial no número de viagens, por sua vez interrompido pela pandemia do COVID-19. Esta abordagem pode ser aplicada a qualquer cidade com dados digitais disponíveis