Data-driven interpretation on interactive and nonlinear effects of the correlated built environment on shared mobility

Understanding the usage demand of shared mobility systems in different areas of a city and its determinants is crucial for planning, operation and management of the systems. This study leverages an unbiased data-driven approach called accumulated effect analysis for examining the complex (nonlinear and interactive) effects of correlated built environment factors on the usage of shared mobility. Special research emphasis is given to unraveling the complex effects using an unbiased and data-driven approach that can overcome the impacts of correlations among built environment factors. Based on empirical analysis of synthetic data and a field dataset about dockless bike sharing systems (DLBS), results demonstrate that the method of partial dependency analysis prevalent in the relevant literature, will result in biases when investigating the effects of correlated built environment factors. In comparison, accumulated local effect analysis can appropriately interpret the effects of correlated built environment factors. The main effects of many built environment factors on the usage of DLBS present nonlinear and threshold patterns, quantitively revealed by accumulated local analysis. The approach can reveal complex interaction effects between different built environment factors (e.g., commercial service and education facility, and metro station coverage and living facility) on the usage of DLBS as well. The interactions among two built environment factors could even change with the values of the factors rather than invariant. The outcomes offer a new approach for revealing complex influences of different built environment factors with correlations as well as in-depth empirical understandings regarding the usage of DLBS

Spatio-temporal forecasts for bike availability in dockless bike sharing systems

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesForecasting bike availability is of great importance when turning the shared bike into a reliable, pleasant and uncomplicated mode of transport. Several approaches have been developed to forecast bike availability in station-based bike sharing systems. However, dockless bike sharing systems remain fairly unexplored in that sense, despite their rapid expansion over the world in recent years. To fill this gap, this thesis aims to develop a generally applicable methodology for bike availability forecasting in dockless bike sharing systems, that produces automated, fast and accurate forecasts. To balance speed and accuracy, an approach is taken in which the system area of a dockless bike sharing system is divided into spatially contiguous clusters that represent locations with the same temporal patterns in the historical data. Each cluster gets assigned a model point, for which an ARIMA(p,d,q) forecasting model is fitted to the deseasonalized data. Each individual forecast will inherit the structure and parameters of one of those pre-build models, rather than building a new model on its own. The proposed system was tested through a case study in San Francisco, California. The results showed that the proposed system outperforms simple baseline methods. However, they also highlighted the limited forecastability of dockless bike sharing data

Impacts of the COVID-19 pandemic on the spatio-temporal characteristics of a bicycle-sharing system: A case study of Pun Pun, Bangkok, Thailand

The COVID-19 pandemic is found to be one of the external stimuli that greatly affects mobility of people, leading to a shift of transportation modes towards private individual ones. To properly explain the change in people's transport behavior, especially in pre- and post- pandemic periods, a tensor-based framework is herein proposed and applied to Pun Pun-the only public bicycle-sharing system in Bangkok, Thailand-where multidimensional trip data of Pun Pun are decomposed into four different modes related to their spatial and temporal dimensions by a non-negative Tucker decomposition approach. According to our computational results, the first pandemic wave has a sizable influence not only on Pun Pun but also on other modes of transportation. Nonetheless, Pun Pun is relatively more resilient, as it recovers more quickly than other public transportation modes. In terms of trip patterns, we find that, prior to the pandemic, trips made during weekdays are dominated by business trips with two peak periods (morning and evening peaks), while those made during weekends are more related to leisure activities as they involve stations nearby a public park. However, after the first pandemic wave ends, the patterns of weekday trips have been drastically changed, as the number of business trips sharply drops, while that of educational trips connecting metro/subway stations with a major educational institute in the region significantly rises. These findings may be regarded as a reflection of the ever-changing transport behavior of people seeking a sustainable mode of private transport, with a more positive outlook on the use of bicycle-sharing system in Bangkok, Thailand

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

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

Are footpaths encroached by shared e-scooters? Spatio-temporal Analysis of Micro-mobility Services

Micro-mobility services (e.g., e-bikes, e-scooters) are increasingly popular among urban communities, being a flexible transport option that brings both opportunities and challenges. As a growing mode of transportation, insights gained from micro-mobility usage data are valuable in policy formulation and improving the quality of services. Existing research analyses patterns and features associated with usage distributions in different localities, and focuses on either temporal or spatial aspects. In this paper, we employ a combination of methods that analyse both spatial and temporal characteristics related to e-scooter trips in a more granular level, enabling observations at different time frames and local geographical zones that prior analysis wasn't able to do. The insights obtained from anonymised, restricted data on shared e-scooter rides show the applicability of the employed method on regulated, privacy preserving micro-mobility trip data. Our results showed population density is the topmost important feature, and it associates with e-scooter usage positively. Population owning motor vehicles is negatively associated with shared e-scooter trips, suggesting a reduction in e-scooter usage among motor vehicle owners. Furthermore, we found that the effect of humidity is more important than precipitation in predicting hourly e-scooter trip count. Buffer analysis showed, nearly 29% trips were stopped, and 27% trips were started on the footpath, revealing higher utilisation of footpaths for parking e-scooters in Melbourne.Comment: Accepted to IEEE International Conference on Mobile Data Managemen

The Spatial Equity of Dockless Micromobility Sharing Systems in Calgary, Canada

Micromobility sharing systems, including bikes and e-scooters, are often promoted as solutions to urban transportation equity challenges. Dockless micromobility sharing systems however remain understudied due in part to their novelty. In particular, there has been limited research on the spatial equity of e-scooter sharing, which concerns whether systems are equally accessible across a city regardless of the relative advantage and disadvantage of urban areas. This thesis reports on two related analyses of the spatial equity of e-scooter sharing in Calgary, Alberta, Canada using an open dataset of three months worth of trip data (July – September, 2019): a gravity model approach to analyzing the spatial equity of e-scooter trip flows, and an ANOVA and linear regression-based comparison of the spatial equity profiles of dockless bike and e-scooter sharing. The results show that both dockless bike and e-scooter sharing in Calgary are spatially inequitable, and that there are no significant spatial equity differences between the use of dockless bikes and e-scooters

Bike sharing as part of urban mobility in Helsinki : a user perspective

The number of bike-sharing systems has increased rapidly during the last decade. These systems expand urban mobility options and provide a solution to the so-called “last-mile” problem. While new bike-sharing systems are opened and current ones expanded in Finland and elsewhere in large numbers, it is important to understand how these systems are used and by whom. Despite the wealth of bike-sharing literature, usage patterns by different user groups are still not yet well studied. This knowledge is needed to ensure that the benefits of bike-sharing systems distribute as evenly as possible to the citizens. In this study, I have employed a person-based approach to study mobility patterns of bike-sharing users in Helsinki. The system in Helsinki was opened in 2016 and the urban bikes quickly became popular among citizens. I have aimed to understand how equally the bike-sharing system in Helsinki is serving the citizens and how different user groups have differed from each other in their use. I have also studied how the system is linking to public transport in Helsinki and compared the bike-sharing system usage and users in Helsinki to other systems internationally. These specific questions stem from the systematic literature review on bike-sharing (n=799), which I carried out before the empirical study. In this study, I have used a dataset provided by Helsinki Region Transport, which contained all the bike-sharing trips (~1.5 million) from 2017. Besides the trip information, the dataset contained the basic demographic information of the user. The results of literature review show bike-sharing systems have been an active and extensive study topic even though the study areas are mostly concentrated to certain cities. Based on the empirical data-analysis, majority of bike-sharing users are young adults between 25-35 years old whereas the share of over 50 year olds is only 12 %. Both men and women use urban bikes actively but men are overrepresented both in the number of users and trips. The use of bikes is not equal but a small minority of users have generated the majority of trips. The users who live inside the bike station coverage area make around 80 % of the trips implying that the proximity of a station has a considerable impact on the use. Trip profiles of those living inside the system coverage area differ considerably from those who live outside the area. For example, the users living inside the area seem to combine urban bikes less with public transport and they use urban bikes relatively more on weekends compared to the other group. The subscription type and use activity are also important factors shaping usage patterns. Then again, age and gender are more important in determining whether someone chooses to become a user than in shaping usage patterns. The use of bike-sharing system in Helsinki has been high even when compared internationally. The results of this study show that the high usage rates still do not necessarily mean that the system would be equally used by citizens. Based on the systematic review, equity is a critical topic to address in relation to bike-sharing users. The user profiles in Helsinki seem to follow similar patterns of bike sharing as found in other cities with an overrepresentation of certain population groups. The use of young adults might promise well for the change of urban mobility. However, it is important to keep promoting cycling to a wider range of the population. The bike-sharing system in Helsinki will expand in 2019 to new areas. Based on the results of this study the expansion seems reasonable as a large part of the users live close to a bike-sharing station. The expansion will then bring the full benefits of bike sharing accessible to a larger group of people in Helsinki. The system seems both to replace and extend the public transport system, which is common to bike-sharing systems in many cities. From the data perspective, the origin-destination type of trip data, which was used in this study, provided a great deal of useful information about users and usage profiles. Even when accounting for limitations in this data type, it is still an excellent addition complementing existing cycling data sources

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

Do shared E-bikes reduce urban carbon emissions?

Under the threat of climate change, many global cities nowadays are promoting shared commuting modes to reduce greenhouse gas emissions. Shared electric bikes (e-bikes) are emerging modes that compete with bikes, cars, or public transit. However, there is a lack of empirical evidence for the net effect of shared e-bikes on carbon emissions, as shared e-bikes can substitute for both higher carbon emissions modes and cleaner commuting modes. Using a large collection of spatio-temporal trajectory data of shared e-bike trips in two provincial cities (Chengdu and Kunming) in China, this study develops a travel mode substitution model to identify the changes in travel modes due to the introduction of shared e-bike systems and to quantify the corresponding impact on net carbon emissions. We find that, on average, shared e-bikes decrease carbon emissions by 108–120 g per kilometre. More interestingly, the reduction effect is much stronger in underdeveloped non-central areas with lower density, less diversified land use, lower accessibility, and lower economic level. Although the actual carbon reduction benefits of shared e-bike schemes are far from clear, this study bears important policy implications for exploring this emerging micro-mobility mode to achieve carbon reduction impacts