Understanding the usage of dockless bike sharing in Singapore

A new generation of bike-sharing services without docking stations is currently revolutionizing the traditional bike-sharing market as it dramatically expands around the world. This study aims at understanding the usage of new dockless bike-sharing services through the lens of Singapore's prevalent service. We collected the GPS data of all dockless bikes from one of the largest bike sharing operators in Singapore for nine consecutive days, for a total of over 14 million records. We adopted spatial autoregressive models to analyze the spatiotemporal patterns of bike usage during the study period. The models explored the impact of bike fleet size, surrounding built environment, access to public transportation, bicycle infrastructure, and weather conditions on the usage of dockless bikes. Larger bike fleet is associated with higher usage but with diminishing marginal impact. In addition, high land use mixtures, easy access to public transportation, more supportive cycling facilities, and free-ride promotions positively impact the usage of dockless bikes. The negative influence of rainfall and high temperatures on bike utilization is also exhibited. The study also offered some guidance to urban planners, policy makers, and transportation practitioners who wish to promote bike-sharing service while ensuring its sustainability.     

Examining spatiotemporal changing patterns of bike-sharing usage during COVID-19 pandemic

The COVID-19 pandemic has led to a globally unprecedented change in human mobility. Leveraging two-year bike-sharing trips from the largest bike-sharing program in Chicago, this study examines the spatiotemporal evolution of bike-sharing usage across the pandemic and compares it with other modes of transport. A set of generalized additive (mixed) models are fitted to identify relationships and delineate nonlinear temporal interactions between station-level daily bike-sharing usage and various independent variables including socio-demographics, land use, transportation features, station characteristics, and COVID-19 infections. Results show: 1) the proportion of commuting trips is substantially lower during the pandemic; 2) the trend of bike-sharing usage follows an “increase-decrease-rebound” pattern; 3) bike-sharing presents as a more resilient option compared with transit, driving, and walking; 4) regions with more white, Asian, and fewer African-American residents are found to become less dependent on bike-sharing; 5) open space and residential areas exhibit less decrease and earlier start-to-recover time; 6) stations near the city center, with more docks, or located in high-income areas go from more increase before the pandemic to more decrease during the pandemic. Findings provide a timely understanding of bike-sharing usage changes and offer suggestions on how different stakeholders should respond to this unprecedented crisis.     

Spatial-interaction network analysis of built environmental influence on daily public transport demand

Many studies have evaluated the influence of the built environment on public transport. Some studies assign subjective weights to environmental factors, which could oversimplify spatial heterogeneity and overlook the temporal dimension. On the other hand, the spatial-interaction network of public transport system is seldom considered. In this paper, we propose an improved framework to explore how individual factors unevenly affect public transport demand over space and time using a geographically and temporally weighted regression (GTWR) model. The proposed framework extends the local built environmental factors by including two network factors extracted from the spatial-interaction network of the public transport system. We conduct a case study in Beijing, China using 686 traffic analysis zones (TAZs). The actual usage of public transport, namely the public transport index (PTI), is estimated by passenger flow divided by the total amount of human flow in a given TAZ. The daily patterns of the spatial heterogeneity in some selected places in the study area is identified and analyzed. It is also found that the estimated coefficient of the variables of the spatial-interaction network is significantly larger than other static environmental factors, indicating that spatial-interaction network can more effectively reflect spatiotemporal heterogeneity in public transport demand. This study provides a better decision-making support for more accurately identifying which factors are most worthy of development, and when and where they can be implemented to improve public transit services.     

Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi

A Mixed Geographically Weighted Regression (GWR) model is applied to explore the effects of shared mobility trips on taxi and public transit ridership at the macro-level. Several essential variables, including socioeconomic, transportation, network, and land use data, are set as the causal factors. The experiment is conducted using the smart card data, vehicle GPS trajectories, and vehicle order data collected in Shenzhen City, China. We show that the Mixed GWR outperforms the basic GWR in model fitting and capturing the unobserved heterogeneity. The spatial analysis reveals that bike-sharing addresses the “last-mile” and “first-mile” problems to bus and metro in the urban periphery. It substitutes the bus and taxis in short-distance journeys in the city center. However, the over-placement of bike-sharing in some regions limits the flexibility of bike-sharing connections to the metro. In the city center, ride-hailing fills the gaps in bus coverage and competes with the metro. In the peripheral areas, ride-hailing replaces buses and improves the accessibility to metro stations. The transportation policy increases the cooperation between ride-hailing and taxis citywide, although competitions in few regions need to be solved. The abovementioned results provide policy suggestions to optimize the allocation of local transportation resources.     

Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai,China

In recent years, dockless bike-sharing has rapidly emerged in many cities all over the world, which provides a flexible tool for short-distance trips and interchange between different modes of transport. However, new problems have arisen with the fast and extensive development of the dockless bike-sharing system, such as high running expenses, ineffective bike repositioning, parking problems and so on. To improve the operations of the dockless bike-sharing system, this study aims to investigate the travel pattern and trip purpose of the bike-sharing users by combining bike-sharing data and points of interest (POIs). A massive amount of bike-sharing trips was obtained from the Mobike company, which is a bike-sharing operator in China. The POIs surrounding each trip origin and destination were derived from the Gaode Map application programming interface. K-means++ clustering was adopted to investigate dockless bike-sharing travel patterns and trip purpose based on trip records and their surrounding POIs. The clustering results show that on weekdays, bike-sharing trip origin and destination can be divided into five typical groups, i.e., dining, transportation, shopping, work and residential places. Dining is the most popular trip purpose by bike-sharing, followed by the transferring to other transportation modes and shopping. In addition, through understanding the spatial distribution of the bike-sharing usage patterns of five typical activities, strategies for improving the operation of the dockless bike-sharing system are provided.     

An empirical analysis of dockless bike-sharing utilization and its explanatory factors: Case study from Shanghai,China

Revealing dockless bike-sharing utilization pattern and its explanatory factors are essential for urban planners and operators to improve the utilization and turnover of public bikes. This study explores the dockless bike-sharing utilization pattern from the perspective of bike using GPS-based bike origin-destination data collected in Shanghai, China. In this paper, utilization patterns are captured by decoupling several spatially cohesive regions with intensive bike use via non-negative matrix factorization. We then measure the utilization efficiency of bikes within each sub-region by calculating Time to booking (ToB) for each bike and explore how the built environment and social-demographic characteristics influence the bike-sharing utilization with ordinary least squares (OLS) regression and geographically weighted regression (GWR) models. The matrix factorization results indicate that the shared bikes mainly serve a certain area instead of the whole city. In addition, the GWR model shows higher explanatory power (Adjusted R² = 0.774) than the OLS regression model (Adjusted R² = 0.520), which suggests a close relationship between bike-sharing utilization and the selected explanatory variables. The coefficients of the GWR model reveal the spatial variations of the linkage between bike-sharing utilization and its explanatory factors across the study area. This study can shed light on understanding the demand and supply of shared bikes for rebalancing and provide support for operators to improve the dockless bike-sharing utilization efficiency.     

Modeling bike-sharing demand using a regression model with spatially varying coefficients

As an emerging mobility service, bike-sharing has become increasingly popular around the world. A critical question in planning and designing bike-sharing services is to know how different factors, such as land-use and built environment, affect bike-sharing demand. Most research investigated this problem from a holistic view using regression models, where assume the factor coefficients are spatially homogeneous. However, ignoring the local spatial effects of different factors is not tally with facts. Therefore, we develop a regression model with spatially varying coefficients to investigate how land use, social-demographic, and transportation infrastructure affect the bike-sharing demand at different stations to address this problem. Unlike existing geographically weighted models, we define station-specific regression and use a graph structure to encourage nearby stations to have similar coefficients. Using the bike-sharing data from the BIXI service in Montreal, we showcase the spatially varying patterns in the regression coefficients and highlight more sensitive areas to the marginal change of a specific factor. The proposed model also exhibits superior out-of-sample prediction power compared with traditional machine learning models and geostatistical models.     

The relationship between ridehailing and public transit in Chicago: A comparison before and after COVID-19

As Transportation Network Companies (TNCs) have expanded their role in U.S. cities recently, their services (i.e. ridehailing) have been subject to scrutiny for displacing public transit (PT) ridership. Previous studies have attempted to classify the relationship between transit and TNCs, though analysis has been limited by a lack of granular TNC trip records, or has been conducted at aggregated scales. This study seeks to understand the TNC-PT relationship in Chicago at a spatially and temporally granular level by analyzing detailed individual trip records. An analysis framework is developed which enables TNC trips to be classified according to their potential relationship with transit: complementary (providing access to/from transit), substitutive (replacing a transit alternative), or independent (not desirably completable by transit). This framework is applied to both regular operating conditions and to early stages of the COVID-19 pandemic, to identify the TNC-PT relationship in these two contexts. We find that complementary TNC trips make up a small fraction of trips taken (approximately 2%), while potential independent trips represent 48% to 53% and potential substitution trips represent 45% to 50%. The percentage of substitution trips drops substantially following COVID-19 shutdowns (to around 14%). This may be attributed to a reduction in work-based TNC trips from Chicago's north side, indicated by changes in spatial distributions and flattening of trips occurring during peak hours. Furthermore, using spatial regression, we find that an increased tendency of TNC trips to substitute transit is related to a lower proportion of elderly people, greater proportion of peak-period TNC travel, greater transit network availability, a higher percentage of white population, and increased crime rates. Our findings identify spatial and temporal trends in the tendency to use TNC services in place of public transit, and thus have potential policy implications for transit management, such as spatially targeted service improvements and safety measures to reduce the possibility of public transit being substituted by TNC services.     

Contested values in bike-sharing mobilities – A case study from Sweden

The aim of this paper is to analyse the type of mobilities and subjects that are being promoted and constituted through bike-sharing systems. This is done through an analysis of the bike-sharing system in the city of Lund in Sweden. The analysis utilises Bacchi's What is the Problem Represented to be? framework and develops it through adding a spatial perspective. Departing from a critical velomobilities perspective, we argue that urban transport policies cannot merely be regarded as one specific and delimited reaction to well-defined policy problems. Instead, the ways that BSSs are, described, motivated – but also spatially organised – constitute which mobilities are produced. The analysis is based in an analysis of relevant policy documents, maps and observations. It is concluded that bike sharing is not seen as cycling and is rarely linked to cycling as such, but rather is seen as part of the public transport system. Further, it is concluded that the motivation behind the location of the stations is to facilitate the flow of workers to public transport, and promote attractiveness and tourism, thus constituting a strong belief in a win-win situation between sustainability and growth. Here prioritisation between different values, and the possible tensions between different social and environmental dimensions of sustainability is down-played.     

Public bicycle as a feeder mode to rail transit in China: The role of gender,age, income,trip purpose,and bicycle theft experience

A marriage between public bicycle and rail transit presents new opportunities for sustainable transportation in Chinese cities. To examine determinants of public bicycle usage for rail transit access, an intercept survey of feeder mode choice among rail transit users was conducted near rail stations in Nanjing, China. Mode choice models were estimated with five feeder mode alternatives, including car, bus, walk, private bike, and public bike. By differentiating between public and private bicycle modes in the mode choice models, the study reveals the effects of personal demographics, trip characteristics, and station environments on public bicycle usage for rail transit access. Results show that female, older, and low-income rail commuters are less likely to use public bicycle to access rail transit. Rail commuters with bicycle theft experience and making school- or work-related trips are more likely to use public bicycle to access rail transit. Land use variables are largely insignificant in this study except that density shows a positive relationship with walking to rail transit. The results on demographic differences raise equity concerns when it comes to investing in public bicycle systems. Policy implications are discussed for Chinese cities to equitably boost public bicycle integration with rail transit.     

Unravel the impact of COVID-19 on the spatio-temporal mobility patterns of microtransit

Shared mobility is an essential component of the larger sharing economy. Ride-hailing, bike-sharing, e-scooters, and other types of shared mobility continue to grow worldwide. Among these services is microtransit, a new transport mode that extends transit coverage within a region. Mobile devices enable microtransit services, aggregating riders and using real-time routing algorithms to group customers traveling in similar directions. Meanwhile, the newly emerged coronavirus, COVID-19, has radically reshaped the ridership behavior of all transit services, including microtransit. While existing research evaluates the performance of microtransit pilot programs before the pandemic, there is no information concerning the spatio-temporal pattern of microtransit activities under the impact of COVID-19. The purpose of this paper is to apply eigendecomposition and k-clique percolation methods to uncover the spatio-temporal patterns of microtransit trips. Further, we used these approaches to identify underlying communities using data from a pilot program in Salt Lake City, Utah. The resulting research offers insight into how COVID-19 altered travel behavior. Specifically, eigendecomposition delineated the homogeneity and heterogeneity of travel patterns across temporal dimensions. We identified first mile/last mile trips as a major source of variance in both pre- and post-COVID periods and that transit-dependent users prove to be inelastic despite the threat of COVID-19. The k-clique percolation method detected possible community formations and tracked how these communities evolved during the pandemic. In addition, we systematically analyzed overlapping communities and the network structure around shared nodes by using a clustering coefficient. The workflow developed in this research broadly is generalizable and valuable for understanding the unique spatio-temporal patterns of microtransit. The framework can also help transit agencies with performance evaluation, regional transport strategies, and optimal vehicle dispatching.     

Measuring destination-based segregation through mobility patterns: Application of transport card data

This study explores the level of segregation experienced by seniors, children/youth, and passengers with disabilities, compared to normal-fare passengers at their trip destination when using public transportation. One week's travel records of public transit passengers were extracted from Seoul's transport card data to compute dissimilarity and exposure indices, theoretically equivalent to those developed in segregation research, to capture destination-based segregation through mobility patterns. Additionally, a multigroup entropy index was computed to measure diversity by assessing the social mixture of all passenger flows in a spatial unit. The results revealed that segregation levels experienced by passengers based on their social groups are notably different depending on the time of day and the day of the week. The computed exposure measure illustrates that the potential interaction between the selected social groups and normal-fare passengers is relatively higher during peak hours on weekdays. The results also show that subway stations provide more opportunities for interaction among different social groups. These findings can contribute to a better understanding of social segregation through mobility patterns as well as the effective quantification of the public transport network performance in terms of providing an interaction opportunity for the groups.     

Can high quality public transport support reduced car parking requirements for new residential apartments?

This paper explores the extent to which high quality public transport can support reduced car parking requirements for new residential apartment buildings. Using a case study of Melbourne, the demand for car parking at residential apartment buildings in proximity to high frequency public transport is assessed, while controlling for a range of socio-demographic, urban design and demand management variables. Key findings indicate that while lower demand for car parking is associated with proximity to high quality public transport, this association is not significant when controlling for other factors that influence car ownership. Public transport service supply within 800 m of residential apartment buildings was instead found to be significant, rather than simple distance to transit. Modelling results suggest an inelastic relationship whereby a 10% increase in public transport service supply is associated with a 0.9–1.2% reduction in car parking demand as measured by levels of car ownership. Notwithstanding broader criticisms of residential off-street parking minimums, the findings have important implications for the development of residential car parking policies, suggesting that city-wide car parking requirements should appropriately reflect the spatial distribution and quality of public transport services.     

Measuring railway network performance considering accessibility levels in cities worldwide

There is a growing acceptance of and interest in transit accessibility-based developments as a means to address urban challenges, such as automobile dependency, air pollution, urban sprawl, and congestion. Additionally, prompting car drivers to switch to public transit requires the construction of attractive and accessible public transit systems. Accordingly, it is necessary to measure railway network performance while considering accessibility, which indicates the potential opportunity of interaction. Thus, this study aims to develop a railway network performance index (RPI) to evaluate transit accessibility, with regard to differences in travel speed, and conduct a comparative analysis of 40 cities worldwide. The major findings are as follows. European cities have a high RPI, but cities in developing countries have relatively low RPI values, due to railway infrastructure shortages. Railway and station density have a positive relationship with RPI, but differences in RPI emerge between cities with the same infrastructure levels. This difference indicates the importance of efficient railway system connections between the distributions of populations and facilities. Overall, this study enhances understandings of transit accessibility and provides benchmark points that may be useful for decision-making processes, transportation investments, and land use policies.     

The impact of car ownership and public transport usage on cancer screening coverage: Empirical evidence using a spatial analysis in England

A spatial analysis has been conducted in England, with the aim to examine the impact of car ownership and public transport usage on breast and cervical cancer screening coverage. District-level cancer screening coverage data (in proportions) and UK census data have been collected and linked. Their effects on cancer screening coverage were modelled by using both non-spatial and spatial models to control for spatial correlation.Significant spatial correlation has been observed and thus spatial model is preferred. It is found that increased car ownership is significantly associated with improved breast and cervical cancer screening coverage. Public transport usage is inversely associated with breast cancer screening coverage; but positively associated with cervical cancer screening. An area with higher median age is associated with higher screening coverage. The effects of other socio-economic factors such as deprivation and economic activity have also been explored with expected results. Some regional differences have been observed, possibly due to unobserved factors.Relevant transport and public health policies are thus required for improved coverage. While restricting access to cars may lead to various benefits in public health, it may also result in worse cancer screening uptake. It is thus recommended that careful consideration should be taken before implementing policy interventions.     

Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap

Over the past two decades, smart card data have received increasing interest from transport researchers as a new source of data for travel behaviour investigation. Collected by smart card systems, smart card data surpass traditional travel survey data in providing more comprehensive spatial–temporal information about urban public transport-based (UPT) trips. However, the utility of smart card data has arguably yet to be exploited fully in terms of extracting and exploring the spatial–temporal dynamics of UPT passenger travel behaviour. To advance previous work in this area, this paper demonstrates a multi-step methodology in order to render more insightful spatial–temporal patterns of UPT passenger travel behaviour. Drawing on the Brisbane, Australia, bus network as the case study, a smart card dataset was first processed in combination with General Transit Specification Feed (GTFS) data to reconstruct travel trajectories of bus passengers at bus stop level of spatial granularity. By applying geographical information system-based (GIS) techniques, this dataset was used to create flow-comaps to visualise the aggregate flow patterns at a network level. The flow-comaps uncovered the major pathways of bus passengers and its variations over a one-day period. The differences within the flow-comaps were also quantified to produce weighted flow-comaps that highlighted the major temporal changes of passenger flow patterns along a number of stop-to-stop linkages of the bus network. The proposed methodology visually unveiled the spatial–temporal travel behaviour dynamics of UPT passengers and, in doing so, showed the potential to contribute to a new evidence base with the capacity to inform local public transport policy.     

Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou,China

Understanding the relationship between the rail transit ridership and the built environment is crucial to promoting transit-oriented development and sustainable urban growth. Geographically weighted regression (GWR) models have previously been employed to reveal the spatial differences in such relationships at the station level. However, few studies characterized the built environment at a fine scale and associated them with rail transit usage. Moreover, none of the existing studies attempted to categorize the stations for policy-making considering varying impacts of the built environment. In this study, taking Guangzhou as an example, we integrated multi-source spatial big data, such as high spatial resolution remote sensing images, points of interest (POIs), social media and building footprint data to precisely quantify the characteristics of the built environment. This was combined with a GWR model to understand how the impacts of the fine-scale built environment factors on the rail transit ridership vary across the study region. The k-means clustering method was employed to identify distinct station groups based on the coefficients of the GWR model at the local stations. Policy zoning was proposed based on the results and differentiated planning guidance was suggested for different zones. These recommendations are expected to help increase rail transit usage, inform rail transit planning (to relieve the traffic burden on currently crowed lines), and re-allocate industrial and living facilities to reduce the commute for the residents. The policy and planning implications are crucial for the coordinated development of the rail transit system and land use.     

Examining public transport usage by older adults with smart card data: A longitudinal study in Japan

Understanding public transport usage by older adults is necessary to develop senior-friendly public transport and improve the mobility of older persons. Although extensive literature has examined the travel patterns of older adults, very limited efforts have been invested to explore the longitudinal variability in public transport usage by different age groups of older adults. To address this limitation, we developed user-monthly profiles to explore the seasonal variability in public transport usage by older adults and defined user-based time slots of the day and geographical user areas to represent daily trip patterns and examine day-to-day variability. Using one-year smart card transaction data and an anonymous cardholder database from Shizuoka, Japan, we evaluated the seasonal and day-to-day variability in public transport usage by older adults. We also analyzed the role of age and living environment in travel pattern variability. The results indicate that older adults in highly developed areas and younger-old group (aged 65–74) are more likely to be characterized by high-frequency public transport usage and low seasonal variability. Additionally, the day-to-day variability in public transport usage by older adults is greater in more developed areas and appears to increase with age. This study enhances our understanding of public transport usage by older adults, which may contribute to the development of senior-friendly public transport policies and services.     

Structures of commitment in mode use: a comparison of Switzerland, Germany and Great Britain

Travellers commit themselves to particular behaviours through the ownership of cars and season tickets. They trade a large one-off payment for low or zero marginal cost at the point of use. It can be assumed that these commitments influence travel behaviour. To the knowledge of the authors there is no literature which addresses the choice between the commitment to the one or the other mode and its impacts on travel behaviour.The paper presents models using structural equation modelling to test a-priori hypotheses on the paths linking car-availability, season-ticket-ownership and modal usage. Modal usage is operationalised as the number of trips by car, public transport, or as the distances travelled by car or public transport. The models are based on three different surveys: Switzerland, Germany and Great Britain. The results confirm the dominance of car-availability, which drives the other variables, but the relationships are more complicated than generally assumed.     

Measuring transit use variability with smart-card data

The potential of smart-card data for measuring the variability of urban public transit network use is the focus of this paper. Data collected during 277 consecutive days of travel on a Canadian transit network are processed for this purpose. The organization of data using an object-oriented approach is discussed. Then, measures of spatial and temporal variability of transit use for various types of card are defined and estimated using the data sets presented. Data mining techniques are also used to identify transit use cycles and homogenous days and weeks of travel among card segments and at various times of the year.