Bikeshare trip generation in New York City

Cities around the world and in the US are implementing bikesharing systems, which allow users to access shared bicycles for short trips, typically in the urban core. Yet few scholars have examined the determinants of bikeshare station usage using a fine-grained approach. We estimate a series of Bayesian regression models of trip generation at stations, examining the effects bicycle infrastructure, population and employment, land use mix, and transit access separately by season of the year, weekday/weekend, and user type (subscriber versus casual). We find that bikeshare stations located near busy subway stations and bicycle infrastructure see greater utilization, and that greater population and employment generally predict greater usage. Our findings are nuanced, however; for instance, those areas with more residential population are associated with more trips by subscribers and on both weekdays and non-working days; however, the effect is much stronger on non-working days. Additional nuances can be found in how various land use variables affect bikeshare usage. We use our models, based on 2014 data, to forecast the trips generated at new stations opened in 2015. Results suggest there is large variation in predictive power, partly caused by variation in weather, but also by other factors that cannot be predicted. This leads us to the conclusion that the nuances we find in our inferential analysis are more useful for transportation planners.     

Dockless bike-sharing systems: what are the implications?

ABSTRACT

The emergence of dockless bike-sharing services has revolutionised bike-sharing markets in recent years, and the dramatic growth of shared bike fleets in China, as well as their rapid expansion throughout the world, exceeds prior expectations. An understanding of the impacts of these new dockless bike-sharing systems is of vital importance for system operations, transportation and urban planning research. This paper provides a first overview of the emerging literature on implications of dockless bike-sharing systems for users' travel behaviour, user experience, and relevant social impacts of dockless bike-sharing systems. Our review suggests that the dockless design of bike-sharing systems significantly improves users' experiences at the end of their bike trips. Individuals can instantly switch to a dockless shared bike without the responsibility of returning it back to a designated dock. Additionally, the high flexibility and efficiency of dockless bike-sharing often makes the bike-sharing systems' integration with public transit even tighter than that of traditional public bikes, providing an efficient option for first/last-mile trips. The GPS tracking device embedded in each dockless shared bike enables the unprecedented collection of large-scale riding trajectory data, which allow scholars to analyse people's travel behaviour in new ways. Although many studies have investigated travel satisfaction amongst cyclists, there is a lack of knowledge of the satisfaction with bikeshare trips, including both station-based and dockless bikeshare systems. The availability and usage rates of dockless bike-sharing systems implies that they may seriously impact on individuals' subjective well-being by influencing their satisfaction with their travel experiences, health and social participation, which requires further exploration. The impact of dockless bike-sharing on users' access to services and social activities and the related decreases in social exclusion are also relevant issues about which knowledge is lacking. With the increases in popularity of dockless shared bikes in some cities, issues related to the equity and access and the implications for social exclusion and inequality are also raised.     

Investigating the impacts of weather variability on individual’s daily activity–travel patterns: A comparison between commuters and non-commuters in Sweden

Understanding travel behaviour change under various weather conditions can help analysts and policy makers incorporate the uniqueness of local weather and climate within their policy design, especially given the fact that future climate and weather will become more unpredictable and adverse. Using datasets from the Swedish National Travel Survey and the Swedish Meteorological and Hydrological Institute that spans a period of thirteen years, this study explores the impacts of weather variability on individual activity–travel patterns. In doing so, this study uses an alternative representation of weather from that of directly applying observed weather parameters. Furthermore, this study employs a holistic model structure. The model structure is able to analyse the simultaneous effects of weather on a wide range of interrelated travel behavioural aspects, which has not been investigated in previous weather studies. Structural equation models (SEM) are applied for this purpose. The models for commuters and non-commuters are constructed separately. The analysis results show that the effects of weather can be even more extreme when considering indirect effects from other travel behaviour indicators involved in the decision-making processes. Commuters are shown to be much less sensitive to weather changes than non-commuters. Variation of monthly average temperature is shown to play a more important role in influencing individual travel behaviour than variation of daily temperature relative to its monthly mean, whilst in the short term, individual activity–travel choices are shown to be more sensitive to the daily variation of the relative humidity and wind speed relative to the month mean. Poor visibility and heavy rain are shown to strongly discourage the intention to travel, leading to a reduction in non-work activity duration, travel time and the number of trips on the given day. These findings depict a more comprehensive picture of weather impact compared to previous studies and highlight the importance of considering interdependencies of activity travel indicators when evaluating weather impacts.     

Estimating origin-destination matrices from roadside survey data

The purpose of this study is to develop a valid and efficient method for estimating origin-destination tables from roadside survey data. Roadside surveys, whether conducted by interviews or postcard mailback methods, typically have in common the sampling of trip origin and destination information at survey stations. These survey stations are generally located where roads cross “screenlines,” which are imaginary barriers drawn to intercept the trip types of interest.Such surveys also include counts of traffic volumes, by which the partial origin-destination (O-D) tables obtained at the different stations can be expanded and combined to obtain the complete O-D table which represents travel throughout the entire study area. The procedure used to expand the sample O-D information from the survey stations must recognize and deal appropriately with a number of problems:

• 1.(i) The “double counting” problem: Long-distance trips may pass through more than one survey station location; thus certain trips have the possibility of being sampled and expanded more than once, leading to a potentially serious overrepresentation of long-distance trips in the complete expanded trip table.
• 2.(ii) The “leaky screenline” problem: Some route choices, particularly those using very lightly traveled roads, may miss the survey stations entirely, leading to an underestimation of certain O-D patterns, or to distorted estimates if such sites are arbitrarily coupled with actual nearby station locations.
• 3.(iii) The efficient use of the data: There is a need to adjust expansion factors to compensate for double counting and leaky screenlines. How can this be accomplished such that all of the data obtained at the stations are used without loss of information?
• 4.(iv) The consequences of uncertainty and unknown travel behavior: Since the O-D data and other sampled variables are subject to random error, and since in general the probability of encountering a long-distance trip at some survey stations is affected by traveler route-choice behavior, which is not understood, the sample expansion procedure must rely on the use of erroneous input data and questionable assumptions. The preferred procedure must minimize, rather than amplify, the effects of such input errors.

Here, five alternate methods for expanding roadside survey data in an unbiased manner are proposed and evaluated. In all cases, it is assumed that traveler route choice generally follows the pattern described by Dial's multipath assignment method. All methods are applied to a simple hypothetical network in order to examine their efficiency and error amplification properties. The evaluation of the five methods reveals that their performance properties vary considerably and that no single method is best in all circumstances. A microcomputer program has been provided as a tool to facilitate comparison among methods and to select the most appropriate expansion method for a particular application.     

Bicycling renaissance in North America?: Recent trends and alternative policies to promote bicycling

Over the past two decades, the number of bicycle trips in the United States has doubled. Since 48% of trips by all modes in American cities are shorter than three miles, the potential for further growth in bicycling seems enormous. So far, efforts to promote bicycling have focused on building bike paths and bike lanes. Although necessary, separate cycling facilities must be complemented by a comprehensive program to make all roads bikeable, through both physical adaptations and enforcement of cyclists' right to use the road. It seems likely that cycling will continue to grow in North America, but that its mode share will remain far lower than levels in northern Europe. Bicycling in Canada and especially the United States is impeded by the lack of a tradition of cycling for utilitarian purposes and by the marginal legal, cultural and infrastructure status of cyclists in both countries' automobile-based transport systems. As long as car use remains cheap and transportation policy remains dominated by motoring, bicycles will continue to be used primarily for recreation and not for daily urban travel in North America.     

An analysis of Metro ridership at the station-to-station level in Seoul

While most aggregate studies of transit ridership are conducted at either the stop or the route level, the present study focused on factors affecting Metro ridership in the Seoul metropolitan area at the station-to-station level. The station-to-station analysis made it possible to distinguish the effect of origin factors on Metro ridership from that of destination factors and to cut down the errors caused by the aggregation of travel impedance-related variables. After adopting two types of direct-demand patronage forecasting models, the multiplicative model and the Poisson regression model, the former was found to be superior to the latter because it clearly identified the negative influences of competing modes on Metro ridership. Such results are rarely found with aggregate level analyses. Moreover, the importance of built environment in explaining Metro demand was confirmed by separating built environment variables for origin and destination stations and by differentiating ridership by the time of day. For morning peak hours, the population-related variables of the origin stations played a key role in accounting for Metro ridership, while employment-related variables prevailed in destination stations. In evening peak hours, both employment- and population-related variables were significant in accounting for the Metro ridership at the destination station. This showed that a significant number of people in the Seoul metropolitan area appear to take various non-home-based trips after work, which is consistent with the results from direct household travel surveys.     

A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand

In this paper we analyze demand for cycling using a discrete choice model with latent variables and a discrete heterogeneity distribution for the taste parameters. More specifically, we use a hybrid choice model where latent variables not only enter into utility but also inform assignment to latent classes. Using a discrete choice experiment we analyze the effects of weather (temperature, rain, and snow), cycling time, slope, cycling facilities (bike lanes), and traffic on cycling decisions by members of Cornell University (in an area with cold and snowy winters and hilly topography). We show that cyclists can be separated into two segments based on a latent factor that summarizes cycling skills and experience. Specifically, cyclists with more skills and experience are less affected by adverse weather conditions. By deriving the median of the ratio of the marginal rate of substitution for the two classes, we show that rain deters cyclists with lower skills from bicycling 2.5 times more strongly than those with better cycling skills. The median effects also show that snow is almost 4 times more deterrent to the class of less experienced cyclists. We also model the effect of external restrictions (accidents, crime, mechanical problems) and physical condition as latent factors affecting cycling choices.     

How does our natural and built environment affect the use of bicycle sharing?

Public bicycle-sharing programs (PBSP) are short-term bicycle hire systems. In recent years their popularity has soared. This study examined Brisbane’s CityCycle scheme, the largest PBSP in Australia, and investigated the role of (natural and built) environmental features on usage. The study addressed four research questions: (1) What are the dynamics of PBSP use in terms of travel time, speed, and distance? (2) What is the relationship between PBSP participation and cycling infrastructure? (3) How does land-use affect PBSP usage? (4) How does topography affect PBSP usage? To answer these four questions, the authors analysed large existing datasets on CityCycle usage, land-use, topography, and cycling infrastructure, which were each obtained through multiple sources. Correlation and regression analysis were employed to establish significant relationships amongst variables. It was found that: most users take short trips within the free initial period provided under the CityCycle scheme and do not incur any charges other than for membership; PBSP use is strongly correlated with the length of off-road bikeways near each CityCycle station; CityCycle is more frequently used on weekends for recreational purposes; loop journeys, which are also associated with leisure trips, are popular in Brisbane, especially on weekends; leisure trips are taken at a relatively slower pace than utilitarian trips; during weekdays, a trimodal peak is clearly evident, with PBSP commute trips in the morning and evening peaks and a smaller but significant peak around lunchtime; and users avoid returning CityCycle bicycles to stations located on hilltops. These findings can collectively enhance both the siting and design of PBSP, thereby optimizing investments in sustainable mobility.     

Exploring gendered cycling behaviours within a large-scale behavioural data-set

Analysing over 10 million journeys made by members of London's Cycle Hire Scheme, we find that female customers' usage characteristics are demonstrably different from those of male customers. Usage at weekends and within London's parks characterises women's journeys, whereas for men, a commuting function is more clearly identified. Some of these variations are explained by geo-demographic differences and by an atypical period of usage during the first three months after the scheme's launch. Controlling for each of these variables brings some convergence between men and women. However, many differences are preserved. Studying the spatio-temporal context under which journeys are made, we find that women's journeys are highly spatially structured. Even when making utilitarian cycle trips, routes that involve large, multi-lane roads are comparatively rare, and instead female cyclists preferentially select areas of the city associated with slower traffic streets and with cycle routes slightly offset from major roads.     

Characterising group-cycling journeys using interactive graphics

The group-cycling behaviours of over 16,000 members of the London Cycle Hire Scheme (LCHS), a large public bikeshare system, are identified and analysed. Group journeys are defined as trips made by two or more cyclists together in space and time. Detailed insights into group-cycling behaviour are generated using specifically designed visualization software. We find that in many respects group-cycle journeys fit an expected pattern of discretionary activity: group journeys are more likely at weekends, late evenings and lunchtimes; they generally take place within more pleasant parts of the city; and between individuals apparently known to each other. A separate set of group activity is found, however, that coincides with commuting peaks and that appears to be imposed onto LCHS users by the scheme’s design. Studying the characteristics of individuals making group journeys, we identify a group of less experienced LCHS cyclists that appear to make more spatially extensive journeys than they would do normally while cycling with others; and that female cyclists are more likely to make late evening journeys when cycling in groups. For 20% of group cyclists, the first journey ever made through the LCHS was a group journey; this is particularly surprising since just 9% of all group cyclists’ journeys are group journeys. Moreover, we find that women are very significantly (p < 0.001) overrepresented amongst these ‘first time group cyclists’. Studying the bikeshare cyclists, or bike share ‘friends’, that individuals make ‘first time group journeys’ with, we find a significantly high incidence (p < 0.001) of group journeys being made with friends of the opposite gender, and for a very large proportion (55%) of members these first ever journeys are made with a friend that shares the same postcode. A substantial insight, then, is that group cycling appears to be a means through which early LCHS usage is initiated.     

Passengers’ response to transit fare change: an ex post appraisal using smart card data

Fare change is an effective tool for public transit demand management. An automatic fare collection system not only allows the implementation of complex fare policies, but also provides abundant data for impact analysis of fare change. This study proposes an assessment approach for analyzing the influence when substituting a flat-fare policy with a distance-based fare policy, using smart card data. The method can be used to analyze the impact of fare change on demand, riding distances, as well as price elasticity of demand at different time and distance intervals. Taking the fare change of Beijing Metro implemented in 2014 as a case study, we analyze the change of network demand at various levels, riding distances, and demand elasticity of different distances on weekdays and weekends, using the method established and the smart card data a week before and after the fare change. The policy implication of the fare change was also addressed. The results suggest that the fare change had a significant impact on overall demand, but not so much on riding distances. The greatest sensitivity to fare change is shown by weekend passengers, followed by passengers in the evening weekday peak time, while the morning weekday peak time passengers show little sensitivity. A great variety of passengers’ responses to fare change exists at station level because stations serve different types of land usage or generate trips with distinct purposes at different times. Rising fares can greatly increase revenue, and can shift trips to cycling and walking to a certain extent, but not so much as to mitigate overcrowding at morning peak times. The results are compared with those of the ex ante evaluation that used a stated preference survey, and the comparison illustrates that the price elasticity of demand extracted from the stated preference survey significantly exaggerates passengers’ responses to fare increase.     

Fast-charging station choice behavior among battery electric vehicle users

This study explores how battery electric vehicle users choose where to fast-charge their vehicles from a set of charging stations, as well as the distance by which they are generally willing to detour for fast-charging. The focus is on fast-charging events during trips that include just one fast-charge between origin and destination in Kanagawa Prefecture, Japan. Mixed logit models with and without a threshold effect for detour distance are applied to panel data extracted from a two-year field trial on battery electric vehicle usage in Japan. Findings from the mixed logit model with threshold show that private users are generally willing to detour up to about 1750 m on working days and 750 m on non-working days, while the distance is 500 m for commercial users on both working and non-working days. Users in general prefer to charge at stations requiring a shorter detour and use chargers located at gas stations, and are significantly affected by the remaining charge. Commercial users prefer to charge at stations encountered earlier along their paths, while only private users traveling on working days show such preference and they turn to prefer the stations encountered later when choosing a station in peak hours. Only private users traveling on working days show a strong preference for free charging. Commercial users tend to pay for charging at a station within 500 m detour distance. The fast charging station choice behavior is heterogeneous among users. These findings provide a basis for early planning of a public fast charging infrastructure.     

Crowdsourced data for bicycling research and practice

ABSTRACT

Cities are promoting bicycling for transportation as an antidote to increased traffic congestion, obesity and related health issues, and air pollution. However, both research and practice have been stalled by lack of data on bicycling volumes, safety, infrastructure, and public attitudes. New technologies such as GPS-enabled smartphones, crowdsourcing tools, and social media are changing the potential sources for bicycling data. However, many of the developments are coming from data science and it can be difficult evaluate the strengths and limitations of crowdsourced data. In this narrative review we provide an overview and critique of crowdsourced data that are being used to fill gaps and advance bicycling behaviour and safety knowledge. We assess crowdsourced data used to map ridership (fitness, bike share, and GPS/accelerometer data), assess safety (web-map tools), map infrastructure (OpenStreetMap), and track attitudes (social media). For each category of data, we discuss the challenges and opportunities they offer for researchers and practitioners. Fitness app data can be used to model spatial variation in bicycling ridership volumes, and GPS/accelerometer data offer new potential to characterise route choice and origin-destination of bicycling trips; however, working with these data requires a high level of training in data science. New sources of safety and near miss data can be used to address underreporting and increase predictive capacity but require grassroots promotion and are often best used when combined with official reports. Crowdsourced bicycling infrastructure data can be timely and facilitate comparisons across multiple cities; however, such data must be assessed for consistency in route type labels. Using social media, it is possible to track reactions to bicycle policy and infrastructure changes, yet linking attitudes expressed on social media platforms with broader populations is a challenge. New data present opportunities for improving our understanding of bicycling and supporting decision making towards transportation options that are healthy and safe for all. However, there are challenges, such as who has data access and how data crowdsourced tools are funded, protection of individual privacy, representativeness of data and impact of biased data on equity in decision making, and stakeholder capacity to use data given the requirement for advanced data science skills. If cities are to benefit from these new data, methodological developments and tools and training for end-users will need to track with the momentum of crowdsourced data.     

Metro commuters’ satisfaction in multi-type access and egress transferring groups

This paper considers both the access and egress stages as an entire process to analyze the satisfaction levels of commuters with metro commuter journeys. Based on a survey in Nanjing, China, seven intermodal travel groups are employed as targets for this analysis. The groups include Walk–Metro–Walk, Walk–Metro–Bus, Bike–Metro–Walk, Bike–Metro–Bus, Bus–Metro–Walk, Bus–Metro–Bus and Car–Metro–Walk, which are named according to the modes of transportation that are employed for access and egress trips. Binary logit models are developed for each group to identify the main factors of satisfaction level. The results show that access and egress stages serve important but different roles in the seven groups. Facility service qualities in two stages are the primary factors that affect overall satisfaction. The groups with same access or egress modes have significantly different core factors. Access by bike and bike–metro–transit users are concerned with bike parking safety, whereas bike–metro–walk users value parking spaces near metro stations. With two transfers between bus and metro, transit–metro–transit users indicate that the weak point in the access stage is the crowded spaces on buses. However, transit–metro–walk users value bus on-time performance, which is also valued by groups with metro–bus egress transfers. For egress by walking, commuters that use motorized modes for access are concerned with the egress walking environment, whereas users of non-motorized access modes are more concerned with egress walking spaces. The findings of this study are helpful for policy developments than can improve public satisfaction with commutes by urban metro.     

Roadmap for free-floating bikeshare research and practice in North America

ABSTRACT

The deployment of smartphone-operated, non-station-based bicycle fleets (“dockless” or “free-floating” bikeshare) represents a new generation of bikesharing. Users locate bikes in these free-floating systems using Global Positioning Systems (GPS) and lock bikes in place at their destinations. In this paper, we review current free-floating bikesharing systems in North America and discuss priorities for future research and practice. Since launching in 2017, free-floating bikeshare has expanded rapidly to encompass 200+ systems operating 40,000+ bikes within 150+ cities. In contrast with previous systems, free-floating systems operate almost exclusively using commercial “for-profit” models, amidst concerns of financial sustainability. Governance for these systems is in early stages and can include operating fees, fleet size caps, safety requirements, parking restrictions, data sharing, and equity obligations. We identify research and practice gaps within the themes of usage, equity, sharing resources, business model, and context. While some existing bikesharing literature translates to free-floating systems, novel topics arise due to the ubiquity, fluidity, and business models of these new systems. Systems have numerous obstacles to overcome for long-term sustainability, including barriers common to station-based systems: limited supportive infrastructure, equity, theft or vandalism, and funding. Other unique obstacles arise in free-floating bikeshare around parking, sidewalk right of ways, varied bicycle types, and data sharing. This review offers background in and critical reflection on the rapidly evolving free-floating bikeshare landscape, including priorities for future research and practice. If concerns can be overcome, free-floating bikeshare may provide unprecedented opportunities to bypass congested streets, encourage physical activity, and support urban sustainability.     

Biases in response over time in a seven-day travel diary

Data from multi-day travel or activity diaries might be biased if recording inaccuracies and tendencies for respondents to skip certain types of trips or activities increases (or decreases) from day-to-day over the diary period. One objective of the research reported here is to test for such temporal biases in a seven-day travel diary. A second objective is to calculate correction factors which can be applied to the data in the case that biases are found. The analyses were conducted using regression and analysis-of-variance techniques. The variables investigated included total trips per day, total travel time per day, and trips per day by various modes (such as walking, car driver and car passenger). Results showed that most biases per capita statistics are due to increases over time in the percentage of respondents reporting no travel at all for an entire day. However, even after accounting for this bias by measuring statistics in terms of per mobile person, there remains a decrease over time of about 3.5 percent per day in the reporting of walking trips. This appears to be the main factor in the overall bias of about one percent per day in total trips per mobile person per day. No significant differences were found among population segments in terms of the levels of their biases.     

Factors associated with bicycle ownership and use: a study of six small U.S. cities

As a means of transportation and as a form of physical activity, bicycling generates benefits to the bicyclist as well as to the community as a whole. Bicycling now accounts for less than 1 percent of all trips for all purposes in the U.S., but evidence from other western countries suggests that under the right conditions, bicycling levels can be significantly higher. Indeed, the experiences of some U.S. cities suggest that it is possible to create conditions conducive to higher levels of bicycling even in the U.S. However, the extent to which bicycle investments have contributed to bicycling levels in these communities has not been rigorously assessed. The purpose of this study is to provide a better understanding of the determinants of bicycle ownership and use as a basis for identifying ways to promote bicycling. A cross-sectional study of six cities was designed to test the importance of bicycle infrastructure and other physical environment factors relative to individual factors and social environment factors, using a nested logit model to examine ownership and use decisions jointly. The results show strong effects of individual attitudes and physical and social environment factors on bicycle ownership and use.     

Relationship between proximity to transit and ridership for journey-to-work trips in Chicago

The use of privately owned vehicles (POVs) contributes significantly to US energy consumption (EC) and greenhouse gas emissions (GHGe). Strategies for reducing POV use include shifting trips to other modes, particularly public transit. Choices to use transit are based on characteristics of travelers, their trips, and the quality of competing transportation services. Here we focus on the proximity of rail stations to trip origins/destinations as a factor affecting mode choice for work trips. Using household travel survey data from Chicago, we evaluate the profile of journey-to-work (JTW) trips, assessing mode share and potential for more travelers to use rail. For work trips having the origin/destination as close as 1 mile from rail transit stations, POVs were still the dominant travel mode, capturing as much as 61%, followed by rail use at 14%. This high degree of POV use coupled with the proportion of JTW trips within close proximity to rail stations indicated that at least some of these trips may be candidates for shifting from POV to rail. For example, shifting all work trips with both the origin/destination within 1 mile of commuter rail stations would potentially reduce the energy associated with all work-related POV driving trips by a maximum of 24%. Based on the analysis of trips having the origin and destination closest to train stations, a complete shift in mode from POV to train could exceed CO₂ reduction goals targeted in the Chicago Climate Action Plan. This could occur with current settlement patterns and the use of existing infrastructure. However, changes in traveler behavior and possibly rail operation would be necessary, making policy to motivate this change essential.     

A note on the calculation and calibration of doubly constrained trip distribution models

The use of growth factor models for trip distribution has given way in the past to the use of more complex synthetic models. Nevertheless growth factor models are still used, for example in modelling external trips, in small area studies, in input-output analysis, and in category analysis. In this article a particular growth factor model, the Furness, is examined. Its application and functional form are described together with the method of iteration used in its operation. The expected information statistic is described and interpreted and it is shown that the Furness model predicts a trip distribution which, when compared with observed trips, has the minimum expected information subject to origin and destination constraints. An equivalent entropy maximising derivation is described and the two methods compared to show how the Furness iteration can be used in gravity models with specified deterrence functions. A trip distribution model explicitly incorporating information from observed trips, is then derived.It is suggested that if consistency is to be maintained between iteration, calibration, and the derivation of gravity models, then expected information should be used as the calibration statistic to measure goodness of fit. The importance of consistency in this respect is often overlooked.Lastly, the limitations of the models are discussed and it is suggested that it may be better to use the Furness iteration rather than any other, since it is more fully understood. In particular its ease of calculation makes it suitable for use in small models computed by hand.