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.     

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.     

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.     

Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto

Bike Share Toronto is Canada’s second largest public bike share system. It provides a unique case study as it is one of the few bike share programs located in a relatively cold North American setting, yet operates throughout the entire year. Using year-round historical trip data, this study analyzes the factors affecting Toronto’s bike share ridership. A comprehensive spatial analysis provides meaningful insights on the influences of socio-demographic attributes, land use and built environment, as well as different weather measures on bike share ridership. Empirical models also reveal significant effects of road network configuration (intersection density and spatial dispersion of stations) on bike sharing demands. The effect of bike infrastructure (bike lane, paths etc.) is also found to be crucial in increasing bike sharing demand. Temporal changes in bike share trip making behavior were also investigated using a multilevel framework. The study reveals a significant correlation between temperature, land use and bike share trip activity. The findings of the paper can be translated to guidelines with the aim of increasing bike share activity in urban centers.     

Exploring the impact of walk–bike infrastructure,safety perception,and built-environment on active transportation mode choice: a random parameter model using New York City commuter data

This study estimates a random parameter (mixed) logit model for active transportation (walk and bicycle) choices for work trips in the New York City (using 2010–2011 Regional Household Travel Survey Data). We explored the effects of traffic safety, walk–bike network facilities, and land use attributes on walk and bicycle mode choice decision in the New York City for home-to-work commute. Applying the flexible econometric structure of random parameter models, we capture the heterogeneity in the decision making process and simulate scenarios considering improvement in walk–bike infrastructure such as sidewalk width and length of bike lane. Our results indicate that increasing sidewalk width, total length of bike lane, and proportion of protected bike lane will increase the likelihood of more people taking active transportation mode This suggests that the local authorities and planning agencies to invest more on building and maintaining the infrastructure for pedestrians. Further, improvement in traffic safety by reducing traffic crashes involving pedestrians and bicyclists, will increase the likelihood of taking active transportation modes. Our results also show positive correlation between number of non-motorized trips by the other family members and the likelihood to choose active transportation mode. The model would be an essential tool to estimate the impact of improving traffic safety and walk–bike infrastructure which will assist in investment decision making.     

Modelling the potential for cycling in access trips to bus,train and metro in Rio de Janeiro

Most studies that address the integration of cycling and public transport (PT) focus on developed countries and deal with multi-modal bicycle-train trips. Little is known about the integration of cycling and other main modes such as bus and metro, especially in developing countries, where entirely different socio-economic and trip making conditions prevail. The aim of this study is to model the propensity of current PT users to shift to the bicycle in access trips to bus stops, train and metro stations in Rio de Janeiro, Brazil. Interviews were conducted to collect data on the socio-economic characteristics of the interviewee, trip and spatial characteristics and self-reported barriers and motivators for bicycle use. Two binary logit models were estimated to predict the main factors affecting the propensity to use a bicycle as feeder mode to PT. The results show that socio-economic characteristics as well as barriers and motivators are important factors to explain propensity for bike and ride. The barriers’ model reveals that personal constraints, living too close to the PT boarding point, current parking conditions and public safety play a role. For the motivators’ model, changing home location, owning a bicycle, implementation of cycle ways and improvement in parking conditions are explanatory. Policy recommendations are formulated to increase bicycle ownership and improve cycling infrastructure.     

Built environment effects on the integration of dockless bike-sharing and the metro

Bike-sharing provides a convenient feeder mode for connecting to a metro and is believed to be an efficient way to solve the first- and last-mile problem. Despite the increasing attention paid on the use of bike-sharing, few studies have investigated how built environment factors affect the integrated use of dockless bike-sharing (DBS) and the metro. Using data from one of the largest DBS operators in China (Ofo), this paper employed a series of negative binomial regressions to examine the effect of the built environment on the integrated use of DBS and the metro, using Shenzhen as a case study. The findings show that mixed land use is positively related to integrated use. Residential areas have higher access-integrated rates during the morning peak hours, while industrial areas are associated with more integrated uses, connecting factories and metro stations. Furthermore, parks and public squares encourage both access- and egress-integrated use during peak times. Transportation facility features, including bus stops and dedicated bike lanes, are positively related to integrated use, while areas with dense metro distribution and main streets with many intersections are negatively related. Transfer distance also plays a crucial and negative role in integrated use. In addition, metro stations that are closer to the city center with a higher number of passengers are more likely to be integrated with bike-sharing. These findings can be used to collectively facilitate a connection between cycling and metro transit by creating a bicycle-friendly environment.     

Using the sustainable modified TAM and TPB to analyze the effects of perceived green value on loyalty to a public bike system

This article explores the effects of perceived green value, perceived green usefulness, perceived pleasure to use, subjective norms and perceived behavioral control on green loyalty to a public bike system. The mediators between perceived green value and green loyalty and a moderator of general attitude toward protecting the natural environment are also discussed. The aim of this research was to understand how to establish green loyalty via the other dimensions based on the sustainable modified technology acceptance model (modified TAM), the theory of planned behavior (TPB), and a moderator. The findings reveal that perceived pleasure to use and subjective norms have the strongest power to influence loyalty for both users and non-users. The implications of this finding are that fun in people’s lives has a strong influence on sustainable continuous use of public bikes, and that subjective norms are more effective for non-users. In addition, environmental attitude has stronger moderating effects for non-users than for users on perceived green usefulness, perceived pleasure and subjective norms. Therefore, governmental policies should promote the attitude of protecting the natural environment, perceptions of pleasure, and subjective norms so as to increase green loyalty to public bike-sharing.     

Promoting bike-and-ride: The Dutch experience

The number of policy initiatives to promote the use of bike-and-ride, or the combined use of bicycle and public transport for one trip, has grown considerably over the past decade as part of the search for more sustainable transport solutions. This paper discusses the experiences with, and impacts of, such initiatives in the Netherlands. The Dutch measures to promote bicycle use in access trips have been generally successful. A country-wide program to upgrade regular and secure bicycle parking at train stations has led to an increase in user satisfaction and a growth in bicycles parked at stations. Smaller programs to stimulate the combined use of bike-and-bus have resulted in an increase in bicycle use, bus use, and share of infrequent bus passengers. Bicycle lockers at bus stops are hardly used by bus passengers, due in part to the dominance of students among bus users as well as the relatively high price of lockers in comparison to the value of bicycles used for access trips. Measures to promote the use of the bicycle in egress trips have met with more varying results. Projects to introduce leasing bicycles for egress trips have failed to attract passengers, for both train and bus services. In contrast, the introduction of flexible rental bicycles at train stations has resulted in a small reduction in car use, growth in train trips, and growth in bicycle use for non-recurrent trips. The Dutch experiences suggest some lessons for promoting bike-and-ride in countries and cities with a less well-developed bicycle infrastructure.     

The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets

The station-free sharing bike is a new sharing traffic mode that has been deployed in a large scale in China in the early 2017. Without docking stations, this system allows the sharing bike to be parked in any proper places. This study aimed to develop a dynamic demand forecasting model for station-free bike sharing using the deep learning approach. The spatial and temporal analyses were first conducted to investigate the mobility pattern of the station-free bike sharing. The result indicates the imbalanced spatial and temporal demand of bike sharing trips. The long short-term memory neural networks (LSTM NNs) were then developed to predict the bike sharing trip production and attraction at TAZ for different time intervals, including the 10-min, 15-min, 20-min and 30-min intervals. The validation results suggested that the developed LSTM NNs have reasonable good prediction accuracy in trip productions and attractions for different time intervals. The statistical models and recently developed machine learning methods were also developed to benchmark the LSTM NN. The comparison results suggested that the LSTM NNs provide better prediction accuracy than both conventional statistical models and advanced machine learning methods for different time intervals. The developed LSTM NNs can be used to predict the gap between the inflow and outflow of the sharing bike trips at a TAZ, which provide useful information for rebalancing the sharing bike in the system.     

Modelling the impact of weather conditions on active transportation travel behaviour

Three weather sensitive models are used to explore the relationship between weather and home-based work trips within the City of Toronto, focusing on active modes of transportation. The data are restricted to non-captive commuters who have the option of selecting among five basic modes of auto driver, auto passenger, transit, bike and walk. Daily trip rates in various weather conditions are assessed. Overall, the results confirm that impact of weather on active modes of transportation is significant enough to deserve attention at the research, data collection and planning levels.     

Measuring the impacts of weather variability on home-based trip chaining behaviour: a focus on spatial heterogeneity

Using the 2011 Swedish national travel survey data, this paper explores the influence of weather characteristics on individuals’ home-based trip chaining complexity. A series of panel mixed ordered Probit models are estimated to examine the influence of individual/household social demographics, land use characteristics, and weather characteristics on individuals’ home-based trip chaining complexity. A thermal index, the universal thermal climate index (UTCI), is used in this study instead of using directly measured weather variables in order to better approximate the effects of the thermal environment. The effects of UTCI are segmented into different seasons to account for the seasonal difference of UTCI effects. Moreover, a spatial expansion method is applied to allow the impacts of UTCI to vary across geographical locations, as individuals in different regions have different weather/climate adaptions. The effects of weather are examined in subsistence, routine, and discretionary trip chains. The results reveal that the ‘ground covered with snow’ condition is the most influential factor on the number of trips chained per trip chain among all other weather factors. The variation of UTCI significantly influences trip chaining complexity in autumn but not in spring and winter. The routine trip chains are found to be most elastic towards the variation of UTCI. The marginal effects of UTCI on the expected number of trips per routine trip chain have considerable spatial variations, while these spatial trends of UTCI effects are found to be not consistent over seasons.     

Making Cycling Irresistible: Lessons from The Netherlands,Denmark and Germany

Abstract

This article shows how the Netherlands, Denmark and Germany have made bicycling a safe, convenient and practical way to get around their cities. The analysis relies on national aggregate data as well as case studies of large and small cities in each country. The key to achieving high levels of cycling appears to be the provision of separate cycling facilities along heavily travelled roads and at intersections, combined with traffic calming of most residential neighbourhoods. Extensive cycling rights of way in the Netherlands, Denmark and Germany are complemented by ample bike parking, full integration with public transport, comprehensive traffic education and training of both cyclists and motorists, and a wide range of promotional events intended to generate enthusiasm and wide public support for cycling. In addition to their many pro‐bike policies and programmes, the Netherlands, Denmark and Germany make driving expensive as well as inconvenient in central cities through a host of taxes and restrictions on car ownership, use and parking. Moreover, strict land‐use policies foster compact, mixed‐use developments that generate shorter and thus more bikeable trips. It is the coordinated implementation of this multi‐faceted, mutually reinforcing set of policies that best explains the success of these three countries in promoting cycling. For comparison, the article portrays the marginal status of cycling in the UK and the USA, where only about 1% of trips are by bike.     

Analysis of light rail rider travel behavior: Impacts of individual,built environment,and crime characteristics on transit access

This paper analyzes factors that influence the mode choice for trips between home and light rail stations, an often neglected part of a person’s trip making behavior. This is important for transit planning, demand modeling, and transit oriented development. Using transit survey data describing St. Louis MetroLink riders in the United States, this study found that some of the factors associated with increased shares of walking relative to other modes were full-time student status, higher income transit riders, and trips made during the evening. It was also found that crime at stations had an impact. In particular, crime made female transit riders more likely to be picked-up/dropped-off at the station. Females are more likely to be picked-up or dropped-off at night. Bus availability and convenience showed that transit riders that have a direct bus connection to a light rail station were more likely to use the bus. Private vehicle availability was strongly associated with increased probability of drive and park, when connecting to light rail.     

Using structural equations modeling to unravel the influence of land use patterns on travel behavior of workers in Montreal

This paper addresses the relations between travel behavior and land use patterns using a Structural Equations Modeling (SEM) framework. The proposed model structure draws on two earlier models developed for Lisbon and Seattle which show significant effects of land use patterns on travel behavior. The travel behavior variables included here are multifaceted including commuting distance, car ownership, the amount of mobility by mode (car, transit and non-motorized modes), both in terms of total kilometers travelled and number of trips. The model also includes a travel scheduling variable, which is the total time spent between the first and last trips to reflect daily constraints in time allocation and travel.The modeled land use variables measure the levels of urban concentration and density, diversity, both in terms of types of uses and the mix between jobs and inhabitants/residents, the transport supply levels, transit and road infrastructure, and accessibility indicators. The land use patterns are described both at the residence and employment zones of each individual included in the model by using a factor analysis technique as a data reduction and multicollinearity elimination technique. In order to explicitly account for self selection bias the land use variables are explicitly modeled as functions of socioeconomic attributes of individuals and their households.The results obtained show that people with different socioeconomic characteristics tend to work and live in places of substantially different urban environments. But besides these socioeconomic self-selection effects, land use variables significantly affect travel behavior. More precisely the effects of land use are in great part passed thru variables describing long term decisions like commuting distance, and car ownership. These results point to similar conclusions from the models developed for Lisbon and Seattle and thus give weight to the use of land use policies as tools for changing travel behavior.     

Effect of environmental awareness on electric bicycle users’ mode choices

Travellers’ environmental awareness can affect their mode choices. The primary objective of this study is to identify the effect of electric bicycle (e-bike) users’ environmental awareness on their mode choice when the use of e-bikes is prohibited in urban areas in China. The data were collected via a questionnaire survey administered at ten locations in Nanjing, China. Using mixed multinomial logit (MMNL) models, we examined the relationship between the e-bike users’ mode choice and their environmental awareness, combined with socioeconomic and demographic characteristics and trip attributes. The results show that the level of environmental awareness, gender, age, education, income, the ownership of car and conventional bike, and trip distance affect e-bike users’ choices significantly. Those with a high level of environmental awareness are more likely to choose zero-emission transport modes. A stratified analysis reveals that the effect of environmental awareness is associated with their original transport mode choice prior to their use of the e-bike. With a high level of environmental awareness, original car users tend to opt for moderate- or zero-emission modes; original bus and metro users incline to choose a zero-emission mode or their original mode; and few original cyclists and walkers favour moderate- or high-emission modes. The results of the current study provide transport authorities with insights to establish sustainable urban transportation management policies and strategies to increase the share of zero- and low-emission transport modes.     

Impact of weather on urban transit ridership

Utilizing daily ridership data, literature has shown that adverse weather conditions have a negative impact on transit ridership and in turn, result in revenue loss for the transit agencies. This paper extends this discussion by using more detailed hourly ridership data to model the weather effects. For this purpose, the daily and hourly subway ridership from New York City Transit for the years 2010–2011 is utilized. The paper compares the weather impacts on ridership based on day of week and time of day combinations and further demonstrates that the weather’s impact on transit ridership varies based on the time period and location. The separation of ridership models based on time of day provides a deeper understanding of the relationship between trip purpose and weather for transit riders. The paper investigates the role of station characteristics such as weather protection, accessibility, proximity and the connecting bus services by developing models based on station types. The findings indicate substantial differences in the extent to which the daily and hourly models and the individual weather elements are able to explain the ridership variability and travel behavior of transit riders. By utilizing the time of day and station based models, the paper demonstrates the potential sources of weather impact on transit infrastructure, transit service and trip characteristics. The results suggest the development of specific policy measures which can help the transit agencies to mitigate the ridership differences due to adverse weather conditions.     

Incorporating spatial interactions in zero-inflated negative binomial models for freight trip generation

This paper formulates a spatial autoregressive zero-inflated negative binomial model for freight trip productions and attractions. The model captures the following freight trip characteristics: count data type, positive trip rates, overdispersion, zero-inflation, and spatial autocorrelation. The spatial autoregressive structure is applied in the negative binomial part of the models to obtain unbiased estimates of the effects of different regressors. Further, we estimate parameters using the full information maximum likelihood estimator. We perform empirical analysis with an establishment based freight survey conducted in Chennai. Separate models are estimated for trips generated by motorised two-wheelers and three-wheelers, and pickups besides an aggregate model. Spatial variables such as road density and indicator of geolocation are insignificant in all the models. In contrast, the spatial autocorrelation is significant in all of the models except for the freight trips attracted and produced by pickups. From a policy standpoint, the elasticity results show the importance of considering spatial autocorrelation. We also highlight the bias due to aggregation of vehicle classes, based on the elasticities.