A Review on Bike-Sharing: the Factors Affecting Bike-Sharing Demand

This paper discusses and illustrates the main issues involved in the estimation an dapplication of mixed Revealed Preference-Stated Preference (RP-SP) models: Section 2 provides background on the theoretical framework of the integrated estimation approach, the structure of the model and the specification of the joint utility function. This section includes a statistical derivation of the "tree logit" estimator of the joint likelihood function. Section 3 describes a case study of the tree logit estimation method. This case study was performed on the same data set used previously by Morikawa, meaning that a direct comparison can be made of the logit and probit estimation techniques. Section 4 presents a second case study, a fairly typical case where stated preference data is used to supplement a revealed preference model in order to add into the model a mode choice alternative which does not yet exist. Section 5 provides a brief summary and conclusions regarding the methods and results described in the paper. (A)

Models using mixed stated-preference and revealed-preference information.

Bike-sharing is one of the fastest growing new modes of transport in the world, with more and more schemes opening every year. This paper examines the trends in a bike-sharing scheme that has been in operation in Cork since 2014. While many studies exist on how bike-sharing schemes are changing mobility in cities across the globe, few studies have looked at the dynamics of these schemes in smaller cities. One of the motivations in looking at a small city like Cork is to determine if smaller cites derive benefits from bike-sharing schemes and can bike-sharing schemes provide a prominent role in these cities. This research found that in a small, compact city like Cork, average trip times recorded are short with regular uses displaying habitual trip patterns. This includes using the same bike stations and following similar routes on a daily or weekly basis. The findings also suggest weather has an impact upon usage, with longer trips more likely during better weather conditions. The findings of the paper provide insights to the dynamics of usage of a smaller bike-sharing scheme and results on how bike-sharing is offering citizens a new transport alternative.

/pdf/examining-usage-patterns-of-a-bike-sharing-scheme-in-a-49egf3oxga.pdf

Examining usage patterns of a bike-sharing scheme in a medium sized city

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.

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

The primary objective of this study was to investigate the bikesharing travel patterns and trip purposes by combining smart card data and online point of interests (POIs). A large-scale smart card trip data was collected from the bikesharing system in New York City. The POIs surrounding each station were obtained from Google Places API. K-means clustering analysis was first applied to divide bikesharing stations into five types based on their surrounding POIs. The Latent Dirichlet Allocation (LDA) analysis was then conducted to discover the hidden bikesharing travel patterns and trip purposes using the identified station types and smart card data. The performance of the LDA models with and without POI data was compared to identify whether the POI data should be used. Finally, a practical application of the proposed methods in bikesharing planning and operation was discussed. The result of comparative analyses verified the importance of POI data in exploring bikesharing travel patterns and trip purposes. The results of LDA model showed that the most prevalent travel purpose in New York City is taking public bike for eating, followed by shopping and transferring to other public transit systems. In addition, the result also suggested that people living around the bikesharing stations are more likely to transfer to other commuting tools on the morning peak and ride for home after work. The proposed methods can be used to provide useful guidance and suggestions for transportation agency to develop strategies and regulation that aim at improving the operations of bikesharing systems.

Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests

https://www.trafikdage.dk/papers_2014/262_CarloPrato.pdf

Intentions to use bike-sharing for holiday cycling: An application of the Theory of Planned Behavior

Inertia measures the effect that experiences in previous periods have on the current choice. As such it measures the tendency of sticking with the past choice or the disposition to change, when some alternative becomes particularly appealing. At the same time new situations force individuals to rethink about their choice and new preferences are formed. A learning process begins that release the effect of inertia in the current choice. In this paper the authors use a mixed dataset of revealed preference (RP)-stated preference (SP) to study the effect of inertia between RP and SP observations and to study if inertia is stable along the SP experiments. Inertia has extensively been studied with panel dataset but only few works used RP/SP dataset. In this paper the authors extend these previous works in several ways. The authors test and compare several ways of measuring inertia, including measures that have been proposed to test inertia in both short and long RP panel dataset. The authors explore some new measures of inertia to test for the effect of learning along the SP experiment and we disentangle this later effect from the pure inertia effect. A mixed logit model is used that allows us to account for both systematic and random variation in the inertia effect and for correlations among RP and SP observations. Finally the authors explore the relation between the utility specification (especially in the SP dataset) and the role of the inertia in explaining current choices.

Accounting for Inertia in Modal Choices: Some New Evidence Using RP/SP Data Set

The present study investigates how social influence and social interactions can affect the adoption of new technologies, using stated preference (SP) survey data combined with an “accelerated reality” experience of social interaction among the respondents. Specifically, the intention to use a pro-environmental transport mode (the bike sharing) during a public transport strike within a cohort of students has been analysed. Previous studies have modelled social influence effects using SP data by providing a hypothetical scenario with simulated interactions or information about social conformity processes (i.e. social adoption) during the survey. In our paper, in addition to the impact of assumed social norms, the effect of live/real social interactions is included in the survey. SP survey is developed to investigate the effect of Level-of-Service attributes on the hypothetical choices in the scenario of a public transport strike. Besides the pre-defined attributes characterising the alternatives in the SP design, the survey includes techniques to acquire information on conformity and social interactions. Specifically, the interviewees undertake a before and after stated preference experiment (SP1 and SP2), with a period of group discussion in between the two parts. This SP experiment involves different cognitive and interpersonal mechanisms, such as the functional information exchange on benefits and drawbacks of cycling and bike sharing. The aim is to establish whether hypothetical scenarios of social conformity are different from real/live social interactions and whether these social influence processes actually affect the individuals' mode choice. A joint SP1/SP2 mixed logit (ML) model has been estimated to explore the choice behaviour of individuals and allows us to incorporate the inertia/propensity to change behaviour between SP1 and SP2. Moreover, considering the “Reflexive Layers of Influence” (RLI) framework, the processes generated by social interactions (diffusion, translation and reflexivity) are measured and incorporated in the model. We finally show the effect of these social influence variables on the goodness-of-fit of the models and choice simulation for prediction. We also draw conclusions about the value of such enhanced choice models in understanding and predicting the impacts of social interactions on choice behaviour in the context of new transport technologies.

/pdf/the-effect-of-social-influence-and-social-interactions-on-533hxekpjl.pdf

The effect of social influence and social interactions on the adoption of a new technology: The use of bike sharing in a student population

Inertia is related with effect that experiences in previous periods may have on the current choice. In particular, it has to do with the tendency to stick with the past choice even when another alternative becomes more appealing. As new situations force individuals to rethink about their choices new preferences may be formed. Thus a learning process begins that relaxes the effect of inertia in the current choice. In this paper we use a mixed dataset of revealed preference (RP)-stated preference (SP) to study the effect of inertia between RP and SP observations and to study if the inertia effect is stable along the SP experiments. Inertia has been studied more extensively with panel datasets, but few investigations have used RP/SP datasets. In this paper we extend previous work in several ways. We test and compare several ways of measuring inertia, including measures that have been proposed for both short and long RP panel datasets. We also explore new measures of inertia to test for the effect of “learning” (in the sense of acquiring experience or getting more familiar with) along the SP experiment and we disentangle this effect from the pure inertia effect. A mixed logit model is used that allows us to account for both systematic and random taste variations in the inertia effect and for correlations among RP and SP observations. Finally we explore the relation between the utility specification (especially in the SP dataset) and the role of inertia in explaining current choices.

Francesco Manca

Papers

Intentions to use bike-sharing for holiday cycling: An application of the Theory of Planned Behavior

Accounting for Inertia in Modal Choices: Some New Evidence Using RP/SP Data Set

The effect of social influence and social interactions on the adoption of a new technology: The use of bike sharing in a student population

Accounting for inertia in modal choices: some new evidence using a RP/SP dataset

Marble protection: An inorganic electrokinetic approach