Willingness to Use a Public Bicycle System: An Example in Nanjing City

Feng, Peiyu; Li, Wenquan

doi:10.5038/2375-0901.19.1.6

Cited by 12 publications

(5 citation statements)

References 10 publications

(8 reference statements)

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“…Travel cost, comfort, age and gender also affect the choice for PBSS. A study (Feng and Li 2016) for Nanjing city (China) concluded that socio-economic factors (such as gender, employment status and car ownership) are more important than journey restriction variables in defining the user willingness to use PBSS. Another recent study (Campbell et al 2016) used a stated preference data from Beijing (China) for determining user's preferences for conventional and electric bikes within a PBSS.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Some studies investigate user preferences for the citywide infrastructure of PBSS installed in large cities (Bachand-Marleau et al 2012;Feng and Li 2016), where PBSS infrastructure such as the number of bicycles and distance between the docking stations have been found as the major determinants of use of PBSS. However, limited studies exist that focused on determining the users preferences for use of PBSS for their inter-modal trip, especially with rail.…”

Section: Introductionmentioning

confidence: 99%

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Last-mile travel and bicycle sharing system in small/medium sized cities: user’s preferences investigation using hybrid choice model

Adnan

Altaf

Bellemans

et al. 2018

J Ambient Intell Human Comput

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First and last-mile access to and from public transport stations/stops is a major problem for encouraging public transport use. Bicycle sharing schemes have shown potential to fill this gap. Majority of studies examined the preference for using bicycle sharing schemes for large cities. Consequently, railway operators in the Netherland and Germany have started their own bike sharing schemes. Majority of the studies examined the preferences for using bike share schemes for larger cities. This study analyses the collected stated preference survey data for the use of bicycle sharing scheme for last mile travel, which is recently launched in small/medium sized cities of Belgium. Within this scheme a single docking station is available and users need to return bicycle at the same station. The survey also includes questions on respondents attitudes towards friendliness-to-cycling. The hybrid choice modelling framework is used to investigate preferences of users. Usual explanatory variables such as temperature, rain conditions, distance, rental cost, gender and age are found significant, which confirms the findings of earlier studies. Along with these; last-mile (to home) and an interaction term between rental cost and duration to keep bicycle are found significant, which indicate the negative effects of having a single docking station. Availability of escorting facility from parents/colleague/friends (a more common phenomenon in small/medium cities) also has a negative effect on the use of the examined bike share scheme. Paper also discusses promotional campaigns and marketing efforts according to the obtained results for making such schemes more successful.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Last-mile travel and bicycle sharing system in small/medium sized cities: user’s preferences investigation using hybrid choice model

Adnan

Altaf

Bellemans

et al. 2018

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…Additionally, we addressed the necessity for research into the nonlinear relationship between the subjective and objective dimensions of the cycling environment and its impact on cycling distance. Concerning objective factors, there is a widespread belief that socio-economic indicators are closely linked to cycling distance (Feng & Li, 2016). However, in special situations such as the pandemic period, this correlation may not be significant (Schaefer et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Mainstream literature indicates that objective attributes like land-use elements and street design in the cycling environment significantly impact riding distance (Chen et al, 2022;Feng & Li, 2016;Ferenchak & Marshall, 2021;Kim et al, 2012;Ospina et al, 2020;Wang et al, 2018;Wei & Zhu, 2023). However, most studies predicting riding distance typically concentrate solely on objective cycling environments.…”

Section: Introductionmentioning

confidence: 99%

The nonlinear impact of cycling environment on bicycle distance: A perspective combining objective and perceptual dimensions

Zhang,

2024

JTLU

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Extending cycling distances is crucial for sustainable urban transport development and plays a role in encouraging the shift from motorized vehicles to public transport. However, there is a lack of research examining the combined impacts of both objective and perceived aspects of the cycling environment on cycling distance, and the existence of threshold effects remains unclear. This study uses 2019 cycling data from Shenzhen, China, employing the XGBoost algorithm to uncover the relative importance and thresholds of objective and perceived factors in the cycling environment. The results indicate that population density (24.8%), road network density (15.2%), the proportion of recreational facilities (9.1%), perceived accessibility (8.0%), and comfort (8.6%) hold high relative importance in predicting cycling distance. Also, maintaining road network density between 3 to 6 km/km2 and increasing the population density to exceed 22,000 people/km2 proves effective in extending cycling distances. Land use demonstrates a threshold effect, with cycling distances increasing when the recreational facilities share exceeds 8%, transport facilities share remains below 25%, and commercial facilities share stays below 30%. Perceived metrics exhibit a clear threshold effect. The study identifies that perceived safety indicates a psychological bottleneck in increasing cycling distance. Perceived accessibility is positively correlated with cycling distance when accessibility is at a low level, while comfort shows a positive correlation with cycling distance when comfort is at a high level. These findings can contribute to refining land planning and prioritizing resource allocation for organizations aiming to promote non-motorized travel and design bicycle-friendly environments.

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“…Understanding the BSP consumer profile entails identifying socio-demographic parameters [73]. Gender, age, education, money, and car ownership affect bike-sharing demand.…”

Section: Socio-demographic Impact Factorsmentioning

confidence: 99%

Demand Prediction on Bike Sharing Data Using Regression Analysis Approach

Butt¹,

Danjuma²,

Ilyas³

et al. 2023

JICET

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In order to forecast the need for bike-sharing services, this paper suggests a rule-based regression model. Commuters and tourists alike are taking advantage of public bike sharing programs because of the convenience and low carbon footprint they provide. Used information from the UCI Machine Learning Repository. Repeated cross-validation was used to fine-tune the hyper-parameters of five statistical models. Conditional Inference Tree, K-Nearest Neighbor Analysis, Regularized Random Forest, Classification and Regression Trees, and CUBIST. The predictive accuracy of the regression models was measured by calculating the Root Mean Squared Error, R-Squared, Mean Absolute Error, and Coefficient. For both the Seoul Bike and Capital Bikeshare programs, the rule-based model CUBIST was able to account for 95 and 89% of the Variance (R2), respectively. All models built from the two datasets using WEKA v3.8.6, and are used a variable significance analysis to establish which variables were most crucial. The most important factors in determining the hourly demand for bike rentals are the weather and the time of day.

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Willingness to Use a Public Bicycle System: An Example in Nanjing City

Abstract: Abstract

Cited by 12 publications

References 10 publications

Last-mile travel and bicycle sharing system in small/medium sized cities: user’s preferences investigation using hybrid choice model

Last-mile travel and bicycle sharing system in small/medium sized cities: user’s preferences investigation using hybrid choice model

The nonlinear impact of cycling environment on bicycle distance: A perspective combining objective and perceptual dimensions

Demand Prediction on Bike Sharing Data Using Regression Analysis Approach

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