Cycling distance and detour extent: Comparative analysis of private and public bikes using city-level bicycle trajectory data

Chung, Jaehoon; Namkung, Ok Stella; Ko, Joonho; Yao, Enjian

doi:10.1016/j.cities.2024.105134

Cities

2024

DOI: 10.1016/j.cities.2024.105134

|View full text |Cite

Cycling distance and detour extent: Comparative analysis of private and public bikes using city-level bicycle trajectory data

Jaehoon Chung,

Ok Stella Namkung,

Joonho Ko

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Evaluation of large‐scale cycling environment by using the trajectory data of dockless shared bicycles: A data‐driven approach

Ni,

Wang,

Chen

et al. 2024

IET Intelligent Trans Sys

View full text Add to dashboard Cite

Cycling is increasingly promoted worldwide, but many urban areas lack satisfactory cycling environments. Assessing these environments is crucial, but existing methods face data challenges for large urban networks. This study proposes a data‐driven framework using dockless shared bicycle data to efficiently evaluate large‐scale cycling environments. First, critical cycling behaviour features that reflect cyclists’ perceptions are identified applying the fuzzy C‐means and random forest model. Then, a distribution‐oriented evaluation method is developed, ensuring the incorporation of cyclist heterogeneity and quantifying the quality differences among road segments by combining statistical analysis with a hierarchical clustering model. The evaluation framework is applied to Yangpu District, Shanghai, using Mobike data covering 114.9 km of cycling roads. Results show that indicators related to speed magnitude and fluctuation are critical, and an experimental study validates the effectiveness of the data‐driven feature extraction method. A minimum trajectory sample size of 260 is required to account for cyclist heterogeneity for one road segment to be evaluated. Further analysis of lower‐performing segments identifies vehicle‐bicycle separation, on‐street parking, and traffic volume as key influencing factors. The rationality of these findings further supports the reliability of the evaluation framework.

show abstract

Evaluation of large‐scale cycling environment by using the trajectory data of dockless shared bicycles: A data‐driven approach

Ni,

Wang,

Chen

et al. 2024

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Cycling distance and detour extent: Comparative analysis of private and public bikes using city-level bicycle trajectory data

Cited by 1 publication

References 29 publications

Evaluation of large‐scale cycling environment by using the trajectory data of dockless shared bicycles: A data‐driven approach

Evaluation of large‐scale cycling environment by using the trajectory data of dockless shared bicycles: A data‐driven approach

Contact Info

Product

Resources

About