Capacity estimation of midblock bike lanes with mixed two-wheeled traffic

ABSTRACT

The primary objectives of this study were to propose and validate a procedure for estimating the capacity of midblock bike lanes by taking into account the characteristics of three types of two-wheeled vehicle. The focus was on uninterrupted-flow midblock bike lanes on urban streets.We developed composite headway distribution models to identify the individual headway distributions of different types of two-wheeled vehicles, which were then aggregated to estimate the overall headway distribution based on their proportions in one lane. A distribution-free estimation approach was used to determine the key parameters of the composite headway distribution models. The proposed capacity estimation method was validated against field data which were collected at seven midblock bike lanes in Nanjing, China. Results suggest that the proposed procedure provides reasonable outcomes and can be used to estimate the capacities of midblock bike lanes with varying geometric design characteristics and traffic compositions.

KEYWORDS:

• Bike
• electric bike
• bike lane
• capacity
• composite headway distribution

Acknowledgements

The study presented in this paper is supported by the National Natural Science Foundation of China (Project # 51925801), the China Postdoctoral Science Foundation (Project # 2019T120378) and the Natural Science Foundation of Jiangsu Province (Project # BK20180397). The authors would like to thank the National Natural Science Foundation of China and China Postdoctoral Science Foundation for supporting this study. The authors also would like to thank the graduate research assistants at the School of Transportation at Southeast University for the assistance in data collection.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The study presented in this paper is supported by the National Natural Science Foundation of China [grant number 51925801], the China Postdoctoral Science Foundation [grant number 2019T120378] and the Natural Science Foundation of Jiangsu Province [grant number BK20180397].