• International Journal of Highway Engineering
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• v.18 no.6
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• pp.69-76
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• 2016

OBJECTIVES : The objective of this study is to propose a capacity analysis methodology for riverside bike-exclusive roads. METHODS : Three steps were performed to develop a methodology to estimate bikeway capacity. First, we reviewed previous studies on the vehicle-road capacity analysis and proposed their applicability to bikeways. Second, two assumptions were made based on the traffic flow characteristics of bikeways: (1) the capacitated state in bikeways occur within a bicycle platoon, and (2) a bicycle platoon consists of more than three bicycles running in close proximity. In addition, it is assumed that the mean time headway of a bicycle platoon represents the characteristics of the platoon. The normality of the mean-time headway of a bicycle platoon calculated using the central limit theorem leads to the development of a method that estimates the riverside bikeway capacity using data collected from two different riverside bike-exclusive roads (Han-river and Anyangcheon). We used a location-fixed video camera to record videos of running bicycles and wrote a special-purpose software program to code the time-headway data from the videos. RESULTS : Time headways from 189 bicycle platoons were analyzed. The estimated mean-time headway of the capacitated bicycle flow is 1.01 s, from which the capacity of the bikeway is found to be 3578 vehicles/h. CONCLUSIONS : The proposed method that estimates bikeway capacity could be applicable to the analysis of short-range congested area rather than planning the number of lanes. In other words, it determines the sections that are temporarily highly congested and proposes appropriate strategies to mitigate the congestion.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.79-90
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• 2019

This study aims to develop time headway distribution models of bicycle traffic flow in a uninterrupted bikeway. The sample data were collected and classified into two groups of traffic volume levels. The lower level traffic volume is defined to be under 8 bicycles per minute, and the higher one is greater or equal to 8 bicycles per minute. The data aggregation interval size was set to be 0.5-second. Four distribution models including normal distribution, negative exponential distribution, shifted negative exponential distribution, and Pearson III distribution were tested, and Chi-square test results shows that the negative exponential distribution and the shifted negative exponential distribution are well fitted to the sample data. Another test results with different sample data also shows the same conclusion.