Bicycle traffic passing through intersections exhibits a dispersion effect that can influence the movements of nearby vehicles. The primary objective of this study was to investigate the dispersion effect in left-turning bicycle traffic at signalized intersections and to evaluate the effect's influence on the capacity of left-turning vehicles. Characteristics of the dispersion effect were investigated in 20 h of video data collected in Nanjing, China. A Poisson model was estimated to evaluate the factors contributing to platoon width of left-turning bicycle traffic. The impacts of platoon width on the capacity and delay of left-turning vehicles also were evaluated. Results showed that several factors, including the number of left-turning electric bicycles (e-bicycles) and conventional bicycles arriving during the red light period and the directional factor, significantly affected the platoon width of left-turning bicycle traffic. Sensitivity analysis results indicated that the platoon was widest when the number of left-turning e-bicycles divided by the number of total left-turning bicycles arriving per cycle was about 60%. An adjustment factor that accounted for the impacts of left-turning bicycles on the capacity of left-turning vehicles was proposed. Increasing the platoon width of left-turning bicycles from three to eight reduced the left-turning vehicle capacity about 19% and increased the capacity of all left-turning traffic around 25%.
Public bicycle acts as a seamless feeder mode in combination with the citywide public transit, as well as a competitor for the inner-city short trips. The primary objective of this study is to address the layout planning of public bicycle system within the attracted scope of a metro station. Based on the land use function, population, and bicycle mode share, bicycle rental stations are divided into three types, namely, the metro station, district station, and resident station, and later the quantity of bicycle facilities in each rental station is estimated. Then, the service stations are selected from these bicycle rental stations to provide the service of periodical bicycle redistribution. An improved immune algorithm is proposed to determine the number and locations of service stations and the optimal route options for the implement of redistributing strategy. Finally, a case study of Nanjing Tianyin Road metro station is conducted to illustrate the proposed model and clarify some of its implementation details.
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