Capacity of Bicycle Platoon Flow at Two-Phase Signalized Intersection: a Case Analysis of Xi’an City

Wang, Yonggang; Wei, Gang; Zhu, Xiangwei; Pei, Yulong

doi:10.7307/ptt.v23i3.121

Cited by 7 publications

(5 citation statements)

References 18 publications

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“…One is the capacity under uninterrupted-flow conditions, which is the main research object of this paper. The other is the capacity under interrupted-flow conditions, especially the saturation flow rate or capacity of bicycles and the effect of bicycles on the motorized vehicle capacity at signalized intersections (Allen et al, 1998a(Allen et al, , 1998bRaksuntorn and Khan, 2003;Wang et al, 2011aWang et al, , 2011b.…”

Section: Cycleway Capacitymentioning

confidence: 99%

Estimating cycleway capacity and bicycle equivalent unit for electric bicycles

Jin

Zhou

et al. 2015

Transportation Research Part A: Policy and Practice

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Section: Cycleway Capacitymentioning

confidence: 99%

Estimating cycleway capacity and bicycle equivalent unit for electric bicycles

Jin

Zhou

et al. 2015

Transportation Research Part A: Policy and Practice

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“…According to the Highway Capacity Manual (HCM), capacity represents the maximum sustainable hourly flow rate at which persons or vehicles can be expected to traverse a point or a uniform section of a lane or roadway during a given time period under prevailing roadway, environmental, traffic, and control conditions (HCM, 2010). Previous studies have considered two different conditions: (a) the capacity of uninterrupted-flow bicycle facilities, such as the exclusive and shared bicycle lanes that are physically separated from vehicular lanes and do not have fixed interruptions; and (b) the capacity of interrupted-flow bicycle facilities, such as the on-street bicycle lanes that pass through intersections (Allen, 1998a(Allen, , 1998bRaksuntorn and Khan, 2003;Wang et al, 2011aWang et al, , 2011b. In the HCM, the capacity of uninterrupted-flow bicycle facilities was measured as the flow rate during the most heavily traveled 15 minutes of peak periods, while the capacity of interrupted-flow bicycle facilities was estimated on the basis of the saturation flow rate and the ratio between the effective green time and cycle length at signalized intersections, and the distribution of vehicle headways at unsignalized intersections (HCM, 2000(HCM, , 2010Opiela et al, 1980).…”

Section: Introductionmentioning

confidence: 99%

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

Bai

Liu

Sze

et al. 2021

Transportmetrica A: Transport Science

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The primary objective of this study was to propose a procedure for estimating the capacity of mid-block bicycle lanes considering different types of two-wheeled vehicles. The focus was on the uninterrupted-flow mid-block bicycle lanes on urban streets. Data were collected at five midblock bicycle lanes in Nanjing, China. Composite headway distribution models were developed to identify the headway distributions of e-bikes, e-scooters and bicycles. The headway distribution of the overall two-wheeled vehicles was estimated by aggregating the headway distributions of e-bikes, escooters and bicycles considering their proportions in a bicycle lane. A distribution-free estimation approach was followed to determine the key parameters of the composite headway distribution models.A procedure was then proposed for estimating the capacity of mid-block bicycle lanes with mixed twowheeled traffic. The proposed procedure was validated using field data, and the results suggested that the proposed procedure provides reasonable estimates for the aggregated capacity of mid-block bicycle lanes. The estimated capacity of a mid-block bicycle lane for e-bikes, e-scooters and bicycles was 3,757, 3,804 and 2,791 bicycles/h, respectively, and the aggregated capacity of a mid-block bicycle lane was 3,332 bicycles/h. The bicycle equivalent factor for e-bikes and e-scooters with respect to bicycles was 0.7429 and 0.7337, respectively. The proposed capacity estimation procedure can be directly used for estimating the capacity of mid-block bicycle lanes with varying geometric design characteristics and traffic compositions.

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“…To evaluate the impact of probabilistic yielding behavior on traffic operations, it is crucial to determine traffic capacity and other measurements of performance (MOEs) such as delay and queue length. Nevertheless, in current practice, limited efforts have been made on the evaluation of the impact of probabilistic yielding behavior on traffic operations; the quantified MOEs presented in existing studies were usually based on the assumption of an absolute priority (3)(4)(5), which may not be applicable for evaluating the operation of permitted left-turn signalized intersection with probabilistic priority.…”

mentioning

confidence: 99%

Capacity Modeling of Permitted Left-Turn Signalized Intersections with Probabilistic Priority

Wang

Yang

Tian

et al. 2020

Transportation Research Record

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Probabilistic yielding behavior is commonly observed at permitted left-turn signalized intersections in China. Nevertheless, its impact on traffic capacity has not been explored in existing research. The uniqueness of this traffic operation is that neither left-turn traffic nor through traffic holds an absolute priority. Based on queuing theory, this research developed an analytical capacity model that took into account the probabilistic priority phenomenon at permitted left-turn signals. To validate the developed capacity model, stochastic simulations with different combinations of left-turn traffic yielding rates and through-traffic flow rates were performed. It was found that modeling results from the analytical model precisely matched the stochastic simulation results. In comparison with traditional capacity estimation models that assumed through traffic holds an absolute priority, this research revealed that when through-traffic flow rate is around 1000 vehicles per hour (vph), the capacity of left-turn traffic increased from 233 vph to 536 vph when left-turn traffic yielding rates decrease from 1 to 0.2; whereas when left-turn traffic yielding rates decreased from 0.2 to 0, left-turn capacity increased sharply to 1200 vehicles per hour (vph). In this regard, it is critical to take into account the probabilistic left-turn yield behavior when developing permitted left-turn signal warrants.

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Capacity of Bicycle Platoon Flow at Two-Phase Signalized Intersection: a Case Analysis of Xi’an City

Abstract: ABSTRACT

Cited by 7 publications

References 18 publications

Estimating cycleway capacity and bicycle equivalent unit for electric bicycles

Estimating cycleway capacity and bicycle equivalent unit for electric bicycles

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

Capacity Modeling of Permitted Left-Turn Signalized Intersections with Probabilistic Priority

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