Dispersion Effect in Left-Turning Bicycle Traffic and Its Influence on Capacity of Left-Turning Vehicles at Signalized Intersections

Chen, Jingxu; Wang, Wei; Li, Zhibin; Jiang, Hang; Chen, Xuewu; Zhu, Senlai

doi:10.3141/2468-05

Cited by 23 publications

(8 citation statements)

References 18 publications

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“…Based on the previous description of the PDM, there are three kinds of parameters that should be calibrated, and they are the elastic boundary parameters, the observed parameters, and the calibrated parameters. and in (11) should be fitted for the elastic boundary location. There exist multiple options for the form of the equation, so the curve fitting toolbox in MATLAB was utilized and the most suitable equation was obtained as follows: = 0.1829 + 0.0721 + 3.3657 −(19.93 ) 2 (19) within which the goodness of fit 2 is 0.933.…”

Section: (4) Shrink Forcementioning

confidence: 99%

“…Previous studies paid more attention to the interactions between motorized and nonmotorized vehicles that have temporal or spatial crossing conflicts, such as the conflict between left-turn, nonmotorized vehicles and opposing gostraight motorized vehicles [6] and between go-straight, nonmotorized vehicles and left-turn/right-turn motorized vehicles [7][8][9][10]. Recent studies have shown that the safety and efficiency of these intersections can decline as well due to interactions without any crossing conflict [11,12]. One of the most common interactions is the lateral interaction between nonmotorized and motorized vehicles driving in the same direction (e.g., go-straight, left-turn) because of the dispersion phenomenon of nonmotorized vehicles.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Simulation of Nonmotorized Vehicles’ Dispersion at Mixed Flow Intersections

Liu

Sun

Tian

et al. 2019

Journal of Advanced Transportation

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Interactions between motorized and nonmotorized vehicles have drawn considerable attention from researchers. They are commonly seen at mixed flow intersections where nonmotorized vehicles, without the restriction of lane markers or physical barriers, may disperse into adjacent lanes and thus lead to complex interactions with motorized vehicles. Such a dispersion phenomenon between heterogeneous participants (e-bikes and bicycles as nonmotorized vehicles versus motorized vehicles) is difficult to model. In this paper, we were inspired by the dispersion of charged particles in an electric field and modeled the dispersion phenomenon of go-straight, nonmotorized vehicles at mixed flow intersections accordingly, as it was discovered in this research that these two dispersion phenomena share three underlying commonalities with each other. A novel particle dispersion model (PDM) based on a particle’s movement in an electric field is proposed. The model is calibrated and validated using 1,490 high-definition sets of trajectory data for go-straight, nonmotorized vehicles during 43 cycles at two typical mixed flow intersections. The PDM is compared with the social force model (SFM) on their dispersion characteristics that are used to describe the nonmotorized bicycles’ behavior. The results show that the PDM performs better than the SFM with regard to depicting the dispersion characteristic indices of the nonmotorized vehicles, such as the travel time, the dispersion intensity of heterogeneous nonmotorized vehicles, the sectional dispersion degree, and other dispersion characteristics.

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Section: (4) Shrink Forcementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Nonmotorized Vehicles’ Dispersion at Mixed Flow Intersections

Liu

Sun

Tian

et al. 2019

Journal of Advanced Transportation

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“…Results showed that significant factors affecting the intersection collision severity included the interaction between roadway and approach-control type, the existence of partial crosswalks and bike signs, and the cyclist's gender and age. Chen et al [20] evaluated the impacts of bicycle-vehicle interactions on the capacity of left-turn vehicles at signalized intersections. A capacity-impact model was calibrated based on empirical data in Nanjing, China.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In such situation, bicycle traffic could have an impact on the operation of vehicles and increase vehicle delays within road segment areas. A review of the literature shows that most previous studies focused on evaluating the interactions between bicycles and vehicles within the intersections in urban areas [16][17][18][19][20][21]. However, such model cannot be directly used for the analysis of street sections.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Interference of Bicycle Traffic on Vehicle Operation on Urban Streets with Bike Lanes

Wang

et al. 2017

Journal of Advanced Transportation

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Many urban streets are designed with on-street bike lanes to provide right-of-way for bicycle traffic. However, when bicycle flow is large, extensive passing maneuvers could occupy vehicle lanes and thus cause interferences to vehicle traffic. The primary objective of this study is to evaluate how bicycle traffic affects vehicle operation on urban streets with bike lanes. Data were collected on six street segments in Nanjing, China. The cumulative curves were constructed to extract traffic flow information including individual bicycle and vehicle speeds and aggregated traffic parameters such as flow and density. The results showed that as bicycle density on bike lanes continuously increases faster bicycles may run into vehicle lanes causing considerable reductions in vehicle speeds. A generalized linear model was estimated to predict the vehicle delay. Results showed that vehicle delay increases as bicycle flow and vehicle flow increase. Number of vehicle lanes and width of bike lane also have significant impact on vehicle delay. Findings of the study are helpful to regions around the world in bike infrastructure design in order to improve operations of both bicycles and vehicles.

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“…Xie et al proposed a two‐dimensional car‐following model to investigate the interaction between left‐turning non‐motorized vehicle flow and straight‐ahead in‐vehicle flow at a typical unsignalized intersection. Chen et al examined the dispersion effect in left‐turning bicycle traffic and its influence on the capacity for left‐turning vehicles at signalized intersections. Guo et al divided the conflicts between bicycles and turning vehicles at signalized intersections into multiple stages and modeled the saturation flow rates of right‐turning and left‐turning vehicles in the presence of bicycles at each stage.…”

Section: Introductionmentioning

confidence: 99%

A cellular automata (CA) model for motorized vehicle flows influenced by bicycles along the roadside

Chen

Wei

2016

J of Advced Transportation

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SUMMARYOn many urban low-grade or branch roads, especially in medium or small cities in China, bicyclists and motorists commonly share the non-barrier road surface. Because bicycles are unpredictable and unstable when moving, motorized vehicles must reduce their speed to safely approach and overtake them. In this study, the gradual deceleration process a motorized vehicle undergoes before it passes a bicycle was analyzed. The motorist was assumed to prefer a comfortable deceleration and to select a higher deceleration rate only when the distance to the bicycle was insufficient to reduce the car's speed to the expected value at a comfortable deceleration rate. Cellular automata (CA) simulations were used to reveal the flow characteristics of motorized vehicles reacting to bicycles traveling along the roadside, and the results show that for the general velocities of motorized vehicles and bicycles traveling on urban branch roads, the road capacity for motorized vehicles is not related to the number of bicycles present. However, the average travel time of motorized vehicles is significantly affected by the presence of bicycles when the number of motorized vehicles on the road is small. In addition, motorized vehicles' average travel time is more influenced by disturbances in the flow of motorized vehicles than by bicycles when the number of motorized vehicles on the road is large. Field observations and surveys were used to validate the traffic behaviors and simulation results.

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Dispersion Effect in Left-Turning Bicycle Traffic and Its Influence on Capacity of Left-Turning Vehicles at Signalized Intersections

Cited by 23 publications

References 18 publications

Modeling and Simulation of Nonmotorized Vehicles’ Dispersion at Mixed Flow Intersections

Modeling and Simulation of Nonmotorized Vehicles’ Dispersion at Mixed Flow Intersections

Evaluating the Interference of Bicycle Traffic on Vehicle Operation on Urban Streets with Bike Lanes

A cellular automata (CA) model for motorized vehicle flows influenced by bicycles along the roadside

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