An improved multi-value cellular automata model for heterogeneous bicycle traffic flow

Jin, Sheng; Qu, Xiaobo; Cheng, Xu; Ma, Dongfang; Wang, Dianhai

doi:10.1016/j.physleta.2015.07.031

Cited by 42 publications

(25 citation statements)

References 29 publications

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“…For instance, Chowdhury et al [19] developed the Symmetric Two-lane Cellular Automata (STCA) model, which was later expanded by Pederson and Ruhoff [20] to the multilane case. CA models were also used to mimic heterogeneous traffic flow [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Characteristic Analysis of Mixed Traffic Flow of Regular and Autonomous Vehicles Using Cellular Automata

Liu

Guo

Taplin

et al. 2017

Journal of Advanced Transportation

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The technology of autonomous vehicles is expected to revolutionize the operation of road transport systems. The penetration rate of autonomous vehicles will be low at the early stage of their deployment. It is a challenge to explore the effects of autonomous vehicles and their penetration on heterogeneous traffic flow dynamics. This paper aims to investigate this issue. An improved cellular automaton was employed as the modeling platform for our study. In particular, two sets of rules for lane changing were designed to address mild and aggressive lane changing behavior. With extensive simulation studies, we obtained some promising results. First, the introduction of autonomous vehicles to road traffic could considerably improve traffic flow, particularly the road capacity and free-flow speed. And the level of improvement increases with the penetration rate. Second, the lane-changing frequency between neighboring lanes evolves with traffic density along a fundamental-diagram-like curve. Third, the impacts of autonomous vehicles on the collective traffic flow characteristics are mainly related to their smart maneuvers in lane changing and car following, and it seems that the car-following impact is more pronounced.

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Section: Introductionmentioning

confidence: 99%

Characteristic Analysis of Mixed Traffic Flow of Regular and Autonomous Vehicles Using Cellular Automata

Liu

Guo

Taplin

et al. 2017

Journal of Advanced Transportation

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“…Based on the above facts, in this work, we use a bicycle cell with the size of 2 m × 1 m, which has also been used in many other CA models (Jin, Qu, Xu, et al, ; Zhang et al., ; Zhao et al., ), and they obtained good simulation results. The size of a cell of a bicycle path is set to 2 × L , where L is the number of bicycle lanes.…”

Section: Model Developmentmentioning

confidence: 99%

“…Jia, Li, Jiang, and Gao () considered differences among bicycles in the EBCA and divided them into two types (fast and slow) based on their maximum speeds. Jin, Qu, Xu, Ma, and Wang () developed an improved EBCA by considering higher maximum speeds of the EBs. The model was calibrated using the field data.…”

Section: Introductionmentioning

confidence: 99%

Lane width‐based cellular automata model for mixed bicycle traffic flow

Jin

Cheng

et al. 2019

Computer aided Civil Eng

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In this paper, an improved extended Burgers cellular automata (EBCA) model for heterogeneous bicycle traffic flow is proposed. The compressibility of bicycle traffic flow is considered as a probability, which is related to the bicycle path width. The proposed improved EBCA model is calibrated and validated using the field data collected from three sites in Hangzhou, China. The proposed model is compared with the original EBCA model that does not consider compressibility. The comparison results show that our improved model can simulate heterogeneous bicycle traffic flow better. Moreover, the proposed model overcomes the shortcoming of the other BCA models that only specific path widths can be simulated directly when lane width is determined. Lastly, the EBCA model performance in the congested regions is improved significantly.

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“…Setp4: Update position: x n (t + 1)=x n (t)+ v n . The bicycle's behavior is similar with pedestrians, but it has uniform behavior, such as s-shaped trajectory, unsteadiness, and so on [8,9].…”

Section: Updating Rules For Pedestrians Bicycles and Carsmentioning

confidence: 99%

An improved multi-value cellular automata model for mixed bicycle traffic flow on campus

Li¹,

Lan²

2017

Proceedings of the 2nd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 20

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Firstly, this paper analyzes the defaults of traffic model applying in campus road in China, such as lacking simulation of vehicles, pedestrians and bicycles under mixed traffic flow. Secondly, the paper proposes an improved multi-value cellular automata model for revealing complex traffic phenomena of pedestrian-bicycle, pedestrian-vehicle and bicycle-vehicle mixed traffic flow. The model includes different forward rules and drift rules for different traffic types under campus roads. Thirdly, considering the mutual interference among different traffic types, it is named different interaction behavior. Finally, the difference of the degree of interaction interference among different traffic types is simulated and analyzed. Through qualitative analysis on traffic fundamental diagram, the complex mixed traffic flow phenomena is simulated by this model successfully, such as overtaking, drift, interaction, free flow, synchronized flow, congestion flow, slow traffic on the right side, etc. Therefore, this research is not only have a profound theoretical and practical value for campus security, but also have the function of reference and guidance for urban mixed traffic network.

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An improved multi-value cellular automata model for heterogeneous bicycle traffic flow

Cited by 42 publications

References 29 publications

Characteristic Analysis of Mixed Traffic Flow of Regular and Autonomous Vehicles Using Cellular Automata

Characteristic Analysis of Mixed Traffic Flow of Regular and Autonomous Vehicles Using Cellular Automata

Lane width‐based cellular automata model for mixed bicycle traffic flow

An improved multi-value cellular automata model for mixed bicycle traffic flow on campus

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