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

Li, Bi-Xiang; Lan, Fang

doi:10.2991/eeeis-16.2017.107

Cited by 5 publications

(8 citation statements)

References 14 publications

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“…Previously, BCA models were proposed for highway traffic. Recent attempts have included BCA models purported to represent bicycle flow [12,13] adapted for the unobvious car-following and lane-changing behavior in bicycle traffic. In order to make a comparison with the NS CA model, the M-CA model for mixed bicycle flow is improved upon in this paper.…”

Section: M-ca Modelmentioning

confidence: 99%

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Modeling Mixed Bicycle Traffic Flow: A Comparative Study on the Cellular Automata Approach

Zhou

Jin

et al. 2015

Discrete Dynamics in Nature and Society

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Simulation, as a powerful tool for evaluating transportation systems, has been widely used in transportation planning, management, and operations. Most of the simulation models are focused on motorized vehicles, and the modeling of nonmotorized vehicles is ignored. The cellular automata (CA) model is a very important simulation approach and is widely used for motorized vehicle traffic. The Nagel-Schreckenberg (NS) CA model and the multivalue CA (M-CA) model are two categories of CA model that have been used in previous studies on bicycle traffic flow. This paper improves on these two CA models and also compares their characteristics. It introduces a two-lane NS CA model and M-CA model for both regular bicycles (RBs) and electric bicycles (EBs). In the research for this paper, many cases, featuring different values for the slowing down probability, lane-changing probability, and proportion of EBs, were simulated, while the fundamental diagrams and capacities of the proposed models were analyzed and compared between the two models. Field data were collected for the evaluation of the two models. The results show that the M-CA model exhibits more stable performance than the two-lane NS model and provides results that are closer to real bicycle traffic.

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Section: M-ca Modelmentioning

confidence: 99%

“…Different results were analyzed and investigated under both deterministic and stochastic regimes. Li et al [13] presented a multivalue cellular model for mixed nonmotorized traffic flow composed of bicycles and tricycles. A bicycle was assumed to occupy one unit of cell space and a tricycle two units of cell space.…”

Section: Introductionmentioning

confidence: 99%

Modeling Mixed Bicycle Traffic Flow: A Comparative Study on the Cellular Automata Approach

Zhou

Jin

et al. 2015

Discrete Dynamics in Nature and Society

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“…At present, research on mixed traffic flow using CA has achieved fruitful results, such as the mixed traffic flow of the different attributes of vehicles [7,8], mixed traffic flow of motor vehicles and pedestrians [9,10], mixed traffic flow of different types of nonmotor vehicles [11][12][13], and mixed traffic flow of motor and nonmotor vehicles represented by bicycles and cars [14][15][16]. However, in the recent years, there have been significant changes in the composition of nonmotor vehicles in China, where electric bicycles have become the main component of nonmotor vehicles.…”

Section: Introductionmentioning

confidence: 99%

Traffic Conflict Analysis of Motor Vehicles and Nonmotor Vehicles Based on Improved Cellular Automata

Dong

Rong-guo

Yin

et al. 2020

Mathematical Problems in Engineering

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In recent years, with the rapid development of China's logistics industry and urban service industry, electric bicycles have gradually become an important means of transportation in cities due to their flexibility, green technology, and low operating costs. Because electric bicycles travel though motor vehicle lanes and nonmotor vehicle lanes, the conflict between motor and nonmotor vehicles has become increasingly prominent, and the safety situation is not optimistic. However, most theories and models of mixed traffic flow are based on motor vehicles and bicycles and few involve electric bicycles. To explore the traffic safety situation in an urban mixed traffic environment, this paper first uses cellular automata (CA) to establish a three-strand mixed traffic flow model of motor vehicles, electric bicycles, and bicycles and verifies the reliability of the model by using a MATLAB simulation based on the actual survey data. en, using the technology of traffic conflicts and the conflict rate as the index to evaluate the traffic safety situation, the change in the conflict rate with different road occupancies and different proportional coefficients of motor vehicles is studied. In the end, the conflict rate is compared between the mixed traffic flow and the setting of a physical isolation divider, which provides some suggestions on when to set a physical isolation divider to separate motor vehicles from nonmotor vehicles. e results show that in a mixed traffic environment, the conflict rate first increases and then decreases with increasing road occupancy and reaches a peak when the road occupancy is 0.6. In addition, in mixed traffic environments, the conflict rate increases with an increasing proportional coefficient of the motor vehicle. When the road occupancy rate is within the range of [0.6, 0.9] or when the proportional coefficient of motor vehicle is between [0.8, 0.9], a physical isolation divider can be set to separate motor vehicles and nonmotor vehicles from the space to improve traffic safety.

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“…Initially proposed for mixed traffic of cars and motorcycles in Lan and Chang (2005), it was later applied to mixed bicycle traffic by Jia et al (2007). Since then a whole range of studies have considered mixed bicycle traffic (Gould and Karner, 2009;Zhao et al, 2013;Jin et al, 2015a;Shan et al, 2015;Li and Fang, 2017;Xue et al, 2017) or mixed traffic with bicycles alongside other modes (Vasic and Ruskin, 2012;Luo et al, 2015) using similar and extended CA approaches.…”

Section: Introductionmentioning

confidence: 99%

Fast or forced to follow: A speed heterogeneous approach to congested multi-lane bicycle traffic simulation

Paulsen

Rasmussen

Nielsen

2019

Transportation Research Part B: Methodological

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

Cited by 5 publications

References 14 publications

Modeling Mixed Bicycle Traffic Flow: A Comparative Study on the Cellular Automata Approach

Modeling Mixed Bicycle Traffic Flow: A Comparative Study on the Cellular Automata Approach

Traffic Conflict Analysis of Motor Vehicles and Nonmotor Vehicles Based on Improved Cellular Automata

Fast or forced to follow: A speed heterogeneous approach to congested multi-lane bicycle traffic simulation

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