Simulation model of speed–density characteristics for mixed bicycle flow—Comparison between cellular automata model and gas dynamics model

Zhang, Shuichao; Ren, Gang; Yang, Renfa

doi:10.1016/j.physa.2013.06.019

Cited by 35 publications

(25 citation statements)

References 7 publications

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“…Accurately describing the size and speed of heterogeneous bicycles is the foundation of modeling bicycle traffic. Based on field surveys [2,22], the typical lengths of RBs and EBs in China are not significantly different from one another, at around 1.7-1.9 m. Adding in a safe distance between two successive bicycles, the length of a bicycle cell is set to 2 m, which has also been widely used in most of the previous bicycle CA models [9][10][11][12]. Based on the criteria in the US and China, it is recommended that the standard width of a bicycle lane should be 1-1.2 m [2,10].…”

Section: Definition Of Bicycles' Maximum Speedsmentioning

confidence: 99%

“…Field data under uncongested conditions, from Davis, California, were used for calibration. Zhang et al [10] also used a three-lane NS model and an improved lane-changing rule for analyzing the speed-density characteristics of mixed bicycle flow. Zhao et al [11] used the CA method to model the characteristics of bicycle passing events in mixed bicycle traffic on separated bicycle paths.…”

Section: Introductionmentioning

confidence: 99%

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

Jin

Cheng

et al. 2015

Physics Letters A

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Section: Definition Of Bicycles' Maximum Speedsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Jin

Cheng

et al. 2015

Physics Letters A

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“…Their results showed that there was a critical value that divides the vehicle flow into free and congested flow portions. Zhang et al [16] compared CA and gas dynamics models using speed density characteristics of the mixed bicycle traffic (i.e. bicycle traffic including electric bicycles).…”

Section: Introductionmentioning

confidence: 99%

Modelling vehicular interactions for heterogeneous traffic flow using cellular automata with position preference

2017

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This paper proposes and validates a modified cellular automata model for determining interaction rate (i.e. number of car-following/overtaking instances) using traffic flow data measured in the field. The proposed model considers lateral position preference by each vehicle type and introduces a position preference parameter b in the model which facilitates gradual drifting towards preferred position on road, even if the gap in front is sufficient. Additionally, the model also improves upon the conventional model by calculating safe front and back gap dynamically based on speed and deceleration properties of leader and follower vehicles. Sensitivity analysis was carried out to determine the effect of b on vehicular interactions and the model was calibrated and validated using interaction rates observed in the field. Paired tests were conducted to determine the validity of the model in determining interaction rates. Results of the simulations show that there is a parabolic relationship between area occupancy and interaction rate of different vehicle types. The model performed satisfactorily as the simulated interaction rate between different vehicle types were found to be statistically similar to those observed in field. Also, as expected, the interaction rate between light motor vehicles (LMVs) and heavy motor vehicles (HMVs) were found to be higher than that between LMVs and three wheelers because LMVs and HMVs share the same lane. This could not be done using conventional CA models as lateral movement rules were dictated by only speeds and gaps. So, in conventional models, the vehicles would end up in positions which are not realistic. The position preference parameter introduced in this model motivates vehicles to stay in their preferred positions. This study demonstrates the use of interaction rate as a measure to validate microscopic traffic flow models.

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“…The simulation results showed that the mixed nonmotorized traffic capacity increased with an increase in the electric bicycle ratio. Zhang et al [16] used an improved three-lane NS model to analyze the speed-density characteristics of mixed bicycle flow. The simulation results of the CA model were effectively consistent with the actual survey data when the density was lower than 0.225 bic/m 2 .…”

Section: Introductionmentioning

confidence: 99%

“…Based on field surveys, the length of most RBs and EBs is 1.7-1.9 m. Meanwhile, bicycle lanes are set at 1 meter wide in both China and the USA [17,18]. Therefore, the size of a RB or an EB is assumed rectangular, with length 2 m and width 1 m, as is widely used in other CA models [14][15][16]. The other parameter for modeling bicycle traffic is speed.…”

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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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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Simulation model of speed–density characteristics for mixed bicycle flow—Comparison between cellular automata model and gas dynamics model

Cited by 35 publications

References 7 publications

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

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

Modelling vehicular interactions for heterogeneous traffic flow using cellular automata with position preference

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

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