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

Abstract

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.

Multimetric Analysis of a Simulated Mixed Traffic of Motorcycles and Automobiles: Flow, Energy, CO2 and Costs

“…The CA model is also known as the NaSch model, an abbreviation from the researchers that first employed the model: Nagel and Schreckenberg, in 1992. This model was successfully improved and is still employed by several researchers (Chen and Wang, 2016;Hua, Yue, Wei, Chen, and Wang, 2020;Lan and Chang, 2005;Lan, Chiou, Lin, and Hsu, 2010;Lv, Song, Liu, and Ma, 2013;(Sean) Qian, Li, Li, Zhang, and Wang, 2017;Ruan, Zhou, Tu, Jin, and Shi, 2017;Yang, Qiu, Yu, Sun, and Pu, 2015;Zeng et al, 2021).…”

Dynamic swarm particle for fast motion vehicle tracking

“…According to [24], the movement of cars can be effectively modeled as a continuum. One of the model that is very effective is the cellular automaton model [25,26], which uses discrete variables to represent the traffic dynamical system. In this approach, the road is divided into road part length Δx and the time is discretized into Δt.…”

A cellular automaton-based model of ship traffic flow in busy waterways

“…Due to improvements in computing capability, the method of CA microscopic simulation was successfully used for modelling traffic flows (Biham et al, 1992; Takayasu and Takayasu, 1993; Kerner et al, 2002). One of the most favourable aspects of CA simulation is that it can extract the features from complex systems and then represent the systems using abstract forms for subsequently analysing and solving the relevant problems (Chowdhury et al, 2000; Helbing, 2001; Chen and Wang, 2015, 2016; Bao et al, 2018; Yang et al, 2019). The applications of CA modelling are very diverse in transportation fields ranging from motor vehicles, bicycles, pedestrians, trains, airplanes and ships (Naito and Nagatani, 2012; Rahman et al, 2013; Chen et al, 2019c; Yi et al, 2020), but research on ship traffic flow based on CA modelling is in its infancy and there have so far been very few research achievements.…”