CELLULAR AUTOMATON MODELS FOR MIXED TRAFFIC FLOW CONSIDERING PASSAGE WAY OF EMERGENCY VEHICLE

2013 ◽  
Vol 27 (08) ◽  
pp. 1350052 ◽  
Author(s):  
HAN-TAO ZHAO ◽  
HONG-YAN MAO ◽  
RUI-JIN HUANG

Two kinds of cellular automaton models are proposed for mixed traffic flow with emphasis on emergency vehicles. By analyzing the characteristics of ordinary vehicles in giving way to emergency vehicles, the rules for changing lanes are modified. Computer numerical simulation results indicate that an emergency vehicle without changing lanes can enhance speed with density lower than 0.1, while its speed can be enhanced by changing lane with density greater than 0.1. Meanwhile, vehicle speed and density within a certain range around emergency vehicles are lower than the road section average velocity and average density. The passage way of emergency vehicle that facilitate lane change causes less interference than that of an emergency vehicle which is unable to change lane. The study found that the physical characteristics of traffic flow when there are emergency vehicles are significantly different from routine traffic flow. Emergency vehicles can facilitate their passage by changing lanes at a medium or high density.

2020 ◽  
Vol 537 ◽  
pp. 122686 ◽  
Author(s):  
Xue Wang ◽  
Yu Xue ◽  
Bing-ling Cen ◽  
Peng Zhang ◽  
Hong-di He

2010 ◽  
Vol 21 (12) ◽  
pp. 1443-1455 ◽  
Author(s):  
DONG-FAN XIE ◽  
ZI-YOU GAO ◽  
XIAO-MEI ZHAO

To depict the mixed traffic flow consisting of motorized (m-) and non-motorized (nm-) vehicles, a new cellular automaton model is proposed by combining the NaSch model and the BCA model, and some rules are also introduced to depict the interaction between m-vehicles and nm-vehicles. By numerical simulations, the flux-density relations are investigated in detail. It can be found that the flux-density curves of m-vehicle flow can be classified into two types, corresponding to small and large density regions of nm-vehicles, respectively. In small density region of nm-vehicles, the maximum flux as well as the critical density decreases with the increase of nm-vehicle density. Similar characteristics can also be found in large density region of nm-vehicles. However, compared with the former case, the maximum flux is much lower, the phase transition from free flow to congested flow becomes continuous and thus the corresponding critical points are non-existent. The flux-density curves of nm-vehicle flow can also be classified into two types. And interestingly, the maximum flux and the corresponding density decrease first and keep constant later as the density of m-vehicle increases. Finally, the total transport capacity of the system is investigated. The results show that the maximum capacity can be reached at appropriate proportions for m-vehicles and nm-vehicles, which induces a controlling method to promote the capacity of mixed traffic flow.


2012 ◽  
Vol 241-244 ◽  
pp. 2082-2087
Author(s):  
Li Yang ◽  
Jun Hui Hu ◽  
Ling Jiang Kong

Based on the two-dimension cellular automaton traffic flow model (BML model), a mixed traffic flow model for urban traffic considering the transit traffic is established in this paper. Under the don't block the box rules and the opening boundary conditions, the impacts of transit traffic, the central station, traffic lights cycle, the vehicles length on the mixed traffic flow is studied by computer simulation. Some important characters appearing in the new model are also elucidated. It shows that traffic flow is closely related to traffic lights cycle, the geometric structure of transport network and boundary conditions. Under certain traffic light cycle time, the traffic flow has a periodical oscillation change. The comparison to practical measured data shows that our stimulation results are accordant with the changes of real traffic flow, which confirms the accuracy and rationality of our model.


2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Chenhao Dong ◽  
Rongguo Ma ◽  
Yujie Yin ◽  
Borui Shi ◽  
Wanting Zhang ◽  
...  

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. Then, 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. The 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.


2007 ◽  
Vol 380 ◽  
pp. 470-480 ◽  
Author(s):  
Jian-ping Meng ◽  
Shi-qiang Dai ◽  
Li-yun Dong ◽  
Jie-fang Zhang

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