Limits...
A new cellular automaton model for urban two-way road networks.

Shi J, Cheng L, Long J, Liu Y - Comput Intell Neurosci (2014)

Bottom Line: Simulation results show that the network fundamental diagram is very similar to that of road traffic flow.We found that the randomization probability and the maximum vehicle speed have significant impact on network traffic mobility for free-flow state.Their effect may be weak when the network is congested.

View Article: PubMed Central - PubMed

Affiliation: College of Engineering, Zhejiang Normal University, Jinhua 321004, China ; School of Transportation, Southeast University, Nanjing 210096, China.

ABSTRACT
A new cellular automaton (CA) model is proposed to simulate traffic dynamics in urban two-way road network systems. The NaSch rule is adopted to represent vehicle movements on road sections. Two novel rules are proposed to move the vehicles in intersection areas, and an additional rule is developed to avoid the "gridlock" phenomenon. Simulation results show that the network fundamental diagram is very similar to that of road traffic flow. We found that the randomization probability and the maximum vehicle speed have significant impact on network traffic mobility for free-flow state. Their effect may be weak when the network is congested.

Show MeSH
Update rules of the vehicles in intersection areas (e.g., take the vehicles from Lane 1). (a), (b), (c), and (d) show the four scenarios by which the vehicle is allowed to enter the intersection. (e), (f), (g), and (h) show the four occasions on which the vehicle is forbidden to enter the intersection. (i) shows the “gridlock” phenomenon. The number on the vehicle represents which lane it comes from. ⌀ represents that the cell is empty. × represents that the cell is occupied by a vehicle.
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fig4: Update rules of the vehicles in intersection areas (e.g., take the vehicles from Lane 1). (a), (b), (c), and (d) show the four scenarios by which the vehicle is allowed to enter the intersection. (e), (f), (g), and (h) show the four occasions on which the vehicle is forbidden to enter the intersection. (i) shows the “gridlock” phenomenon. The number on the vehicle represents which lane it comes from. ⌀ represents that the cell is empty. × represents that the cell is occupied by a vehicle.

Mentions: There are a total of 36 conflict points in each intersection and 9 conflict points for each cell in the intersection. To prevent vehicle collision, we assume that a vehicle in the cells in an intersection has priority over the vehicles in the cells near the intersection. For example, if Cell 4 is occupied by a left-turning vehicle from Lane 2 to Lane 7 or an ahead or left-turning vehicle from Lane 4, the vehicle in Cell 5 will be forbidden to drive into Cell 1. The following three rules will be adopted to update vehicles in intersection areas (see Figure 4).


A new cellular automaton model for urban two-way road networks.

Shi J, Cheng L, Long J, Liu Y - Comput Intell Neurosci (2014)

Update rules of the vehicles in intersection areas (e.g., take the vehicles from Lane 1). (a), (b), (c), and (d) show the four scenarios by which the vehicle is allowed to enter the intersection. (e), (f), (g), and (h) show the four occasions on which the vehicle is forbidden to enter the intersection. (i) shows the “gridlock” phenomenon. The number on the vehicle represents which lane it comes from. ⌀ represents that the cell is empty. × represents that the cell is occupied by a vehicle.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4236970&req=5

fig4: Update rules of the vehicles in intersection areas (e.g., take the vehicles from Lane 1). (a), (b), (c), and (d) show the four scenarios by which the vehicle is allowed to enter the intersection. (e), (f), (g), and (h) show the four occasions on which the vehicle is forbidden to enter the intersection. (i) shows the “gridlock” phenomenon. The number on the vehicle represents which lane it comes from. ⌀ represents that the cell is empty. × represents that the cell is occupied by a vehicle.
Mentions: There are a total of 36 conflict points in each intersection and 9 conflict points for each cell in the intersection. To prevent vehicle collision, we assume that a vehicle in the cells in an intersection has priority over the vehicles in the cells near the intersection. For example, if Cell 4 is occupied by a left-turning vehicle from Lane 2 to Lane 7 or an ahead or left-turning vehicle from Lane 4, the vehicle in Cell 5 will be forbidden to drive into Cell 1. The following three rules will be adopted to update vehicles in intersection areas (see Figure 4).

Bottom Line: Simulation results show that the network fundamental diagram is very similar to that of road traffic flow.We found that the randomization probability and the maximum vehicle speed have significant impact on network traffic mobility for free-flow state.Their effect may be weak when the network is congested.

View Article: PubMed Central - PubMed

Affiliation: College of Engineering, Zhejiang Normal University, Jinhua 321004, China ; School of Transportation, Southeast University, Nanjing 210096, China.

ABSTRACT
A new cellular automaton (CA) model is proposed to simulate traffic dynamics in urban two-way road network systems. The NaSch rule is adopted to represent vehicle movements on road sections. Two novel rules are proposed to move the vehicles in intersection areas, and an additional rule is developed to avoid the "gridlock" phenomenon. Simulation results show that the network fundamental diagram is very similar to that of road traffic flow. We found that the randomization probability and the maximum vehicle speed have significant impact on network traffic mobility for free-flow state. Their effect may be weak when the network is congested.

Show MeSH