A new cellular automata traffic flow model considering asynchronous update of vehicle velocity

2020 ◽  
Vol 31 (12) ◽  
pp. 2050167
Author(s):  
Qi-Lang Li ◽  
Rui Jiang ◽  
Zhong-Jun Ding ◽  
Bing-Hong Wang
Keyword(s):  
Cellular Automata ◽  
Traffic Flow ◽  
Flow Model ◽  
Maximum Flow ◽  
Fundamental Diagram ◽  
Time Step ◽  
Traffic Flow Model ◽  
Traffic Jam ◽  
Vehicle Velocity ◽  
Flow Phase

This study examines the cellular automata traffic flow model, which considers the asynchronous update of vehicles’ velocities. Computer simulations are used to identify three typical phases: linear free flow phase, nonlinear moving phase and traffic jam phase. Compared to the original NaSch model, the system of the present model can reach the maximum flow when the vehicle density is higher. The influence of the delay probability and the maximum time step in which drivers intend to keep their current velocity on fundamental diagram is discussed.

THE TRAFFIC BOTTLENECK EFFECTS CAUSED BY THE LANE CLOSING IN THE CELLULAR AUTOMATA MODEL

2003 ◽  
Vol 14 (10) ◽  
pp. 1295-1303 ◽  
Author(s):  
BIN JIA ◽  
RUI JIANG ◽  
QING-SONG WU
Keyword(s):  
Cellular Automata ◽  
Traffic Flow ◽  
Flow Rate ◽  
Flow Model ◽  
Maximum Flow ◽  
Density Inversion ◽  
Cellular Automata Model ◽  
Traffic Flow Model ◽  
Density Distributions ◽  
Inversion Phenomenon

As a kind of bottleneck, the lane closing has seldomly been investigated with cellular automata model. In this paper, we study this issue using the cellular automata traffic flow model. The capacity and the density distribution of this kind of bottleneck are discussed in details. We find that (i) the capacity of the bottleneck is a little smaller than the maximum flow rate of single-lane road; (ii) different regulations may lead to different density distributions of the vehicles upstream of the lane closing. Moreover, the density inversion phenomenon is reported under certain conditions. This enlightens us to propose that the phenomenon of density inversion reported in many publications may be caused by the bottlenecks on the highway.

scholarly journals A Cellular Automaton Traffic Flow Model with Advanced Decelerations

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Yingdong Liu
Keyword(s):  
Cellular Automaton ◽  
Traffic Flow ◽  
Flow Model ◽  
Stable State ◽  
Practical Significance ◽  
Fundamental Diagram ◽  
Traffic Flow Model ◽  
Short Time ◽  
Vehicle Deceleration ◽  
Real Traffic

A one-dimensional cellular automaton traffic flow model, which considers the deceleration in advance, is addressed in this paper. The model reflects the situation in the real traffic that drivers usually adjust the current velocity by forecasting its velocities in a short time of future, in order to avoid the sharp deceleration. The fundamental diagram obtained by simulation shows the ability of this model to capture the essential features of traffic flow, for example, synchronized flow, meta-stable state, and phase separation at the high density. Contrasting with the simulation results of the VE model, this model shows a higher maximum flux closer to the measured data, more stability, more efficient dissolving blockage, lower vehicle deceleration, and more reasonable distribution of vehicles. The results indicate that advanced deceleration has an important impact on traffic flow, and this model has some practical significance as the result matching to the actual situation.

scholarly journals Evaluating Impacts of Overloaded Heavy Vehicles on Freeway Traffic Condition by a Novel Multi-Class Traffic Flow Model

2018 ◽  
Vol 10 (12) ◽  
pp. 4694 ◽  
Author(s):  
Xiang Wang ◽  
Po Zhao ◽  
Yanyun Tao
Keyword(s):  
Traffic Flow ◽  
Flow Model ◽  
Management Strategies ◽  
Maximum Speed ◽  
Heavy Vehicles ◽  
Fundamental Diagram ◽  
Traffic Condition ◽  
Traffic Flow Model ◽  
Traffic Conditions ◽  
The Impact

Overloaded heavy vehicles (HVs) have significant negative impacts on traffic conditions due to their inferior driving performance. Highway authorities need to understand the impact of overloaded HVs to assess traffic conditions and set management strategies. We propose a multi-class traffic flow model based on Smulders fundamental diagram to analyze the influence of overloaded HVs on traffic conditions. The relationship between the overloading ratio and maximum speed is established by freeway toll collection data for different types of HVs. Dynamic passenger car equivalent factors are introduced to represent the various impacts of overloaded HVs in different traffic flow patterns. The model is solved analytically and discussed in detail in the appendices. The model validation results show that the proposed model can represent traffic conditions more accurately with consideration for overloaded HVs. The scenario tests indicate that the increase of overloaded HVs leads to both a higher congestion level and longer duration.

scholarly journals A Stochastic Continuous Cellular Automata Traffic Flow Model with a Multi-agent Fuzzy System

2012 ◽  
Vol 54 ◽  
pp. 1350-1359 ◽  
Author(s):  
Öznur Yeldan ◽  
Alberto Colorni ◽  
Alessandro Luè ◽  
Emanuele Rodaro
Keyword(s):  
Cellular Automata ◽  
Traffic Flow ◽  
Fuzzy System ◽  
Flow Model ◽  
Traffic Flow Model ◽  
Multi Agent

Theoretical analysis of a hybrid traffic model accounting for safe velocity

2017 ◽  
Vol 31 (11) ◽  
pp. 1750104 ◽  
Author(s):  
Yu-Qing Wang ◽  
Chao-Fan Zhou ◽  
Bo-Wen Yan ◽  
De-Chen Zhang ◽  
Ji-Xin Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Flow Model ◽  
Local Equilibrium ◽  
Density Wave ◽  
Traffic Model ◽  
Safe Driving ◽  
Traffic Flow Model ◽  
Velocity Wave ◽  
Vehicle Velocity ◽  
Global Equilibrium

A hybrid traffic-flow model [Wang–Zhou–Yan (WZY) model] is brought out in this paper. In WZY model, the global equilibrium velocity is replaced by the local equilibrium one, which emphasizes that the modification of vehicle velocity is based on the view of safe-driving rather than the global deployment. In the view of safe-driving, the effect of drivers’ estimation is taken into account. Moreover, the linear stability of the traffic model has been performed. Furthermore, in order to test the robustness of the system, the evolvement of the density wave and the velocity wave of the traffic flow has been numerically calculated.

The Role of Traffic Flow Preview for Planning Fuel Optimal Vehicle Velocity

2010 ◽  
Author(s):  
Behrang Asadi ◽  
Chen Zhang ◽  
Ardalan Vahidi
Keyword(s):  
Traffic Flow ◽  
Fuel Economy ◽  
Flow Model ◽  
Optimal Velocity ◽  
Traffic Flow Model ◽  
Traffic Pattern ◽  
Vehicle Velocity ◽  
Simulation Results ◽  
Traffic Wave

A vehicle’s untimely arrival at a local traffic wave with lots of stops and goes increases its fuel use. This paper proposes predictive planning of the vehicle velocity for reducing the velocity transients in upcoming traffic waves. Macroscopic evolution of traffic pattern along the vehicle route is first estimated by combining a traffic flow model. The fuel optimal velocity trajectory is calculated by solving an optimal control problem with the spatiotemporally varying constraint imposed by the traffic. Preliminary simulation results indicate the potential for improvement in fuel economy with a little compromise on travel time.

Effect of Multi-Slowdown Sections on Traffic Flow

2012 ◽  
Vol 253-255 ◽  
pp. 1619-1622
Author(s):  
Yan Hong Fan ◽  
Hua Kuang ◽  
Guo Xin Zhang ◽  
Ling Jiang Kong ◽  
Xing Li Li
Keyword(s):  
Energy Consumption ◽  
Traffic Flow ◽  
Maximum Flow ◽  
Underlying Mechanism ◽  
Speed Limit ◽  
Fundamental Diagram ◽  
Mean Velocity ◽  
Traffic System ◽  
Flow Phase ◽  
Multi Phase

Based on the NS model, an extended cellular automaton model is proposed to simulate complex characteristics and energy consumption of traffic flow with some slowdown sections on a highway by considering the number, speed limit and distribution of slowdown sections. The simulation results show that the present model can exhibit a multi-phase coexistence phenomenon, i.e., the freely moving phase, the maximum flow phase and the jamming phase coexist in traffic system. The fundamental diagram shows that the number of slowdown section has no influence on the mean velocity and flow. However, energy consumption increases with increase of the number of slowdown section at low density. In addition, it can be found that the speed limit and distribution of different slowdown sections have an important effect on traffic flow and energy consumption, and the underlying mechanism is also analyzed.

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