scholarly journals Modelling Signal Controlled Traffic Based on Driving Behaviors

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Yang Wang ◽  
Yanyan Chen ◽  
Ning Chen
Keyword(s):  
Cellular Automaton ◽  
Numerical Simulations ◽  
Traffic Signal ◽  
Cellular Automaton Model ◽  
Urban Traffic ◽  
Road Segment ◽  
Traffic Light ◽  
Driving Behaviors ◽  
Proposed Model ◽  
Anticipatory Behavior

In urban traffic, of particular interest the traffic breakdown which is primarily resulted from the driving behaviors is emerged to respond to the traffic signal. To investigate the influences of driving behaviors on the traffic breakdown, a cellular automaton model has been developed by incorporating a number of driving behaviors typically manifesting during the different stages when the vehicle approaching a traffic light. Numerical simulations have been performed based on a road segment consisting of three sections and each section is associated with a set of rules. The numerical simulations have demonstrated that the proposed model is capable of producing the time-delayed traffic breakdown and the dissolution of the oversaturated traffic. Furthermore, it has been evidenced that the probability of the traffic breakdown can be increased by involving the slow-to-start behavior. However, the activation of the anticipatory behavior can effectively impede the transition from undersaturated to oversaturated traffic. Finally, the contributions of the driving behaviors on the traffic breakdown have been quantitatively examined.

scholarly journals Multi-Agent Reinforcement Learning for Optimizing Traffic Signal Timing

2021 ◽  
Author(s):  
Areej Salaymeh ◽  
Loren Schwiebert ◽  
Stephen Remias
Keyword(s):  
Reinforcement Learning ◽  
Traffic Signals ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Timing ◽  
Model Free ◽  
Proposed Model ◽  
Multi Agent ◽  
Traffic Signal Timing

Designing efficient transportation systems is crucial to save time and money for drivers and for the economy as whole. One of the most important components of traffic systems are traffic signals. Currently, most traffic signal systems are configured using fixed timing plans, which are based on limited vehicle count data. Past research has introduced and designed intelligent traffic signals; however, machine learning and deep learning have only recently been used in systems that aim to optimize the timing of traffic signals in order to reduce travel time. A very promising field in Artificial Intelligence is Reinforcement Learning. Reinforcement learning (RL) is a data driven method that has shown promising results in optimizing traffic signal timing plans to reduce traffic congestion. However, model-based and centralized methods are impractical here due to the high dimensional state-action space in complex urban traffic network. In this paper, a model-free approach is used to optimize signal timing for complicated multiple four-phase signalized intersections. We propose a multi-agent deep reinforcement learning framework that aims to optimize traffic flow using data within traffic signal intersections and data coming from other intersections in a Multi-Agent Environment in what is called Multi-Agent Reinforcement Learning (MARL). The proposed model consists of state-of-art techniques such as Double Deep Q-Network and Hindsight Experience Replay (HER). This research uses HER to allow our framework to quickly learn on sparse reward settings. We tested and evaluated our proposed model via a Simulation of Urban MObility simulation (SUMO). Our results show that the proposed method is effective in reducing congestion in both peak and off-peak times.

scholarly journals Streamline Simulation and Analysis of Pedestrian Weaving Flow in Large Passenger Terminal

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jun Li ◽  
Jing Wang ◽  
Yuanfang Dong ◽  
Hongfei Jia ◽  
Yanzhong Li
Keyword(s):  
Cellular Automaton ◽  
Numerical Simulations ◽  
Cellular Automaton Model ◽  
Time And Space ◽  
Streamline Simulation ◽  
Floor Field ◽  
The Relationship ◽  
Emptying Time

A new concept called the extended weaving area is proposed to relieve the conflicts and clogging caused by pedestrian weaving in both time and space in large passenger terminal. The cellular automaton model that considers pedestrian walking habits based on the floor field is adopted. Numerical simulations are carried out in MATLAB environment to explore the relationship between the emptying time and bottleneck setting when four groups of pedestrians walk to four exits through the weaving areas with different settings. It is found that, by using improved extended weaving area settings, the stress of the weaving area could be relieved in both time and space; thus the efficiency of pedestrians passing could be improved. Based on the simulation, the threshold of single bottleneck width in the extended weaving area is also given in this research.

scholarly journals Analyzing the Impact of Trucks on Traffic Flow Based on an Improved Cellular Automaton Model

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Dewen Kong ◽  
Xiucheng Guo ◽  
Bo Yang ◽  
Dingxin Wu
Keyword(s):  
Cellular Automaton ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Traffic Volume ◽  
Cellular Automaton Model ◽  
Passenger Cars ◽  
Passenger Car ◽  
Car Following ◽  
Proposed Model ◽  
The Impact

This paper aims to analyze the impact of trucks on traffic flow and propose an improved cellular automaton model, which considers both the performance difference between passenger cars and trucks and the behaviour change of passenger cars under the impact of trucks. A questionnaire survey has been conducted to find out whether the impact of trucks exists and how the behaviour of passenger car drivers changes under the impact of trucks. The survey results confirm that the impact of trucks exists and indicate that passenger car drivers will enlarge the space gap, decelerate, and change lanes in advance when they are affected. Simulation results show that traffic volume is still affected by percentages of trucks in the congestion phase in the proposed model compared with traditional heterogeneous cellular automaton models. Traffic volume and speed decrease with the impact of trucks in the congestion phase. The impact of trucks can increase traffic congestion as it increases. However, it has different influences on the speed variance of passenger cars in different occupancies. In the proposed model, the relative relationship of the space gap between car-following-truck and car-following-car is changeable at a certain value of occupancy, which is related to the impact of trucks.

Carbon dioxide emission in a single-lane cellular automaton model with a series of traffic lights

2020 ◽  
Vol 31 (11) ◽  
pp. 2050154
Author(s):  
H. Binoua ◽  
H. Ez-Zahraouy ◽  
A. Khallouk ◽  
N. Lakouari
Keyword(s):  
Energy Dissipation ◽  
Cellular Automaton ◽  
Traffic Flow ◽  
Control Method ◽  
Carbon Dioxide Emission ◽  
Cellular Automaton Model ◽  
Traffic Light ◽  
Fundamental Diagram ◽  
Traffic Lights ◽  
Synchronized Control

In this paper, we propose a cellular automaton model to simulate traffic flow controlled by a series of traffic lights. The synchronized traffic light and the green wave light strategies were investigated. The spatiotemporal diagrams, energy dissipation, and CO2 emission of the system were presented. Our simulations are conducted to clarify the difference between both strategies and their effects on the traffic flow and the CO2 emission. We found that the traffic flow depends mainly on the strategy used for managing the traffic lights as well as on the parameters of the traffic lights, namely the cycle length, the number of traffic lights and the length of the system. The fundamental diagram has barely the same characteristics for both methods and it depends on the combination of the parameters of the system. We find that the green wave is more convenient for the management of a series of traffic lights than the synchronized control strategy in terms of throughput, especially for large-sized systems. Unlike in terms of CO2 emission and energy dissipation, both control strategies outperform each other depending on the density regions and the parameters of the system. Finally, we investigate the effect of both cycles (i.e. red and green) for the synchronized control method on the CO2 emission. It is found that the green cycle generates often a series of acceleration events that increase CO2 emission.

TRAFFIC FLOW INFLUENCED BY TRAFFIC LIGHT AND TURNING PROBABILITY FOR A CROSSROAD

2004 ◽  
Vol 18 (17n19) ◽  
pp. 2658-2662 ◽  
Author(s):  
HUILI TAN ◽  
CHAOYING ZHANG ◽  
LINGJIANG KONG ◽  
MUREN LIU
Keyword(s):  
Boundary Condition ◽  
Cellular Automaton ◽  
Traffic Flow ◽  
Cellular Automaton Model ◽  
Open Boundary ◽  
Open Boundary Condition ◽  
Traffic Light ◽  
Cycle Times ◽  
The Road ◽  
The Impact

A cellular automaton model with open boundary condition for a crossroad system controlled by a traffic light is presented. The traffic flow and speed of the first part of the road are quite different from those of the second part behind the crossing. The impact of turning probabilities and the cycle times of traffic light on the flow are investigated.

scholarly journals A Cellular Automaton Model for Traffic Flow -Investigating the Effect of Turning

2014 ◽  
Vol 12 (1) ◽  
Author(s):  
Tracy Finner ◽  
Matthew Beauregard
Keyword(s):  
Cellular Automaton ◽  
Traffic Flow ◽  
Power Law ◽  
Average Velocity ◽  
Cellular Automaton Model ◽  
Light Cycle ◽  
Vehicular Traffic ◽  
Traffic Light ◽  
Vehicular Traffic Flow

A cellular automaton model is proposed, modeling vehicular traffic flow on a two dimensional lattice in which the vehicles turn at an intersection with a given probability. It is shown that the introduction of turning reduces the long-term average velocity, and can be predicted by a power law depending on the probability of a vehicle turning and the density of cars. The reduction in speed decreases rapidly once the light cycle length surpasses a certain threshold, the value of which can be predicted from the observed power law. Keywords: cellular automaton, traffic flow, traffic light strategy, turning, dynamical systems, power law

scholarly journals A Mixed-Flow Cellular Automaton Model for Vehicle Nonstrict Priority Give-Way Behavior at Crosswalks

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yunxuan Li ◽  
Zeyang Cheng ◽  
Jian Lu ◽  
Lin Zhang
Keyword(s):  
Cellular Automaton ◽  
Traffic Flow ◽  
Cellular Automaton Model ◽  
Mixed Flow ◽  
Saturated Flow ◽  
Mixed Traffic Flow ◽  
Driving Behaviors ◽  
Bicycle Model ◽  
Negative Effect ◽  
Saturation Flow

The vehicle nonstrict priority give-way behavior (VNPGWB) is a common part of traffic interaction between motorized and nonmotorized vehicles in many countries. This study proposes a mixed-flow cellular automaton model to simulate the passing of vehicles in front of bicycles at crosswalks. The mixed-flow model combines a vehicle model with a bicycle model, using nonstrict priority give-way and strict give-way two driving behaviors defined as relating to the decision point rule and the launching rule, respectively. Simulation results showed that as the vehicle and bicycle inflow rates increased, a critical inflow rate divided vehicle and bicycle traffic flow into free flow and saturated flow conditions. The values of vehicle saturation flow decreased from 0.34 to 0.05, and the values of bicycle saturation flow decreased from 0.54 to 0.44, indicating that the mixed traffic flow has a negative effect on vehicle and bicycle saturated flow. Results also showed that VNPGWB effectively improves vehicle saturation flow over that of the strict give way. The advantage of VNPGWB is more significant when vehicles and bicycles are in saturation traffic flow.

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