scholarly journals A Modified Cellular Automaton Model for Accounting for Traffic Behaviors during Signal Change Intervals

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Chih-Cheng Hsu ◽  
Yu-Chiun Chiou
Keyword(s):  
Microscopic Analysis ◽  
Traffic Signal ◽  
Traffic Signal Control ◽  
Fundamental Diagram ◽  
Macroscopic Fundamental Diagram ◽  
Proposed Model ◽  
Signal Change ◽  
Ca Model ◽  
Speed Adjustment ◽  
Diagram Analysis

Previous cellular automata (CA) models have been developed for simulating driver behaviors in response to traffic signal control. However, driver behaviors during traffic signal change intervals, including cross/stop decision and speed adjustment, have not yet been studied. Based on this, this paper aims to propose a change interval CA model for replicating driver’s perception and response to amber light based on stopping probability and speed adjusting functions. The proposed model has been validated by exemplified and field cases. To investigate the applicability of the proposed model, macroscopic and microscopic analyses are conducted. Although the macroscopic fundamental diagram analysis reveals only a small decrease in maximum traffic flow rates with considering driver behaviors in change intervals, in the microscopic analysis, the proposed model can present reasonable vehicular trajectories and deceleration rates during slowdown process.

scholarly journals Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Duowei Li ◽  
Jianping Wu ◽  
Ming Xu ◽  
Ziheng Wang ◽  
Kezhen Hu
Keyword(s):  
Reinforcement Learning ◽  
Waiting Time ◽  
Traffic Signals ◽  
Control Model ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Average Waiting Time ◽  
Adaptive Traffic Signal Control ◽  
Proposed Model

Controlling traffic signals to alleviate increasing traffic pressure is a concept that has received public attention for a long time. However, existing systems and methodologies for controlling traffic signals are insufficient for addressing the problem. To this end, we build a truly adaptive traffic signal control model in a traffic microsimulator, i.e., “Simulation of Urban Mobility” (SUMO), using the technology of modern deep reinforcement learning. The model is proposed based on a deep Q-network algorithm that precisely represents the elements associated with the problem: agents, environments, and actions. The real-time state of traffic, including the number of vehicles and the average speed, at one or more intersections is used as an input to the model. To reduce the average waiting time, the agents provide an optimal traffic signal phase and duration that should be implemented in both single-intersection cases and multi-intersection cases. The co-operation between agents enables the model to achieve an improvement in overall performance in a large road network. By testing with data sets pertaining to three different traffic conditions, we prove that the proposed model is better than other methods (e.g., Q-learning method, longest queue first method, and Webster fixed timing control method) for all cases. The proposed model reduces both the average waiting time and travel time, and it becomes more advantageous as the traffic environment becomes more complex.

Time-Space Diagram Analysis of Urged Acceleration CA Model

2013 ◽  
Vol 779-780 ◽  
pp. 1203-1206
Author(s):  
Rui Kang ◽  
Chen Yu Huang ◽  
Kai Yang
Keyword(s):  
Cellular Automaton ◽  
Metastable State ◽  
Traffic Model ◽  
Nonlinear Phenomena ◽  
Time Space ◽  
Proposed Model ◽  
Ca Model ◽  
Combined Action ◽  
Diagram Analysis ◽  
Real Traffic

Based on the SDNS cellular automaton (CA) traffic model, an improved single lane traffic CA model with considering the urged acceleration and safe deceleration was proposed. The model not only simulated the interaction between vehicles, but also reproduced nonlinear phenomena which tallies with real traffic such as the synchronism flow and the metastable state. The time-space diagram of new model shows a gray synchronous band zone rather than that black blocking band zone. The range of synchronization is smaller when ps is bigger, urging has less function, congestion area is wider. Comparing with SDNS model, with the proposed model in this paper, when emerging congestion, the combined action of urging and safe deceleration enabled system self-adjustment so that efficiently mitigated congestion.

scholarly journals Adaptive Traffic Signal Control: Game-Theoretic Decentralized vs. Centralized Perimeter Control

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 274
Author(s):  
Maha Elouni ◽  
Hossam M. Abdelghaffar ◽  
Hesham A. Rakha
Keyword(s):  
Traffic Congestion ◽  
State Of The Art ◽  
Simulation Software ◽  
Traffic Signal ◽  
Nash Bargaining ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Fundamental Diagram ◽  
Total Delay ◽  
Urban Network

This paper compares the operation of a decentralized Nash bargaining traffic signal controller (DNB) to the operation of state-of-the-art adaptive and gating traffic signal control. Perimeter control (gating), based on the network fundamental diagram (NFD), was applied on the borders of a protected urban network (PN) to prevent and/or disperse traffic congestion. The operation of gating control and local adaptive controllers was compared to the operation of the developed DNB traffic signal controller. The controllers were implemented and their performance assessed on a grid network in the INTEGRATION microscopic simulation software. The results show that the DNB controller, although not designed to solve perimeter control problems, successfully prevents congestion from building inside the PN and improves the performance of the entire network. Specifically, the DNB controller outperforms both gating and non-gating controllers, with reductions in the average travel time ranging between 21% and 41%, total delay ranging between 40% and 55%, and emission levels/fuel consumption ranging between 12% and 20%. The results demonstrate statistically significant benefits of using the developed DNB controller over other state-of-the-art centralized and decentralized gating/adaptive traffic signal controllers.

scholarly journals Network Fundamental Diagram (NFD) and traffic signal control: first empirical evidences from the city of Santander

2017 ◽  
Vol 27 ◽  
pp. 27-34 ◽  
Author(s):  
Borja Alonso ◽  
Ángel Ibeas Pòrtilla ◽  
Giuseppe Musolino ◽  
Corrado Rindone ◽  
Antonino Vitetta
Keyword(s):  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Fundamental Diagram ◽  
Network Fundamental Diagram ◽  
The City

Stochastic Model of Traffic Jam and Traffic Signal Control

2011 ◽  
Vol 131 (2) ◽  
pp. 303-310
Author(s):  
Ji-Sun Shin ◽  
Cheng-You Cui ◽  
Tae-Hong Lee ◽  
Hee-hyol Lee
Keyword(s):  
Stochastic Model ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Jam

Recent Advances in Reinforcement Learning for Traffic Signal Control

2021 ◽  
Vol 22 (2) ◽  
pp. 12-18 ◽  
Author(s):  
Hua Wei ◽  
Guanjie Zheng ◽  
Vikash Gayah ◽  
Zhenhui Li
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Methods ◽  
Advantages And Disadvantages ◽  
Recent Advances

Traffic signal control is an important and challenging real-world problem that has recently received a large amount of interest from both transportation and computer science communities. In this survey, we focus on investigating the recent advances in using reinforcement learning (RL) techniques to solve the traffic signal control problem. We classify the known approaches based on the RL techniques they use and provide a review of existing models with analysis on their advantages and disadvantages. Moreover, we give an overview of the simulation environments and experimental settings that have been developed to evaluate the traffic signal control methods. Finally, we explore future directions in the area of RLbased traffic signal control methods. We hope this survey could provide insights to researchers dealing with real-world applications in intelligent transportation systems

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