A Novel Approach to Coordinating Green Wave System With Adaptation Evolutionary Strategy

Zheng, Ye; Guo, Ruifeng; Ma, Ding; Li, Xiaoming

doi:10.1109/access.2020.3037129

Cited by 13 publications

(9 citation statements)

References 47 publications

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“…Qu et al [12] optimized the key parameters of arterial control based on traffic wave theory. Zheng et al [13] proposed two arterial traffic coordination control methods, namely, DDPG-BAND and ES-BAND. Yao et al [14] proposed a coordinated control model based on trajectory data.…”

Section: Literature Reviewmentioning

confidence: 99%

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Arterial Coordination Control Optimization Based on AM–BAND–PBAND Model

Luo

Liu

et al. 2022

Sustainability

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The green wave coordinated control model has evolved from the basic bandwidth maximization model to the multiweight approach to an asymmetrical multiband model and a general signal progression model with phase optimization to improve the operational efficiency of urban arterial roads and reduce driving delays and the amount of exhaust gas generated by vehicles queuing at intersections. However, most of the existing green wave models of arterial roads are based on a single phase pattern and little consider the optimization of the combination of multiple phase patterns. Initial queue clearing time is also considered at the green wave progression line in the time–space diagram, which leads to a waste of green light time. This study proposes a coordination control optimization method based on an asymmetrical multiband model with phase optimization to address the abovementioned problem. This model optimizes four aspects in the time–distance diagram: phase pattern selection, phase sequence, offset, and queue clearing time. Numerical experiments were conducted using the VISSIM micro traffic simulation tool for intersections along Kunlunshan South Road in Qingdao, and the effect of green wave coordination was evaluated using hierarchical analysis and compared with the signal-timing schemes generated by the four models: the multiweight approach, the improved multiweight approach, an asymmetrical multiband model, and a general signal progression model with phase optimization. The results show that an asymmetrical multiband model with phase optimization obtains a total bandwidth of 314 s in both directions. In the outbound direction, average number of stops, average travel speed, average travel time, and average delay time improve by 16%, 7.9%, 17.9%, and 15.6%, respectively. In the inbound direction, they improve by 43.7%, 16.1%, 40.7%, and 36%, respectively. Polluting gas emissions and fuel consumption improve by 17.9%. The applicability of the optimization method under different traffic flow conditions is analyzed, and results indicate a clear control effect when the traffic volume is moderate and the turning vehicles on the feeder roads are few. This work can provide a reference for the optimization of subsequent arterial signal coordination and also has indirect significance for environmental protection to a certain extent.

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Section: Literature Reviewmentioning

confidence: 99%

“…Constraint (12) restricts the value to positive numbers; constraint (13) restricts the value to integers; constraints ( 14) and ( 15) indicate that both are binary variables and take the value of 0 or 1.…”

Section: Constraintsmentioning

confidence: 99%

Arterial Coordination Control Optimization Based on AM–BAND–PBAND Model

Luo

Liu

et al. 2022

Sustainability

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“…Examples of autonomous distributed control include methods using mixed integer linear programming problems [5] or evolutionary strategies [6]. However, these have only been validated on fixed control areas or small road networks.…”

Section: Introductionmentioning

confidence: 99%

Traffic information interpolation method based on traffic flow emergence using swarm intelligence

Suga

Fujimori²,

Yamada³

et al. 2023

Artif Life Robotics

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Traffic congestion has become one of the most pressing social problems in today’s society, and research into appropriate traffic signal control is actively underway. At present, most traffic signal control methods define traffic signal parameters on the basis of traffic information such as the number of passing vehicles. Installing sensors at a vast number of intersections is necessary for more precise and real-time adaptive control, but this is unrealistic from the viewpoint of cost. As an alternative, we propose a swarm intelligence-based methodology that creates routes with a similar traffic volume using the traffic information from intersections already equipped with sensors and interpolates this information in the intersections without sensors in real time. Our simulation results show that the proposed methodology can effectively create similar traffic routes for main traffic flows with high traffic volumes. The results also show that it has an excellent interpolation performance for heavy traffic flows and can adapt and interpolate to situations where traffic flow changes suddenly. Moreover, the interpolation results are highly accurate at a road link where traffic flows confluence. We also developed an interpolation algorithm that is adaptable to traffic patterns with confluence traffic flows. Experiments were conducted with a simulation of merging traffic flows and the proposed method showed good results.

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“…For instance, some researchers have used deep reinforcement learning (DRL) to optimize signal timings and reduce delays at intersections [13]. Other researchers have applied a combination of genetic algorithms and fuzzy logic to optimize intersection signal control [14]. In these methods, it has been shown how to improve traffic flow and reduce delays and congestion.…”

Section: Introductionmentioning

confidence: 99%

Analyzing and Optimizing the Emission Impact of Intersection Signal Control in Mixed Traffic

Fan,

Najafi,

Sarang

et al. 2023

Sustainability

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Signalized intersections are one of the typical bottlenecks in urban transport systems that have reduced speeds and which have substantial vehicle emissions. This study aims to analyze and optimize the impacts of signal control on the emissions of mixed traffic flow (CO, HC, and NOx) containing both heavy- and light-duty vehicles at urban intersections, leveraging high-resolution field emission data. An OBEAS-3000 (Manufacturer: Xiamen Tongchuang Inspection Technology Co., Ltd., Xiamen, China.) vehicle emission testing device was used to collect microscopic operating characteristics and instantaneous emission data of different vehicle types (light- and heavy-duty vehicles) under different operating conditions. Based on the collected data, the VSP (Vehicle Specific Power) model combined with the VISSIM traffic simulation platform was used to quantitatively analyze the impact of signal control on traffic emissions. Heavy-duty vehicles contribute to most of the emissions regardless of the low proportion in the traffic flows. Afterward, a model is proposed for determining the optimal signal control at an intersection for a specific percentage of heavy-duty vehicles based on the conversion of emission factors of different types of vehicles. Signal control is also optimized based on conventional signal timing, and vehicle emissions are calculated. In the empirical analysis, the changes in CO, HC, and NOx emissions of light- and heavy-duty vehicles before and after conventional signal control optimization are quantified and compared. After the signal control optimization, the CO, HC, and NOx emissions of heavy-duty vehicles were reduced. The CO and HC emissions of light-duty vehicles were reduced, but the NOx emissions of light-duty vehicles remained unchanged. The emissions of vehicles after optimized signal control based on vehicle conversion factors are reduced more significantly than those after conventional optimized signal control. This study provides a scientific basis for developing traffic management measures for energy saving and emission reduction in transport systems with mixed traffic.

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A Novel Approach to Coordinating Green Wave System With Adaptation Evolutionary Strategy

Cited by 13 publications

References 47 publications

Arterial Coordination Control Optimization Based on AM–BAND–PBAND Model

Arterial Coordination Control Optimization Based on AM–BAND–PBAND Model

Traffic information interpolation method based on traffic flow emergence using swarm intelligence

Analyzing and Optimizing the Emission Impact of Intersection Signal Control in Mixed Traffic

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