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

Li, Min; Luo, Dijia; Liu, Bilong; Zhang, Xilong; Liu, Zhen; Li, Mengshan

doi:10.3390/su141610065

Cited by 8 publications

(5 citation statements)

References 39 publications

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“…A recent study employed a fuzzy controller with a multi-objective differential evolution algorithm to implement coordinated control in an urban traffic network model, aiming to reduce the average vehicle delay [18]. Another study proposed a coordinated control optimization method based on an asymmetrical multiband model, incorporating phase optimization to minimize vehicle delays and exhaust gas emissions [19]. For the optimization of traffic signal control parameters, several different optimization methods have been utilized.…”

Section: Traffic Control and Optimization Methodsmentioning

confidence: 99%

Optimal Traffic Signal Control Using Priority Metric Based on Real-Time Measured Traffic Information

et al. 2023

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Optimizing traffic control systems at traffic intersections can reduce network-wide fuel consumption as well as improve traffic flow. While traffic signals have conventionally been controlled based on predetermined schedules, various adaptive control systems have been developed recently using advanced sensors such as cameras, radars, and LiDARs. By utilizing rich traffic information enabled by the advanced sensors, more efficient or optimal traffic signal control is possible in response to varying traffic conditions. This paper proposes an optimal traffic signal control method to minimize network-wide fuel consumption utilizing real-time traffic information provided by advanced sensors. This new method employs a priority metric calculated by a weighted sum of various factors, including the total number of vehicles, total vehicle speed, vehicle waiting time, and road preference. Genetic Algorithm (GA) is used as a global optimization method to determine the optimal weights in the priority metric. In order to evaluate the effectiveness of the proposed method, a traffic simulation model is developed in a high-fidelity traffic simulation environment called SUMO, based on a real-world traffic network. The traffic flow within this model is simulated using actual measured traffic data from the traffic network, enabling a comprehensive assessment of the novel optimal traffic signal control method in realistic conditions. The simulation results show that the proposed priority metric-based real-time traffic signal control algorithm can significantly reduce network-wide fuel consumption compared to the conventional fixed-time control and coordinated actuated control methods that are currently used in the modeled network. Additionally, incorporating truck priority in the priority metric leads to further improvements in fuel consumption reduction.

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Section: Traffic Control and Optimization Methodsmentioning

confidence: 99%

Optimal Traffic Signal Control Using Priority Metric Based on Real-Time Measured Traffic Information

et al. 2023

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“…TRANSIMS is also equipped with an additional module that is used to evaluate vehicle emissions from the model. It allows the estimation of THC, CO, and NO x [149].…”

Section: Traffic Simulation Tools and Exhaust Emissionsmentioning

confidence: 99%

Vehicle Emission Models and Traffic Simulators: A Review

Mądziel

2023

Energies

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Accurate estimations and assessments of vehicle emissions can support decision-making processes. Current emission estimation tools involve several calculation methods that provide estimates of the exhaust components that result from driving on urban arterial roads. This is an important consideration, as the emissions generated have a direct impact on the health of pedestrians near the roads. In recent years, there has been an increase in the use of emission models, especially in combination with traffic simulator models. This is because it is very difficult to obtain an actual measurement of road emissions for all vehicles travelling along the analysed road section. This paper concerns a review of selected traffic simulations and the estimation of exhaust gas components models. The models presented have been aggregated into a group with respect to their scale of accuracy as micro, meso, and macro. This paper also presents an overview of selected works that combine both traffic and emission models. The presented literature review also emphasises the proper calibration process of simulation models as the most important factor in obtaining accurate estimates. This work also contains information and recommendations on modelling that may be helpful in selecting appropriate emission estimation tools to support decision-making processes for, e.g., road managers.

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“…TRANSIMS is also equipped with an additional module that is used to evaluate vehicle emissions from the model. It allows the estimation of THC, CO, and NOx [131].…”

Section: Traffic Simulation Tools and Exhaust Emissionsmentioning

confidence: 99%

Vehicle Emission Models and Traffic Simulators: A Review

Mądziel¹

2023

Preprint

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Accurate estimation of vehicle emissions is essential and can be helpful in the decision-making process. Thanks to calculation methods, it is possible to accurately estimate the emissions that result from driving a car and to determine their impact on, for example, the health of pedestrians who are on the pavement near an arterial road. Recent years have seen an increase in the use and importance of emission models, as well as vehicle traffic simulation models. Increasingly, emission models are being combined with traffic simulation models, as it is not practical to actually measure the on-road emissions of an entire fleet of vehicles over a given stretch of road. This paper provides an overview of the selected work on the topic of emission modelling and vehicle traffic simulation models. The models are distinguished according to their respective scales of accuracy, i.e., micro, meso, and macro. Selected work combining emission and traffic simulation models is also presented, as well as development trends in the field. In particular, the paper also highlights the fact of proper calibration of vehicle traffic simulation models in order to obtain the most reliable vehicle emissions results from computational models. The review of the works carried out may be helpful for future projects concerning the use of simulation tools for transport and environmental decision-makers in the area of arterial roads.

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

Cited by 8 publications

References 39 publications

Optimal Traffic Signal Control Using Priority Metric Based on Real-Time Measured Traffic Information

Optimal Traffic Signal Control Using Priority Metric Based on Real-Time Measured Traffic Information

Vehicle Emission Models and Traffic Simulators: A Review

Vehicle Emission Models and Traffic Simulators: A Review

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