Emission Effects of Cooperative Adaptive Cruise Control: A Simulation Case Using SUMO

Erdağı, İsmet Gökşad; Silgu, Mehmet Ali; Çelikoğlu, Hilmi Berk

doi:10.29007/fbb7

Cited by 18 publications

(10 citation statements)

References 19 publications

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“…The studies focused on exploring different penetration rates of CACC varying from 0-100% in 25% increments in two scenarios with intersections regulated by fixed and adaptive traffic light control. The maximum GHG emission reduction reported in the study was 20% [20].…”

Section: Related Workmentioning

confidence: 72%

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Simulation-Based Impact of Connected Vehicles in Platooning Mode on Travel Time, Emissions and Fuel Consumption

Validi,

Olaverri-Monreal

2021

Preprint

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Several approaches have been presented during the last decades to reduce carbon pollution from transportation. One example is the use of platooning mode. This paper considers data obtained from daily trips to investigate the impact of platooning on travel time, emissions of CO2, CO, NOx and HC and fuel consumption on a road network in Upper Austria. For this purpose, we studied fuel combustion-based engines relying on the extension of the 3DCoAutoSim simulation platform. The obtained results showed that the platooning mode not only increased driving efficiency but also decreased the total emissions by reducing fuel consumption.

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Section: Related Workmentioning

confidence: 72%

“…In further studies relying on the simulation of urban mobility SUMO, the effects of platooning on the generation of emissions were investigated in different networks by applying Cooperative Adaptive Cruise Control (CACC) [20], [21]. The authors relied on the combination of OMNet++ and Veins.…”

Section: Related Workmentioning

confidence: 99%

Simulation-Based Impact of Connected Vehicles in Platooning Mode on Travel Time, Emissions and Fuel Consumption

Validi,

Olaverri-Monreal

2021

Preprint

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“…These studies use SUMO to incorporate the effect of infrastructure and traffic dynamics on the consumption. Grumert et al (2015) and Erdağı et al (2019) go one step further, not merely focusing on the energy used, but also taking the emissions into account. The Krauss model was used for these investigations, which result in realistic velocities and traffic flow, but may produce unrealistic accelerations and therefore emissions.…”

Section: Introductionmentioning

confidence: 99%

Extending the Intelligent Driver Model in SUMO and Verifying the Drive Off Trajectories with Aerial Measurements

Salles

Kaufmann²,

Reuss

2022

SUMO Conf Proc

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Connected and automated driving functions are key components for future vehicles. Due to implementation issues and missing infrastructure, the impact of connected and automated vehicles on the traffic flow can only be evaluated in accurate simulations. Simulation of Urban Mobility (SUMO) provides necessary and appropriate models and tools. SUMO contains many car-following models that replicate automated driving, but cannot realistically imitate human driving behavior. When simulating queued vehicles driving off, existing car-following models are neither able to correctly emulate the acceleration behavior of human drivers nor the resulting vehicle gaps. Thus, we propose a time-discrete 2D Human Driver Model to replicate realistic trajectories. We start by combining previously published extensions of the Intelligent Driver Model (IDM) to one generalized model. Discontinuities due to introduced reaction times, estimation errors and lane changes are conquered with new approaches and equations. Above all, the start-up procedure receives more attention than in existing papers. We also provide a first evaluation of the advanced car-following model using 30 minutes of an aerial measurement. This dataset contains three hours of drone recordings from two signalized intersections in Stuttgart, Germany. The method designed for extracting the vehicle trajectories from the raw video data is outlined. Furthermore, we evaluate the accuracy of the trajectories obtained by the aerial measurement using a specially equipped vehicle.

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“…Song et al [28] proposed a dynamic vehicle path guidance model based on global adaptive optimization scheduling under the Internet of vehicles. Erdagı et al [29] created two hypothetical test networks with different levels of complexity, in which total time spent and total emission were considered to find an optimum penetration rate of CACC in urban road. Yao et al [30] established a method for the stability of mixed traffic flow and obtained the fundamental diagram model under different penetration rates of CAVs.…”

Section: Introductionmentioning

confidence: 99%

Variable Speed Limit Control Method of Freeway Mainline in Intelligent Connected Environment

Wang

Zhang

Gou

et al. 2021

Journal of Advanced Transportation

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Freeway is an important component of transportation system. Bottleneck areas on freeway reduce driving safety and traffic efficiency. The development of intelligent connected technology provides a new idea for traffic management. In order to alleviate traffic congestion on the freeway bottleneck area, this paper proposes a variable speed limit (VSL) control method in intelligent connected environment. In this paper, the METANET model is improved by combining intelligent connected environment and VSL control theory. The total traffic capacity (TTC), total travel time (TTT), and total speed difference (TSD) are used to build multiobjective function. The microsimulation at SUMO by using the data from PeMS is employed as a case study to validate the proposed model. The results show that the VSL online control method in intelligent connected environment has better control effect. And the improvement is more obvious with increasing penetration rate of intelligent connected vehicle (ICV).

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Emission Effects of Cooperative Adaptive Cruise Control: A Simulation Case Using SUMO

Cited by 18 publications

References 19 publications

Simulation-Based Impact of Connected Vehicles in Platooning Mode on Travel Time, Emissions and Fuel Consumption

Simulation-Based Impact of Connected Vehicles in Platooning Mode on Travel Time, Emissions and Fuel Consumption

Extending the Intelligent Driver Model in SUMO and Verifying the Drive Off Trajectories with Aerial Measurements

Variable Speed Limit Control Method of Freeway Mainline in Intelligent Connected Environment

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