Energy consumption investigation for a new car-following model considering driver’s memory and average speed of the vehicles

Jin, Zhizhan; Yang, Zaili; Ge, Hongxia

doi:10.1016/j.physa.2018.05.034

Cited by 15 publications

(4 citation statements)

References 64 publications

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“…Their findings indicate that this delayed effect effectively mitigates traffic congestion and reduces energy consumption. Jin et al [22], in 2018, built upon the two-velocity differential model (TVDM), incorporating vehicle average speed and driver memory, resulting in an improved car tracking model. Their study showed that this model reduces energy consumption while enhancing traffic flow stability.…”

Section: Introductionmentioning

confidence: 99%

Study on the Emission of Connected Autonomous Vehicle Considering the Control of Electronic Throttle Opening

Kang,

Tian

2024

WEVJ

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Aiming at the networked cruise control scenario of CAV (connected autonomous vehicle) queue, we propose a new networked cruise control strategy for CAV by introducing the average information of ET (electronic throttle) opening of the downstream vehicle group as a feedback signal. By performing linear stability analysis on the new model, we derive its linear stability conditions. Further, we design exhaustive numerical simulation experiments aiming to systematically investigate the effect of the multi-vehicle ahead electronic throttle opening average feedback signal on CAV traffic stability, fuel consumption, and key emission factors, such as CO, HC, and NOx, during the cruise control process. The results show that the feedback signal can not only significantly improve the operational stability of the CAV traffic flow but also significantly improve its fuel consumption and the emission levels of CO, HC, and NOx. When the number of CAV vehicles in the feedback signal is set to three, the levels of CO, HC, and NOx emissions as well as fuel consumption in the road system can reach a stable and optimized state.

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Section: Introductionmentioning

confidence: 99%

Study on the Emission of Connected Autonomous Vehicle Considering the Control of Electronic Throttle Opening

Kang,

Tian

2024

WEVJ

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“…Wei and Yu [37] investigated the relationship between traffic flow stability and energy consumption estimated by many common car-following models and discovered that reducing energy usage is dependent on traffic flow stability. Also, sudden deceleration and acceleration may waste a lot of energy in various traffic scenarios [17,19,31].…”

Section: Introductionmentioning

confidence: 99%

Driver Predictions and Energy Consumption in Car-following Model with BFL Effect

Redhu

2023

Comm. Math. Appl.

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In real life, there is a significant role of neighboring vehicles as well as driver's behavior in the nonlinear dynamics of traffic flow. Based on the car-following model, we examined the impact of individual expectations on a single-lane highway with a Backward-Forward Looking (BFL) effect on traffic flow. The model's stability criterion is determined through linear and nonlinear analysis, and it is observed that the prediction parameter not only reduces the unstable region but is also helpful in reducing energy consumption. Moreover, it is also remarked that the driver's prediction effect will become more effective in the case BFL model. Furthermore, the numerical simulation demonstrates that the new model effectively enhances stable regions and it should be considered during the modeling of traffic flow.

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“…They showed that the effects are related to the green split of the signal light and the vehicle's time headway at the origin. Jin et al [32] proposed a modified car-following model that takes into account the influence of the average speed effect of vehicles and driver's memory on traffic flow basing on two velocity difference model. The time-dependent Ginzburg-Landau equation and the modified Korteweg-de Vries equation were constructed to describe the traffic behaviour near the critical point.…”

Section: Introductionmentioning

confidence: 99%

Effects of Driving Style on Energy Consumption and CO2 Emissions

Carreón-Sierra

Salcido

2022

Coll Dyn

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The tractive force developed by energy consumption in a car engine produces its acceleration and sustains the motion against velocity dependent (aerodynamic and rolling) resistance forces. In internal combustion engines, fuel consumption entails pollutant emissions released into the atmosphere. We studied the effect of driving style on the energy consumption and CO2 emissions in traffic cellular automata (TCA). Extending empirical relationships, we proposed models to estimate energy consumption and pollutant emissions from the TCA velocity and acceleration distributions. We assumed that TCA transition rules represent driving styles and we carried out computer simulations to obtain the velocity and acceleration distributions for different TCA to estimate the energy consumption and CO2 emission rates for the corresponding driving styles. As examples, we considered the Nagel-Schreckenberg (NS) and Fukui-Ishibashi (FI) models, and a variant (NS+FI) defined by combining the NS and FI rules. The FI driving style only revealed energy consumption and CO2 emission rates dependent on the stochastic delay ($p$) for low vehicular densities, and we detected that the larger energy consumption and CO2 emission rates were 45.4 kW and 26.7 g/s with no dependence on p. With NS and NS+FI driving styles, the larger energy consumption and CO2 emission rates occurred for small stochastic delays, 18.4 kW and 6.6 g/s and 61.1 kW and 30.2 g/s for p = 0.2. On average, for the NS, FI, and NS+FI models with $p=0.2$, we obtained energy consumptions of 1.88, 2.60, and 2.76 MJ/km, fuel consumptions of 0.08, 0.12, and 0.13 L/km, and CO2 emissions of 0.158, 0.460, and 0.562 kgCO2/km. Our results agree with those ones of petrol combustion car engines.

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Energy consumption investigation for a new car-following model considering driver’s memory and average speed of the vehicles

Abstract: Energy consumption investigation for a new car-following model considering driver's memory and average speed of the vehicles http:

Cited by 15 publications

References 64 publications

Study on the Emission of Connected Autonomous Vehicle Considering the Control of Electronic Throttle Opening

Study on the Emission of Connected Autonomous Vehicle Considering the Control of Electronic Throttle Opening

Driver Predictions and Energy Consumption in Car-following Model with BFL Effect

Effects of Driving Style on Energy Consumption and CO2 Emissions

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