Stability Analysis of Connected and Automated Vehicles to Reduce Fuel Consumption and Emissions

Qin, Yanyan; Wang, Hao

doi:10.1061/jtepbs.0000196

Cited by 43 publications

(20 citation statements)

References 49 publications

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“…Moreover, it is indicated that the rear-end crashes often occurred on the straight roads [27]. Therefore, the simulation segment is assumed to be a onelane highway with an on-ramp located in the middle of the segment [28]- [30], as shown in Figure 2. All parameters needed for simulations are set according to the recent studies [28]- [30], the length of the simulation segment is 6.5km, the mainline flow is set as 1800veh/hr, and the ramp flow is 360veh/hr.…”

Section: A Simulation Resultsmentioning

confidence: 99%

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Rear-End Crash Risk of CACC-Manual Driven Mixed Flow Considering the Degeneration of CACC Systems

Qin

Zhao

2019

IEEE Access

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The influence of (cooperative) adaptive cruise control (CACC/ACC) on rear-end crashes have been conducted by some previous studies. However, they usually did not consider the degeneration from stable CACC to unstable ACC when vehicle-to-vehicle (V2V) communication is not available. California PATH program validated the instability of ACC system by using real experimental data, which might have negative impacts on safety. Therefore, this paper evaluated the impacts of degeneration of CACC vehicles on rear-end crash risks. A micro-simulation based method was adopted to evaluate rear-end crash risks with the increase of the CACC market penetration rate (MPR). The results obtained were quite different from those in previous studies and showed that the rear-end crash risks of the full MDV flow increase before CACC MPR increases to a certain level. It was found that the instability of ACC vehicles might be the main factor for the negative results. The results of this paper provide helpful insight into the management of CACC vehicular flow in the V2V communication environment. INDEX TERMS Rear-end crash risks, cooperative adaptive cruise control, adaptive cruise control, surrogate safety measure, car-following model.

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Section: A Simulation Resultsmentioning

confidence: 99%

“…Simulations here belong to the category of numerical simulations using car-following models. Therefore, we should provide traffic volumes for the mainline and on-ramp and the geometric characteristics of the simulation road to obtain safety evaluation results [28]- [30], all of which are described above.…”

Section: A Simulation Resultsmentioning

confidence: 99%

Rear-End Crash Risk of CACC-Manual Driven Mixed Flow Considering the Degeneration of CACC Systems

Qin

Zhao

2019

IEEE Access

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“…Some specific models have been proposed to reflect CAVs driving behavior. For instance, in [46], a uniform local platoon for stability analysis of CAV mixed-flow was proposed. It was simulated on a highway segment.…”

Section: Simulation Of Cavs Operationmentioning

confidence: 99%

Potential Pollutant Emission Effects of Connected and Automated Vehicles in a Mixed Traffic Flow Context for Different Road Types

Bandeira¹,

Macedo

Fernandes

et al. 2021

IEEE Open J. Intell. Transp. Syst.

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The environmental impact of connected and autonomous vehicles (CAVs) is still uncertain. Little is known about how CAVs operational behavior influences the environmental performance of network traffic, including conventional vehicles (CVs). In this paper, a microscopic traffic and emission modeling platform was applied to simulate CAVs operation in Motorway, Rural, and Urban road sections of a medium-sized European city, assuming different configurations of the car-following model parameters associated with a pre-determined or cooperative adaptative behavior of the CAVs. The main contribution is to evaluate the impact of the CAVs operation on the distribution of accelerations, Vehicle Specific Power (VSP) modal distribution, carbon dioxide (CO2) and nitrogen oxides (NOx) emissions for different road types and Market Penetration Rates (MPR). Results suggest CAVs operational behavior can affect CVs environmental performance either positively or negatively, depending on the driving settings and road type. It was found network-wide CO2 varies between savings of 18% and an increase of 4%, depending on the road type and MPR. CAVs adjusted driving settings allowed minimization of system NOx up to 13-23% for MPR ranging between 10 and 90%. These findings may support policymakers and traffic planners in developing strategies to better accommodate CAVs in a sustainable way.

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“…Over the past decades, most of the studies focused on the dynamic evolutions of human-driving traffic flow, barely considering the dynamic evolutions of human-driving vehicles equipped with V2V communication technology in mixed traffic flow [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

An Asymmetric-Anticipation Car-following Model in the Era of Autonomous-Connected and Human-Driving Vehicles

Jafaripournimchahi

Sun

2020

Journal of Advanced Transportation

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Herein, we explored the impact of anticipation and asymmetric driving behavior on vehicle’s position, velocity, acceleration, energy consumption, and exhaust emissions of CO, HC, and NOx in mixed traffic flow. We present an asymmetric-anticipation car-following model (AAFVD) considering the motion information from two direct preceding vehicles (i.e., human-driving (HD) and autonomous and connected (AC) vehicles platoon) via wireless data transmission. The linear stability approach was used to evaluate the properties of the AAFVD model. Our simulations revealed that the drivers’ anticipation factor using the motion information from two direct preceding vehicles in connected vehicles environment can effectively improve traffic flow stability. The vehicle’s departure and arrival process while passing through a signal lane with a traffic light considering the anticipation and asymmetric driving behavior, and the motion information from two direct preceding vehicles was explored. Our numerical results demonstrated that the AAFVD model can decrease the velocity fluctuations, energy consumption, and exhaust emissions of vehicles in mixed traffic flow system.

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Stability Analysis of Connected and Automated Vehicles to Reduce Fuel Consumption and Emissions

Cited by 43 publications

References 49 publications

Rear-End Crash Risk of CACC-Manual Driven Mixed Flow Considering the Degeneration of CACC Systems

Rear-End Crash Risk of CACC-Manual Driven Mixed Flow Considering the Degeneration of CACC Systems

Potential Pollutant Emission Effects of Connected and Automated Vehicles in a Mixed Traffic Flow Context for Different Road Types

An Asymmetric-Anticipation Car-following Model in the Era of Autonomous-Connected and Human-Driving Vehicles

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