Characterizing the Impact of Autonomous Vehicles on Macroscopic Fundamental Diagrams

Huang, Yi; Ye, Yingjun; Sun, Jian; Tian, Ye

doi:10.1109/tits.2023.3265647

Cited by 11 publications

(3 citation statements)

References 52 publications

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“…Also, the DS pattern of S2 was longer to the right than the DS pattern of S1 (thus, S2 presents higher densities and lower speeds not reached by S1), the DS pattern of S3 (in which the imperfection for driving is at 100%) is below the DS pattern of S2 and presents lower network speeds than S2 for the occurring network densities, and the DS pattern of S3 is longer to the right than the DS pattern of S2 (thus, S3 presents higher densities and lower speeds than S2). When the input flow was 0.1 veh/s per entrance (during the periods range [1,40]) and 0.125 veh/s per entrance (during the periods range [41,80]), we observed comparable network flows and densities (throughout time) among simulations, with S1 presenting higher speeds, followed by S2 and then S3. When the input flow was 0.1666 veh/s per entrance (during the periods range [81,200]) the difference in the network speed, density, and flow between S3 and the rest was notorious, highlighting that in S3, the critical density was reached and exceeded; traffic conditions with drivers driving with total imperfection are not sustainable, since the network gets congested.…”

Section: Discussionmentioning

confidence: 65%

“…The effects of signal coordination on the MFD were investigated in [32], which stated that the impacts of the strategies were sensitive to the signal cycle length chosen (60 s, 90 s, 120 s), and a poor signal coordination reduces the network capacity and the free-flow speed. Other variables that affect the MFD include junction regulation [33], traffic signals [34][35][36], network spatial characteristics [37], buses [38], large-scale activities [39], autonomous vehicles [40][41][42][43], turning traffic [44], rainfall [45], ride pooling [46], bicycle traffic [47], traffic incidents [48], the position of the loop detectors [49], and network heterogeneity [50].…”

Section: Introductionmentioning

confidence: 99%

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Method for the Experimental Identification of Variables and Configurations That Modify the Shape of the Macroscopic Fundamental Diagram

Carrillo-González,

López-Maldonado

2024

Applied Sciences

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In this paper, we propose a method for establishing if a variable is capable of modifying the Macroscopic Fundamental Diagram (MFD) of a street network. The variables have many different configurations, and a simulation is performed for each one. Then, based on the output data of each simulation, the representative speed, density, and flow of the network are calculated. We use three metrics to establish if a variable affects the MFD: the first establishes a distance between the compared density and speed patterns, the second establishes a distance between capacities, and the third establishes a distance between critical densities. We select four variables to test our method: the precision of driving, the vehicles’ top speeds distribution, the procedure for selecting routes, and the procedure for selecting destinations; we determine whether each of these variables can modify the MFD shape. Additionally, we detect which configurations of a variable are able to reach and exceed the critical density (causing congestion) so we can establish which configurations are sustainable and which are not. The novelties of this work are twofold: (1) we introduce a method to detect if a variable modifies the MFD; (2) we establish if the selected variables modify the MFD.

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Section: Discussionmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Method for the Experimental Identification of Variables and Configurations That Modify the Shape of the Macroscopic Fundamental Diagram

Carrillo-González,

López-Maldonado

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Additionally, the introduction of AVs may lead to transformative shifts in traffic dynamics, affecting capacities and congestion patterns [10][11][12][13]. AVs can streamline traffic flow through communication and coordination, potentially optimizing fuel use [14]. However, uncertainties arise regarding the coexistence of AVs with human-driven vehicles, which may impact overall traffic capacities [15].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Paradox of Autonomous Vehicles: Promised Fuel Efficiency vs. Aggregate Fuel Consumption

Mohammed,

Horváth

2024

Energies

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As autonomous vehicles (AVs) continue to evolve and approach widespread adoption in the near future, the touted benefits of improved fuel efficiency at an individual level come under scrutiny when considering the overall impact on fuel consumption. This research delves into the paradoxical relationship between the promising technology of AVs, their impact on traffic capacities, travel demand, and the subsequent influence on aggregate fuel consumption. While AVs have demonstrated enhanced fuel efficiency when considered as a singular mode of transportation, our study reveals a contrasting trend when scaled to a broader societal context. Through comprehensive analysis of the literature, we discovered that, at lower limits of energy savings achievable by a single AV, the overall fuel consumption increases by a staggering 42% compared to conventional human-driven vehicles. This counterintuitive outcome is a result of the aggregate effect of increased AV usage, leading to higher traffic volumes and travel demands. Conversely, at higher thresholds of energy savings by individual AVs, the percentage of fuel consumption increment diminishes, but remains notable. Even with advanced energy-saving features, the overall fuel quantity still experiences a substantial 30% increase compared to conventional vehicles when scaled up to widespread AV use. Our findings emphasize the importance of considering the holistic impact of AVs on transportation systems and energy consumption. As society transitions towards AV-dominated traffic, policymakers and stakeholders must address the challenges associated with increased travel demand, potential traffic congestion, and the resultant implications on fuel consumption.

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Safety performance evaluation of freeway merging areas under autonomous vehicles environment using a co-simulation platform

Chen,

Ni,

Wang

et al. 2024

Accident Analysis & Prevention

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Characterizing the Impact of Autonomous Vehicles on Macroscopic Fundamental Diagrams

Cited by 11 publications

References 52 publications

Method for the Experimental Identification of Variables and Configurations That Modify the Shape of the Macroscopic Fundamental Diagram

Method for the Experimental Identification of Variables and Configurations That Modify the Shape of the Macroscopic Fundamental Diagram

Assessing the Paradox of Autonomous Vehicles: Promised Fuel Efficiency vs. Aggregate Fuel Consumption

Safety performance evaluation of freeway merging areas under autonomous vehicles environment using a co-simulation platform

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