Models, algorithms, and equilibrium conditions for the simulation of autonomous vehicles in exclusive and mixed traffic

Gallo, Mariano

doi:10.1016/j.simpat.2023.102838

Cited by 4 publications

(6 citation statements)

References 44 publications

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“…Gallo [10] proposed a model and solution methods for optimising the routes of centrally controlled autonomous vehicles in both exclusive and mixed traffic hypotheses.…”

Section: Autonomous Vehiclesmentioning

confidence: 99%

“…where Equation ( 9) is the constraint that relates link flows, f , to demand, d, supply, A, and path choice percentage, ψ. Equation (10) expresses the relationship between costs, flows, and signal settings; Equations ( 11) and ( 12) constrain the decision variables to their feasibility sets. Substituting Equation ( 9) into Equation ( 10) and then into Equation ( 8), the model can be written as follows:…”

Section: Model Formulationmentioning

confidence: 99%

“…This appendix contains tables showing some data about the test networks. 3 [4] 4 [3] 2.0 1600 50 9 [10] 3 [12] 12 [3] 5.0 5400 100 11 [12] 4 [5] 5 [4] 2.0 1600 50 13 [14] 4 [11] 11 [4] 5.0 1600 50 15 [16] 5 [6] 6 [5] 2.0 1600 50 17 [18] 5 [9] 9 [5] 2.5 3500 75 19 [20] 6 [8] 8 [6] 2.5 3500 75 21 [22] 7 [8] 8 [7] 3.0 3500 75 23 [24] 7 [18] 18 [7] 2.5 1600 50 25 [26] 8 [9] 9 [8] 2.0 3500 75 27 [28] 8 [16] 16 [8] 2.5 3500 75 29 [30] 9 [ [40] 11 [12] 12 [11] 2.0 3500 75 41 [42] 11 [14] 14 [11] 3.0 3500 75 43 [44] 12 [13] 13 [12] 6.0 5400 100 45 [46] 13 [24] 24 [13] 2.0 3500 75 47 [48] 14 …”

Section: Appendix Bmentioning

confidence: 99%

“…In urban areas, it is also possible to add centralised management of the vehicle fleet to full driving automation; it is assumed that a control centre plans the routes of all vehicles on the road network to optimise the total travel time to obtain the system optimum. A simulation of this type of approach was proposed in [10].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

Gallo

2024

Smart Cities

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In the near future, fully autonomous vehicles may revolutionise mobility and contribute to the development of the smart city concept. In this work, we assume that vehicles are not only fully autonomous but also centrally controlled by a single operator, who can also define the traffic light control parameters at intersections. With the aim of optimising the system to achieve a global optimum, the operator can define both the routes of the fleet of vehicles and the traffic light control parameters. This paper proposes a model for the joint optimisation of traffic light control parameters and autonomous vehicle routes to achieve the system optimum. The model, which is solved using a gradient algorithm, is tested on networks of different sizes. The results obtained show the validity of the proposed approach and the advantages of centralised management of vehicles and intersection control parameters.

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“…Gallo [10] proposed a model and solution methods for optimising the routes of centrally controlled autonomous vehicles in both exclusive and mixed traffic hypotheses.…”

Section: Autonomous Vehiclesmentioning

confidence: 99%

Section: Model Formulationmentioning

confidence: 99%

Section: Appendix Bmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

Gallo

2024

Smart Cities

Self Cite

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“…The results show that the confidence level is not affected by changes in the AV penetration rate. It also reveals that the interaction between these two vehicle types becomes stronger as AV penetration approaches 50% [26,27]. In terms of simulation, we propose a model and an algorithm for estimating the traffic flow generated by the centralized management of automated vehicles under exclusive and mixed traffic conditions.…”

Section: Introductionmentioning

confidence: 99%

Impact of Autonomous Vehicles on Traffic Flow in Rural and Urban Areas Using a Traffic Flow Simulator

Fujiu,

Morisaki,

Takayama

2024

Sustainability

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Autonomous vehicles have the potential to significantly improve modes of transportation, and many businesses and research facilities are developing such systems. Although there are studies on the social implementation of autonomous vehicles, these studies are based on limited conditions such as predetermined driving environments. Therefore, in this study, we target urban areas and rural areas, and we simulate a behavioral algorithm for autonomous vehicles being developed and owned by Kanazawa University. In this study, a traffic flow simulation system (Aimsun) was constructed to reproduce the current situation of traffic flow in the city during normal times, using data from a person-trip survey conducted by the local government. In addition, we varied the mixing rate of automated vehicles and evaluated its effect on the delay time between ODs. We assume the gradual replacement of existing vehicles by autonomous vehicles on actual road networks and for realistic traffic volumes, and we investigate their impact on traffic flow. We vary the mixing rate of autonomous vehicles into actual traffic environments, and we measure the delay in the origin-destination (OD) interval to evaluate the impact of autonomous vehicles on traffic flow. The results obtained show that as the mixing rate of autonomous vehicles increases, the delay between OD intervals increases. Then, once the mixing rate exceeds a certain value, the delay between OD intervals gradually decreased. The delay time for all vehicles slightly increases as the mixing rate of autonomous vehicles increased from 10 to 45%. When the mixing rate increased from 45 to 50%, the delay time for all vehicles decreased notably, and when the mixing rate was 50 to 100%, it remained constant. Analytical results showed that when socially implementing autonomous vehicles, their mixing rate impacts the traffic flow; thus, there is a need to determine appropriate distribution scenarios and areas for implementation.

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Traffic flow impact of mixed heterogeneous platoons on highways: an approach combining driving simulation and microscopic traffic simulation

Ren,

Zhao,

et al. 2024

Physica A: Statistical Mechanics and its Applications

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Models, algorithms, and equilibrium conditions for the simulation of autonomous vehicles in exclusive and mixed traffic

Cited by 4 publications

References 44 publications

Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

Combined Optimisation of Traffic Light Control Parameters and Autonomous Vehicle Routes

Impact of Autonomous Vehicles on Traffic Flow in Rural and Urban Areas Using a Traffic Flow Simulator

Traffic flow impact of mixed heterogeneous platoons on highways: an approach combining driving simulation and microscopic traffic simulation

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