Dynamic traffic-aware auction-based signal control under vehicle to infrastructure communication

Iliopoulou, Christina; Kampitakis, Emmanouil; Κεπαπτσόγλου, Κωνσταντίνος; Vlahogianni, Eleni I.

doi:10.1016/j.physa.2022.128258

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…In [27], the authors' goal was to minimize the personal delay of drivers through an auction for green time. In [28], the authors used a second-price marked approach for auctions. The importance of such auctions is that drivers are able to define their Value of Time (VOT), as in [29,30].…”

Section: Traffic Lights and Intersection Managementmentioning

confidence: 99%

Exploiting Traffic Light Coordination and Auctions for Intersection and Emergency Vehicle Management in a Smart City Mixed Scenario

Muzzini,

Montangero

2024

Sensors

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IoT (Internet-of-Things)-powered devices can be exploited to connect vehicles to smart city infrastructure, allowing vehicles to share their intentions while retrieving contextual information about diverse aspects of urban viability. In this paper, we place ourselves in a transient scenario in which next-generation vehicles that are able to communicate with the surrounding infrastructure coexist with traditional vehicles with limited or absent IoT capabilities. We focus on intersection management, in particular on reusing existing traffic lights empowered by a new management system. We propose an auction-based system in which traffic lights are able to exchange contextual information with vehicles and other nearby traffic lights with the aim of reducing average waiting times at intersections and consequently overall trip times. We use bid propagation to improve standard vehicle trip times while allowing emergency vehicles to free up the way ahead without needing ad hoc system for such vehicle, only an increase in their budget. The proposed system is then tested against two baselines: the classical Fixed Time Control system currently adopted for traffic lights, and an auction strategy that does not exploit traffic light coordination. We performed a large set of experiments using the well known MATSim transport simulator on both a synthetic Manhattan map and on a map we built of an urban area located in Modena, Northern Italy. Our results show that the proposed approach performs better than the classical fixed time control system and the auction strategy that does not exploit coordination among traffic lights.

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Section: Traffic Lights and Intersection Managementmentioning

confidence: 99%

Exploiting Traffic Light Coordination and Auctions for Intersection and Emergency Vehicle Management in a Smart City Mixed Scenario

Muzzini,

Montangero

2024

Sensors

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“…In order to reflect the relationship between the traffic time and the density of each road segment and other factors, this paper adopts the road resistance function method. The road resistance function is often calculated according to the following formula, calculated by Equation (4) [22]:…”

Section: Road Resistance Functionmentioning

confidence: 99%

Site Selection and Capacity Determination of Electric Hydrogen Charging Integrated Station Based on Voronoi Diagram and Particle Swarm Algorithm

Tian,

Yang,

et al. 2024

Energies

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In response to challenges in constructing charging and hydrogen refueling facilities during the transition from conventional fuel vehicles to electric and hydrogen fuel cell vehicles, this paper introduces an innovative method for siting and capacity determination of Electric Hydrogen Charging Integrated Stations (EHCIS). In emphasizing the calculation of vehicle charging and hydrogen refueling demands, the proposed approach employs the Voronoi diagram and the particle swarm algorithm. Initially, Origin–Destination (OD) pairs represent car starting and endpoints, portraying travel demands. Utilizing the traffic network model, Dijkstra’s algorithm determines the shortest path for new energy vehicles, with the Monte Carlo simulation obtaining electric hydrogen energy demands. Subsequently, the Voronoi diagram categorizes the service scope of EHCIS, determining the equipment capacity while considering charging and refueling capabilities. Furthermore, the Voronoi diagram is employed to delineate the EHCIS service scope, determine the equipment capacity, and consider distance constraints, enhancing the rationality of site and service scope divisions. Finally, a dynamic optimal current model framework based on second-order cone relaxation is established for distribution networks. This framework plans each element of the active distribution network, ensuring safe and stable operation upon connection to EHCIS. To minimize the total social cost of EHCIS and address the constraints related to charging equipment and hydrogen production, a siting and capacity model is developed and solved using a particle swarm algorithm. Simulation planning in Sioux Falls city and the IEEE33 network validates the effectiveness and feasibility of the proposed method, ensuring stable power grid operation while meeting automotive energy demands.

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Section: Road Resistance Functionmentioning

confidence: 99%

Site Selection and Capacity Determination of Electric-Hydrogen Charging Integrated Station Based on Voronoi Diagram and Particle Swarm Algorithm

Tian,

Yang,

et al. 2023

Preprint

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In view of the problem of charging and hydrogen filling facilities construction in the transition from fuel vehicles to electric vehicles and hydrogen fuel cell vehicles, in order to meet the electric energy demand of electric vehicles and hydrogen energy demand of hydrogen fuel cell vehicles at the same time, this paper puts forward a method of siting and capacity determination of electric-hydrogen refueling integrated station (EHRIS) based on Voronoi diagram and particle swarm algorithm based on calculating the demand of charging and filling of hydrogen in vehicles. Firstly, OD pair(Origin-Destination) is used to represent the starting point and end point of the car to portray the travel demand of the car, and on the basis of the traffic network model, the shortest driving path of the new energy car is determined by Dijkstra algorithm, and Monte Carlo simulation is used to get the electric-hydrogen energy demand of the car; secondly, the Voronoi diagram is introduced to classify the service scope of the electric-hydrogen charging station to determine the equipment capacity of the electric-hydrogen charging station, while taking into account the electric-hydrogen charging and refueling capacity. Secondly, a Voronoi diagram is introduced to divide the service scope of the EHRIS, determine the capacity of the equipment in the EHRIS, and consider the distance constraints between the sites of EHRIS, so as to make the division of the site and service scope more reasonable. Finally, a dynamic optimal current model framework for distribution networks based on second-order cone relaxation is established, and each element of the active distribution network is planned, so that the distribution network can operate safely and stably after being connected to the EHRIS. With the objective of minimizing the total social cost of EHRIS and considering the constraints of the charging equipment and hydrogen production and injection equipment of the EHRIS, a siting and capacity model to meet the electric-hydrogen energy demand of electric and hydrogen fuel cell vehicles is developed, and solved by a particle swarm algorithm. Finally, the simulation planning is carried out with Sioux Falls city and IEEE33 network, which ensures the stable operation of the power grid while meeting the energy demand of automobiles, and the results verify the effectiveness and feasibility of the proposed method.

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Dynamic traffic-aware auction-based signal control under vehicle to infrastructure communication

Cited by 6 publications

References 36 publications

Exploiting Traffic Light Coordination and Auctions for Intersection and Emergency Vehicle Management in a Smart City Mixed Scenario

Exploiting Traffic Light Coordination and Auctions for Intersection and Emergency Vehicle Management in a Smart City Mixed Scenario

Site Selection and Capacity Determination of Electric Hydrogen Charging Integrated Station Based on Voronoi Diagram and Particle Swarm Algorithm

Site Selection and Capacity Determination of Electric-Hydrogen Charging Integrated Station Based on Voronoi Diagram and Particle Swarm Algorithm

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