Average density estimation for urban traffic networks: Application to the Grenoble network

Abstract

This paper deals with the problem of the average density estimation in largescale traffic networks, without requiring to know the density of each individual road. This is achieved by the design of a reduced-order open-loop observer. In general, this type of observers require some specific graph properties, but we show that it is possible to find a virtual representation of the network that satisfies these conditions, by diving each road into a number of cells of specific length. The virtual network is shown to provide close approximations to the average density of the real system for large enough networks. Algorithms to efficiently calculate the observer parameters are proposed. This approach is based on the assumption that traffic dynamics are linear, and is applied at first in free-flow regime. Using microscopic simulations and real data we evaluate the observer performance even when congestion is present in the network.

Optimal Location of EVs Public Charging Stations Based on a Macroscopic Urban Electromobility Model

“…We consider the link EVs flows ϕ i,j (t) as exogenous inputs given by the people mobility model from [11], depending on the number of people wanting to make each trip and a time profile function calibrated from real data (see [13]), coupled with a mode choice model from [10], calibrated with public survey data. The penetration rate η of EVs is defined as the proportion of EVs in the total vehicle flow ϕ V .…”

“…The dynamics of the SoC are given by the energy balance from transported, lost, and PCS-provided energy, between nodes (see [10] for a more detailed derivation),…”

“…They are then inclined to charge at stations with higher utility ratings. This addition is crucial because, without it, drivers would invariably opt to charge whenever their State of Charge (SoC) falls below 1 (as referenced in [10]). This discrete choice modeling approach has been extensively explored in the existing literature, see [15].…”

“…it defines the transition between different systems regimes (as introduced by [10]). Its implications in the systems stability are given in Property 3.…”

“…This paper addresses the issue by presenting an enhanced Electromobility model, which delineates the spatiotemporal distribution of electric vehicles (EVs) and their average State of Charge (SoC) within an urban context. The primary contribution extends the model introduced in [10], establishing a connection between urban mobility patterns, EV flows, energy consumption, and power inputs from both private and public charging stations. The extension in this paper incorporates a driver behavior model in Section II, which calculates charging demand at each public charging station (PCS) based on factors like price, distance, EV SoC, and the attributes of competing PCS.…”