A FIFO Rule Consistent Model for the Continuous Dynamic Network Loading Problem

Castillo, Enrique; Menéndez, José María; Nogal, María; Jimenez, P.; Sánchez-Cambronero, Santos

doi:10.1109/tits.2011.2169668

Cited by 12 publications

(11 citation statements)

References 45 publications

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“…[25]). Relating to the link travel time function, various expressions have been proposed in the literature ranging from linear functions [22] to polynomial functions [26]. Considering the exit function the works of Daganzo [27], Smith [28] and the study conducted by Mahmassani et al [29] can be cited.…”

Section: A Examination Of Dynamic Network Loading Modelsmentioning

confidence: 99%

Modeling Evacuation Risk Using a Stochastic Process Formulation of Mesoscopic Dynamic Network Loading

Gangi¹,

Watling²,

Salvo³

2022

IEEE Trans. Intell. Transport. Syst.

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One of the actions usually conducted to limit exposure to a hazardous event is the evacuation of the area that is subject to the effects of the event itself. This involves modifications both to demand (a large number of users all want to move together) and to supply (the transport network may experience changes in capacity, unusable roads, etc.). In order to forecast the traffic evolution in a network during an evacuation, a natural choice is to adopt an approach based on Dynamic Traffic Assignment (DTA) models. However, such models typically give a deterministic prediction of future conditions, whereas evacuations are subject to considerable uncertainty. The aim of the present paper is to describe an evacuation approach for decision support during emergencies that directly predicts the time-evolution of the probability of evacuating users from an area, formulated within a discrete-time stochastic process modelling framework. The approach is applied to a small artificial case as well as a real-life network, where we estimate users' probabilities to reach a desired safe destination and analyze time dependent risk factors in an evacuation scenario.

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Section: A Examination Of Dynamic Network Loading Modelsmentioning

confidence: 99%

Modeling Evacuation Risk Using a Stochastic Process Formulation of Mesoscopic Dynamic Network Loading

Gangi¹,

Watling²,

Salvo³

2022

IEEE Trans. Intell. Transport. Syst.

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“…The approaches to solve the problem can be classified considering the function on which they are based (exit function or link travel time [28][29][30]) or considering the aggregation level of the assignment model (macroscopic, i.e., [31]; mesoscopic, i.e., [32]; microscopic, i.e., [33]). Relating to the link travel time function, various expressions are proposed in literature ranging from linear functions [30] to polynomial functions [34].…”

Section: Background On Dynamic Network Loadingmentioning

confidence: 99%

A Model to Simulate Multimodality in a Mesoscopic Dynamic Network Loading Framework

Gangi

Polimeni

2017

Journal of Advanced Transportation

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A dynamic network loading (DNL) model using a mesoscopic approach is proposed to simulate a multimodal transport network considering en-route change of the transport modes. The classic mesoscopic approach, where packets of users belonging to the same mode move following a path, is modified to take into account multiple modes interacting with each other, simultaneously and on the same multimodal network. In particular, to simulate modal change, functional aspects of multimodal arcs have been developed; those arcs are properly located on the network where modal change occurs and users are packed (or unpacked) in a new modal resource that moves up to destination or to another multimodal arc. A test on a simple network reproducing a real situation is performed in order to show model peculiarities; some indicators, used to describe performances of the considered transport system, are shown.

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“…The above constraints belong to point queue models. One possibility for physical queue models is to divide links into moving and queuing sections (see Castillo et al [45]).…”

Section: Traffic Flow Dynamicsmentioning

confidence: 99%

“…These relations allow modelling the flow evolution through the network. For example, Castillo et al [45] present a FIFO consistent model for the continuous dynamic network loading problem. The nonlinear link travel time functions are calculated at a finite set of times used to interpolate a monotone cubic spline for all times.…”

Section: Mathematical Programming Based Traffic Assignment Modelsmentioning

confidence: 99%

A State-of-the-Art Review of the Sensor Location, Flow Observability, Estimation, and Prediction Problems in Traffic Networks

et al. 2015

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A state-of-the-art review of flow observability, estimation, and prediction problems in traffic networks is performed. Since mathematical optimization provides a general framework for all of them, an integrated approach is used to perform the analysis of these problems and consider them as different optimization problems whose data, variables, constraints, and objective functions are the main elements that characterize the problems proposed by different authors. For example, counted, scanned or “a priori” data are the most common data sources; conservation laws, flow nonnegativity, link capacity, flow definition, observation, flow propagation, and specific model requirements form the most common constraints; and least squares, likelihood, possible relative error, mean absolute relative error, and so forth constitute the bases for the objective functions or metrics. The high number of possible combinations of these elements justifies the existence of a wide collection of methods for analyzing static and dynamic situations.

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A FIFO Rule Consistent Model for the Continuous Dynamic Network Loading Problem

Cited by 12 publications

References 45 publications

Modeling Evacuation Risk Using a Stochastic Process Formulation of Mesoscopic Dynamic Network Loading

Modeling Evacuation Risk Using a Stochastic Process Formulation of Mesoscopic Dynamic Network Loading

A Model to Simulate Multimodality in a Mesoscopic Dynamic Network Loading Framework

A State-of-the-Art Review of the Sensor Location, Flow Observability, Estimation, and Prediction Problems in Traffic Networks

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