A model to identify urban traffic congestion hotspots in complex networks

Solé-Ribalta, Albert; Gómez, Sergio; Arenas, Àlex

doi:10.1098/rsos.160098

Cited by 47 publications

(36 citation statements)

References 30 publications

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“…These equations correspond to a discrete-time dynamics, in which each time-step represents a day. They are built upon previous work on Microscopic Markov-Chain Approach (MMCA) modelization of epidemic spreading dynamics [22], but which has also been applied to other types of processes, e.g., information spreading and traffic congestion [24,25,32].…”

Section: Epidemic Spreading Modelmentioning

confidence: 99%

A mathematical model for the spatiotemporal epidemic spreading of COVID19

Arenas

Cota

Gómez‐Gardeñes

et al. 2020

Preprint

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164

165

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166 countries/regions, including cases of human-to-human transmission around the world. The proportions of this epidemics is probably one of the largest challenges faced by our interconnected modern societies. According to the current epidemiological reports, the large basic reproduction number, R0 ∼ 2.3, number of secondary cases produced by an infected individual in a population of susceptible individuals, as well as an asymptomatic period (up to 14 days) in which infectious individuals are undetectable without further analysis, pave the way for a major crisis of the national health capacity systems. Recent scientific reports have pointed out that the detected cases of COVID19 at young ages is strikingly short and that lethality is concentrated at large ages. Here we adapt a Microscopic Markov Chain Approach (MMCA) metapopulation mobility model to capture the spread of COVID-19. We propose a model that stratifies the population by ages, and account for the different incidences of the disease at each strata. The model is used to predict the incidence of the epidemics in a spatial population through time, permitting investigation of control measures. The model is applied to the current epidemic in Spain, using the estimates of the epidemiological parameters and the mobility and demographic census data of the national institute of statistics (INE). The results indicate that the peak of incidence will happen in the first half of April 2020 in absence of mobility restrictions. These results can be refined with improved estimates of epidemiological parameters, and can be adapted to precise mobility restrictions at the level of municipalities. The current estimates largely compromises the Spanish health capacity system, in particular that for intensive care units, from the end of March. However, the model allows for the scrutiny of containment measures that can be used for health authorities to forecast with accuracy their impact in prevalence of COVID-19. Here we show by testing different epidemic containment scenarios that we urge to enforce total lockdown to avoid a massive collapse of the Spanish national health system.

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Section: Epidemic Spreading Modelmentioning

confidence: 99%

A mathematical model for the spatiotemporal epidemic spreading of COVID19

Arenas

Cota

Gómez‐Gardeñes

et al. 2020

Preprint

Self Cite

164

165

View full text Add to dashboard Cite

show abstract

“…These equations correspond to a discrete-time dynamics, in which each time-step represents a day. They are built upon previous work on Microscopic Markov-Chain Approach (MMCA) modelization of epidemic spreading dynamics [6], but which has also been applied to other types of processes, e.g., information spreading and traffic congestion [8,9,16].…”

Section: Supplementary Note 1 Epidemic Spreading Modelmentioning

confidence: 99%

Derivation of the effective reproduction number ℛ for COVID-19 in relation to mobility restrictions and confinement

Arenas

Cota

Gómez‐Gardeñes

et al. 2020

Preprint

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This expression for R is an extremely useful tool to design containment policies that are able to suppress the epidemics. We applied our epidemic model for the case of Spain, successfully forecasting both the observed incidence in each region and the overload of the health system. The expression for R allowed us to determine the precise reduction of mobility κ 0 needed to bend the curve of epidemic incidence, which turned out to be κ 0 ∼ 0.7.This value, for the case of Spain, translates to a total lockdown with the exception of the mobility associated to essential services, a policy that was finally enforced on March 28.

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“…Gradually, these theories have been introduced to the field of transportation. More and more scholars have conducted research on the characteristics of various transportation networks, among others those of (urban) road networks (De Montis et al 2007;Erath et al 2009; Barthélemy 2011; Lin and Ban 2013), railway networks (Latora and Marchiori 2002;Sen et al 2003), and transit networks (Guo Xl and Lu 2016;Solé-Ribalta et al 2016). In addition, current studies use complex networks to analyse traffic time series (Tang et al 2013;Yan et al 2017;Bao et al 2017).…”

Section: Traffic On Complex Networkmentioning

confidence: 99%

Estimation of traffic flow changes using networks in networks approaches

Hackl

Adey

2019

Appl Netw Sci

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Understanding traffic flow in urban areas has great importance and implications from an economic, social and environmental point of view. For this reason, numerous disciplines are working on this topic. Although complex network theory made their appearance in transportation research through empirical measures, the relationships between dynamic traffic patterns and the underlying transportation network structures have scarcely been investigated so far. In this work, a novel Networks in Networks (NiN) approach is presented to study changes in traffic flows, caused by topological changes in the transportation network. The NiN structure is a special type of multi-layer network in which vertices are networks themselves. This embedded network structure makes it possible to encode multiple pieces of information such as topology, paths, and origin-destination information, within one consistent graph structure. Since each vertex is an independent network in itself, it is possible to implement multiple diffusion processes with different physical meanings. In this way, it is possible to estimate how the travellers' paths will change and to determine the cascading effect in the network. Using the Sioux Falls benchmark network and a real-world road network in Switzerland, it is shown that NiN models capture both topological and spatial-temporal patterns in a simple representation, resulting in a better traffic flow approximation than single-layer network models.

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A model to identify urban traffic congestion hotspots in complex networks

Cited by 47 publications

References 30 publications

A mathematical model for the spatiotemporal epidemic spreading of COVID19

A mathematical model for the spatiotemporal epidemic spreading of COVID19

Derivation of the effective reproduction number ℛ for COVID-19 in relation to mobility restrictions and confinement

Estimation of traffic flow changes using networks in networks approaches

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