A simple contagion process describes spreading of traffic jams in urban networks

Saberi, Meead; Ashfaq, Mudabber; Hamedmoghadam, Homayoun; Hosseini, Seyed Amir; Gu, Ziyuan; Shafiei, Sajjad; Nair, Divya Jayakumar; Dixit, Vinayak; Gardner, Lauren; Waller, S. Travis; González, Marta C.

doi:10.1038/s41467-020-15353-2

Cited by 115 publications

(62 citation statements)

References 30 publications

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“…The changes in performance at the onset of, and during the disruption, depend on the specific transport mode and disruption. On most road and pedestrian networks, limitations occur exclusively at links or nodes (prohibiting passage, restricting speed or flow 30 ). Routes (sequences of consecutive links) can be affected if any of their links is affected.…”

Section: Introductionmentioning

confidence: 99%

“…In the Swiss network, the planned timetable and infrastructure usage of all passenger trains remained the same, with no significant cancellations or adjustments. Nevertheless, the short turning of the long-distance international trains resulted in smaller variability of actual operations, and less entrance delays when entering into the Swiss network at Basel, which hints at a reduced epidemic spreading of delays in a network 30 , 36 .…”

Section: Introductionmentioning

confidence: 99%

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Empirical dynamics of railway delay propagation identified during the large-scale Rastatt disruption

Büchel

Spanninger

Corman

2020

Sci Rep

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Transport networks are becoming increasingly large and interconnected. This interconnectivity is a key enabler of accessibility; on the other hand, it results in vulnerability, i.e. reduced performance, in case any specific part is subject to disruptions. We analyse how railway systems are vulnerable to delay, and how delays propagate in railway networks, studying real-life delay propagation phenomena on empirical data, determining real-life impact and delay propagation for the uncommon case of railway disruptions. We take a unique approach by looking at the same system, in two different operating conditions, to disentangle processes and dynamics that are normally present and co-occurring in railway operations. We exploit the unique chance to observe a systematic change in railway operations conditions, without a correspondent system change of infrastructure or timetable, coming from the occurrence of the large-scale disruption at Rastatt, Germany, in 2017. We define new statistical methods able to detect weak signals in the noisy dataset of recorded punctuality for passenger traffic in Switzerland, in the disrupted and undisrupted state, along a period of 1 year. We determine how delay propagation changed, and quantify the heterogeneous, large-scale cascading effects of the Rastatt disruption towards the Swiss network, hundreds of kilometers away. Operational measures of transport performance (i.e. punctuality and delays), while globally being very decreased, had a statistically relevant positive increase (though very geographically heterogeneous) on the Swiss passenger traffic during the disruption period. We identify two factors for this: (1) the reduced delay propagation at an international scale; and (2) to a minor extent, rerouted railway freight traffic; which show to combine linearly in the observed outcomes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Empirical dynamics of railway delay propagation identified during the large-scale Rastatt disruption

Büchel

Spanninger

Corman

2020

Sci Rep

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“…That is because the functions are usually not continuous or differentiable 39 . To efficiently estimate the parameters in the proposed model, we applied a global pattern search algorithm 40 as a derivative-free numerical optimization method to fit the curves for each variable (i.e., f (t) , e(t) , c(t) , and r(t)) 26 . The objective function in this optimization process is the RMSE, which will be minimized by searching for the optimal propagation rate β , recovery rate µ , and exposed rate α .…”

Section: Study Context and Data Collectionmentioning

confidence: 99%

“…Recognizing the limitations of existing models, there is a real need for mathematical models that can capture the spatial-temporal evolution of flooding without relying on a variety of input parameters and historical data such as the volume of waters and the width of the roads. Recent studies have demonstrated a surprisingly significant similarity among spreading processes in different systems, including the spread of traffic congestion in transportation, the contagion of infectious disease in populations, the diffusion of ideas in social networks 25 , as well as the evolution of flooding in urban road networks 26,27 . Motivated by these studies, our goal in this research was to describe the floodwater spreading process using generalized mathematical contagion models, such as classical epidemic models 28 .…”

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confidence: 99%

A network percolation-based contagion model of flood propagation and recession in urban road networks

Fan

Jiang

Mostafavi

2020

Sci Rep

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in this study, we propose a contagion model as a simple and powerful mathematical approach for predicting the spatial spread and temporal evolution of the onset and recession of floodwaters in urban road networks. A network of urban roads resilient to flooding events is essential for the provision of public services and for emergency response. The spread of floodwaters in urban networks is a complex spatial-temporal phenomenon. this study presents a mathematical contagion model to describe the spatial-temporal spread and recession process of floodwaters in urban road networks. The evolution of floods within networks can be captured based on three macroscopic characteristicsflood propagation rate (β), flood incubation rate (α), and recovery rate (µ)-in a system of ordinary differential equations analogous to the Susceptible-Exposed-Infected-Recovered (SEIR) model. We integrated the flood contagion model with the network percolation process in which the probability of flooding of a road segment depends on the degree to which the nearby road segments are flooded. The application of the proposed model is verified using high-resolution historical data of road flooding in Harris County during Hurricane Harvey in 2017. The results show that the model can monitor and predict the fraction of flooded roads over time. Additionally, the proposed model can achieve 90% precision and recall for the spatial spread of the flooded roads at the majority of tested time intervals. The findings suggest that the proposed mathematical contagion model offers great potential to support emergency managers, public officials, citizens, first responders, and other decision-makers for flood forecast in road networks. Given the essential role transportation plays in emergency response, provision of essential services, and maintenance of economic well-being 1 , the resilience of urban road networks to natural hazards, especially flooding events, has received increasing attention 2, 3. Floodwaters in urban networks propagate over time and space, inducing a great deal of spatial-temporal uncertainty visa -vis protective actions, such as evacuation, and rapid emergency response 4. Developing effective prediction tools to forecast the characteristics of flooding events is critical to the enhancement of urban road network resilience 5. Multiple studies have explored the spatial-temporal properties of floods in urban networks, including impact evaluation of environmental stress 6-8 and cascading effects in road networks 9, 10. In particular, empirical studies adopting remote sensors 11 , hydraulic data 12 , or satellite images 13 have attempted to capture the properties of urban flooding. Temporal evolution of flood status is driven by the time-dependent profile of environmental stress, such as the duration of rainfall in hurricanes 12. This temporal information facilitates identification of the outbreak and inflection points for flooding in affected networks. Flooding also exhibits high spatial correlation 14 in which the co-located road segments are...

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“…Airline congestion has been studied via network dynamics like queuing models [ 30 ]. Epidemic spreading process has been recently used to model the spreading of traffic jams in urban networks, assuming both homogeneous infection and recovery rate and homogeneous mixing approximation in network topology [ 31 ]. The possibility of modeling congestion contagion on an airline network using epidemic spreading process has been barely explored, not to mention how to develop a full-fledged heterogeneous spreading model.…”

Section: Introductionmentioning

confidence: 99%

Modeling airport congestion contagion by heterogeneous SIS epidemic spreading on airline networks

et al. 2021

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In this work, we explore the possibility of using a heterogeneous Susceptible- Infected-Susceptible SIS spreading process on an airline network to model airport congestion contagion with the objective to reproduce airport vulnerability. We derive the vulnerability of each airport from the US Airport Network data as the congestion probability of each airport. In order to capture diverse flight features between airports, e.g. frequency and duration, we construct three types of airline networks. The infection rate of each link in the SIS spreading process is proportional to its corresponding weight in the underlying airline network constructed. The recovery rate of each node is also heterogeneous, dependent on its node strength in the underlying airline network, which is the total weight of the links incident to the node. Such heterogeneous recovery rate is motivated by the fact that large airports may recover fast from congestion due to their well-equipped infrastructures. The nodal infection probability in the meta-stable state is used as a prediction of the vulnerability of the corresponding airport. We illustrate that our model could reproduce the distribution of nodal vulnerability and rank the airports in vulnerability evidently better than the SIS model whose recovery rate is homogeneous. The vulnerability is the largest at airports whose strength in the airline network is neither too large nor too small. This phenomenon can be captured by our heterogeneous model, but not the homogeneous model where a node with a larger strength has a higher infection probability. This explains partially the out-performance of the heterogeneous model. This proposed congestion contagion model may shed lights on the development of strategies to identify vulnerable airports and to mitigate global congestion by e.g. congestion reduction at selected airports.

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A simple contagion process describes spreading of traffic jams in urban networks

Cited by 115 publications

References 30 publications

Empirical dynamics of railway delay propagation identified during the large-scale Rastatt disruption

Empirical dynamics of railway delay propagation identified during the large-scale Rastatt disruption

A network percolation-based contagion model of flood propagation and recession in urban road networks

Modeling airport congestion contagion by heterogeneous SIS epidemic spreading on airline networks

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