Multiscale Pedestrian Dynamics and Infection Spread Model for Policy Analysis

Namilae, Sirish; Derjany, Pierrot; Liu, Dahai; Mubayi, Anuj; Srinivasan, Ashok

doi:10.17815/cd.2020.86

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…Yang et al estimated demand on diesel fuel (which is needed for emergency power generation) at the county level based on weather data (e.g., wind speed) and power outage data (23). A group of researchers from Florida predicted fuel shortages and identified an intervention strategy by using real-time traffic information and fuel availability data from GasBuddy (an online crowd-sourcing database providing real-time gas station information) (2,24). Overall, human behavior and the associated demographic characteristics have not been featured in understanding fuel access needs.…”

Section: Fuel Consumption and Refueling Behaviormentioning

confidence: 99%

Analyzing Gas Station Visits during Hurricane Ida: Implications for Future Fuel Supply

Bian

Smiley

Parr

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Fuel demand surges and supply shortages during hurricanes are problematic. However, fuel consumption and refueling behavior are not well discussed in disaster scenarios compared with the extant literature on these topics during normal conditions. This study used an emerging data source to report when fuel demand (indicated by gas station visits) deviated from a normal variation range during Hurricane Ida (a Category 4 Atlantic hurricane in 2021), how long the deviation lasted, and which area(s) experienced greater deviations. Gas station visits are likely to surge within 2 days before storm landfall, while evacuation destinations and intermediate trip connectors had longer surges. Using univariate linear regression, this study also statistically explored what factors might affect the fuel demand deviation at an aggregate level. The testing results revealed that zones of higher vehicle ownership, more daily commuters driving alone, lower residential stability, more mobile homes, and less storm impacts tended to have more gas station visits (i.e., greater fuel demand). Then, this study analyzed data at a more disaggregate level to identify the characteristics of gas stations that are more likely to have more visitors. Gas stations near interstates/highways attracted more visits, especially within 2 days before storm landfall. Gas stations farther away from interstates/highways attracted more visits after storm landfall. Overall, this study contributes to our knowledge about fuel consumption and refueling location preferences during hurricanes. These findings could help public agencies and private companies in their fuel supply planning (e.g., fuel distribution) and response (e.g., providing power generators to focal locations).

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Section: Fuel Consumption and Refueling Behaviormentioning

confidence: 99%

Analyzing Gas Station Visits during Hurricane Ida: Implications for Future Fuel Supply

Bian

Smiley

Parr

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

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“…In prior related work [20, 23–27], we developed a particle dynamics pedestrian movement model that tracks the movement of individual passengers in airplanes, airports, and combined it with stochastic infection dynamics. In Liu and Namilae et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In prior related work [20,[23][24][25][26][27], we developed a particle dynamics pedestrian movement model that tracks the movement of individual passengers in airplanes, airports, and combined it with stochastic infection dynamics. In Liu and Namilae et al (2018) [22], human panic behaviours were considered, and several simulations models were developed based on review of previous studies, using social forces models and agent-based models.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep adaptive learning for safe and efficient navigation of pedestrian dynamics

Pugh

Park

Derjany

et al. 2021

IET Intelligent Trans Sys

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An efficient and safe evacuation of passengers is important during emergencies. Overcapacity on a route can cause an increased evacuation time. Decision making is essential to optimally guide and distribute pedestrians to multiple routes while ensuring safety. Developing an optimal pedestrian path planning route while considering learning dynamics and uncertainties in the environment generated from pedestrian behaviour is challenging. While previous evacuation planning studies have focused on either simulation of realistic behaviours or simple route planning, the best route decisions with several intermediate decision‐points, especially under real‐time changing environments, have not been considered. This paper develops an optimal navigation model providing more navigation guidance for evacuation emergencies to minimize the total evacuation time while considering the influence of other passengers based on the social‐force model. The integration of the optimal navigation model was ultimately able to reduce the overall evacuation time of multiple scenarios presented with two different overall pedestrian totals. The overall maximum evacuation time savings presented was 10.6%.

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“…These rules are often based on mutual distances. For example, virtual persons close to an infectious one become infected after a certain time (see [8][9][10][11][12]). Ronchi and Lovreglio [13] expand the concept of proximity to further contact types, e. g. physical contact, proximity within a certain radius and occupancy of the same room or building.…”

Section: Introductionmentioning

confidence: 99%

Modelling airborne transmission of SARS-CoV-2 at a local scale

et al. 2022

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The coronavirus disease (COVID-19) pandemic has changed our lives and still poses a challenge to science. Numerous studies have contributed to a better understanding of the pandemic. In particular, inhalation of aerosolised pathogens has been identified as essential for transmission. This information is crucial to slow the spread, but the individual likelihood of becoming infected in everyday situations remains uncertain. Mathematical models help estimate such risks. In this study, we propose how to model airborne transmission of SARS-CoV-2 at a local scale. In this regard, we combine microscopic crowd simulation with a new model for disease transmission. Inspired by compartmental models, we describe virtual persons as infectious or susceptible. Infectious persons exhale pathogens bound to persistent aerosols, whereas susceptible ones absorb pathogens when moving through an aerosol cloud left by the infectious person. The transmission depends on the pathogen load of the aerosol cloud, which changes over time. We propose a ‘high risk’ benchmark scenario to distinguish critical from non-critical situations. A parameter study of a queue shows that the new model is suitable to evaluate the risk of exposure qualitatively and, thus, enables scientists or decision-makers to better assess the spread of COVID-19 and similar diseases.

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Multiscale Pedestrian Dynamics and Infection Spread Model for Policy Analysis

Cited by 5 publications

References 8 publications

Analyzing Gas Station Visits during Hurricane Ida: Implications for Future Fuel Supply

Analyzing Gas Station Visits during Hurricane Ida: Implications for Future Fuel Supply

Deep adaptive learning for safe and efficient navigation of pedestrian dynamics

Modelling airborne transmission of SARS-CoV-2 at a local scale

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