scholarly journals Analysis and Simulation of Intervention Strategies against Bus Bunching by means of an Empirical Agent-Based Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Wei Liang Quek ◽  
Ning Ning Chung ◽  
Vee-Liem Saw ◽  
Lock Yue Chew
Keyword(s):  
Monte Carlo ◽  
Stochastic Dynamics ◽  
Probability Distributions ◽  
Intervention Strategies ◽  
Interarrival Time ◽  
Agent Based Model ◽  
Test Bed ◽  
Agent Based ◽  
Bus Bunching ◽  
Bus Network

In this paper, we propose an empirically based Monte Carlo bus-network (EMB) model as a test bed to simulate intervention strategies to overcome the inefficiencies of bus bunching. The EMB model is an agent-based model which utilizes the positional and temporal data of the buses obtained from the Global Positioning System (GPS) to constitute (1) a set of empirical velocity distributions of the buses and (2) a set of exponential distributions of interarrival time of passengers at the bus stops. Monte Carlo sampling is then performed on these two derived probability distributions to yield the stochastic dynamics of both the buses’ motion and passengers’ arrival. Our EMB model is generic and can be applied to any real-world bus network system. In particular, we have validated the model against the Nanyang Technological University’s Shuttle Bus System by demonstrating its accuracy in capturing the bunching dynamics of the shuttle buses. Furthermore, we have analyzed the efficacy of three intervention strategies: holding, no-boarding, and centralized-pulsing, against bus bunching by incorporating the rule set of these strategies into the model. Under the scenario where the buses have the same velocity, we found that all three strategies improve both the waiting and travelling times of the commuters. However, when the buses have different velocities, only the centralized-pulsing scheme consistently outperforms the control scenario where the buses periodically bunch together.

scholarly journals Evaluating intervention strategies in controlling coronavirus disease 2019 (COVID-19) spread in care homes: An agent-based model – CORRIGENDUM

2021 ◽  
pp. 1-1
Author(s):  
Le Khanh Ngan Nguyen ◽  
Susan Howick ◽  
Dennis McLafferty ◽  
Gillian H. Anderson ◽  
Sahaya J. Pravinkumar ◽  
...  
Keyword(s):  
Intervention Strategies ◽  
Care Homes ◽  
Agent Based Model ◽  
Agent Based

scholarly journals Optimal foraging and the information theory of gambling

2019 ◽  
Vol 16 (157) ◽  
pp. 20190162 ◽  
Author(s):  
Roland J. Baddeley ◽  
Nigel R. Franks ◽  
Edmund R. Hunt
Keyword(s):  
Information Theory ◽  
Monte Carlo ◽  
Optimal Foraging ◽  
Foraging Behaviour ◽  
Stochastic Dynamics ◽  
Probability Distributions ◽  
Movement Ecology ◽  
Mcmc Methods ◽  
Long Run ◽  
Resource Gradient

At a macroscopic level, part of the ant colony life cycle is simple: a colony collects resources; these resources are converted into more ants, and these ants in turn collect more resources. Because more ants collect more resources, this is a multiplicative process, and the expected logarithm of the amount of resources determines how successful the colony will be in the long run. Over 60 years ago, Kelly showed, using information theoretic techniques, that the rate of growth of resources for such a situation is optimized by a strategy of betting in proportion to the probability of pay-off. Thus, in the case of ants, the fraction of the colony foraging at a given location should be proportional to the probability that resources will be found there, a result widely applied in the mathematics of gambling. This theoretical optimum leads to predictions as to which collective ant movement strategies might have evolved. Here, we show how colony-level optimal foraging behaviour can be achieved by mapping movement to Markov chain Monte Carlo (MCMC) methods, specifically Hamiltonian Monte Carlo (HMC). This can be done by the ants following a (noisy) local measurement of the (logarithm of) resource probability gradient (possibly supplemented with momentum, i.e. a propensity to move in the same direction). This maps the problem of foraging (via the information theory of gambling, stochastic dynamics and techniques employed within Bayesian statistics to efficiently sample from probability distributions) to simple models of ant foraging behaviour. This identification has broad applicability, facilitates the application of information theory approaches to understand movement ecology and unifies insights from existing biomechanical, cognitive, random and optimality movement paradigms. At the cost of requiring ants to obtain (noisy) resource gradient information, we show that this model is both efficient and matches a number of characteristics of real ant exploration.

scholarly journals A Spatial Agent-Based Model to Assess the Spread of Malaria in Relation to Anti-Malaria Interventions in Southeast Iran

2020 ◽  
Vol 9 (9) ◽  
pp. 549
Author(s):  
Navid Mahdizadeh Gharakhanlou ◽  
Navid Hooshangi ◽  
Marco Helbich
Keyword(s):  
Indoor Residual Spraying ◽  
Intervention Strategies ◽  
Agent Based Model ◽  
Malaria Endemic Area ◽  
High Coverage ◽  
Agent Based ◽  
The World ◽  
Coverage Rates ◽  
Long Lasting Insecticidal Nets ◽  
Malaria Interventions

Malaria threatens the lives of many people throughout the world. To counteract its spread, knowledge of the prevalence of malaria and the effectiveness of intervention strategies is of great importance. The aim of this study was to assess (1) the spread of malaria by means of a spatial agent-based model (ABM) and (2) the effectiveness of several interventions in controlling the spread of malaria. We focused on Sarbaz county in Iran, a malaria-endemic area where the prevalence rate is high. Our ABM, which was carried out in two steps, considers humans and mosquitoes along with their attributes and behaviors as agents, while the environment is made up of diverse environmental factors, namely air temperature, relative humidity, vegetation, altitude, distance from rivers and reservoirs, and population density, the first three of which change over time. As control interventions, we included long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS). The simulation results showed that applying LLINs and IRS in combination, rather than separately, was most efficient in reducing the number of infected humans. In addition, LLINs and IRS with moderate or high and high coverage rates, respectively, had significant effects on reducing the number of infected humans when applied separately. Our results can assist health policymakers in selecting appropriate intervention strategies in Iran to reduce malaria transmission.

Monte Carlo Physarum Machine: An Agent-based Model for Reconstructing Complex 3D Transport Networks

2020 ◽  
Author(s):  
Oskar Elek ◽  
Joseph N. Burchett ◽  
J. Xavier Prochaska ◽  
Angus G. Forbes
Keyword(s):  
Monte Carlo ◽  
Agent Based Model ◽  
Transport Networks ◽  
Agent Based

Evolution of tag-based cooperation on Erdős–Rényi random graphs

2014 ◽  
Vol 25 (06) ◽  
pp. 1450006 ◽  
Author(s):  
F. W. S. Lima ◽  
Tarik Hadzibeganovic ◽  
Dietrich Stauffer
Keyword(s):  
Monte Carlo ◽  
Random Graph ◽  
Random Graphs ◽  
Phenotypic Traits ◽  
Artificial Agents ◽  
Agent Based Model ◽  
Agent Based ◽  
Mode Of Reproduction ◽  
Interacting Agents ◽  
Short Period

Here, we study an agent-based model of the evolution of tag-mediated cooperation on Erdős–Rényi random graphs. In our model, agents with heritable phenotypic traits play pairwise Prisoner's Dilemma-like games and follow one of the four possible strategies: Ethnocentric, altruistic, egoistic and cosmopolitan. Ethnocentric and cosmopolitan strategies are conditional, i.e. their selection depends upon the shared phenotypic similarity among interacting agents. The remaining two strategies are always unconditional, meaning that egoists always defect while altruists always cooperate. Our simulations revealed that ethnocentrism can win in both early and later evolutionary stages on directed random graphs when reproduction of artificial agents was asexual; however, under the sexual mode of reproduction on a directed random graph, we found that altruists dominate initially for a rather short period of time, whereas ethnocentrics and egoists suppress other strategists and compete for dominance in the intermediate and later evolutionary stages. Among our results, we also find surprisingly regular oscillations which are not damped in the course of time even after half a million Monte Carlo steps. Unlike most previous studies, our findings highlight conditions under which ethnocentrism is less stable or suppressed by other competing strategies.

scholarly journals Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks

2015 ◽  
Vol 7 (9) ◽  
pp. 987-997 ◽  
Author(s):  
J. Walpole ◽  
J. C. Chappell ◽  
J. G. Cluceru ◽  
F. Mac Gabhann ◽  
V. L. Bautch ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Time Lapse ◽  
Confocal Imaging ◽  
Agent Based Model ◽  
Agent Based

We developed an agent-based model of endothelial sprout initiations based on time-lapse confocal imaging in vitro that outperforms Monte Carlo simulations, suggesting that sprout location and frequency are not purely stochastic behaviors.

scholarly journals Lockdowns to Contain COVID-19 Increase Risk and Severity of Mosquito-Borne Disease Outbreaks

2020 ◽  
Author(s):  
Akshay Jindal ◽  
Shrisha Rao
Keyword(s):  
Indoor Residual Spraying ◽  
Disease Outbreaks ◽  
Communicable Diseases ◽  
Intervention Strategies ◽  
Agent Based Model ◽  
Insecticide Treated Nets ◽  
Agent Based ◽  
Infected People ◽  
Increase Risk ◽  
The Impact

AbstractMany countries are implementing lockdown measures to slow the COVID-19 pandemic, putting more than a third of the world’s population under restrictions. The scale of such lockdowns is unprecedented, and while some effects of lockdowns are readily apparent, it is less clear what effects they may have on outbreaks of serious communicable diseases. We examine the impact of these lockdowns on outbreaks of mosquito-borne diseases. Using an agent-based model and simulations, we find that the risk and severity of such outbreaks is much greater under lockdown conditions, with the number of infected people doubling in some cases. This increase in number of cases varies by different mosquito-borne diseases, and is significantly higher for diseases spread by day-biting mosquitoes. We analysed various intervention strategies and found that during lockdowns, decentralised strategies such as insecticide-treated nets and indoor residual spraying are more effective than centralised strategies.

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