Validation of an Agent-based Microscopic Pedestrian Simulation Model at the Pedestrian Walkway of Brooklyn Bridge

2018 ◽  
Vol 2672 (35) ◽  
pp. 33-45 ◽  
Author(s):  
Mohamed Hussein ◽  
Tarek Sayed
Keyword(s):  
Simulation Model ◽  
High Accuracy ◽  
Video Data ◽  
Model Parameters ◽  
Actual Behavior ◽  
Agent Based ◽  
Optimum Model ◽  
Using Data ◽  
Brooklyn Bridge ◽  
Pedestrian Simulation

The objective of this study is to validate a recently developed agent-based pedestrian simulation model, using data collected at the pedestrian walkway of Brooklyn Bridge. Video data were collected at the walkway and the trajectories of 294 pedestrians were extracted using computer vision. A genetic algorithm was applied to identify the optimum model parameters that minimize the error between the simulated and the actual trajectories of the calibration dataset. The simulation model was then applied to reproduce the trajectories of 214 pedestrians, considered for validation. The validation results showed that the model was capable of producing pedestrian trajectories with high accuracy, as the average location error between actual and simulated trajectories was for 0.32 m, while the average speed error was 0.06 m/s. Macroscopic results of the model were assessed by comparing the density–speed relationship in both actual data and the simulation. Finally, the accuracy of the model in reproducing the actual behavior of pedestrians during different interactions was evaluated. Results showed that the model was capable of handling these interactions with high accuracy, ranged between 79% and 100%.

scholarly journals How an election can be safely planned and conducted during a pandemic: Decision support based on a discrete event model

2021 ◽  
Author(s):  
Nadine Weibrecht ◽  
Matthias Roessler ◽  
Štefan Emrich ◽  
Niki Popper
Keyword(s):  
Simulation Model ◽  
Social Life ◽  
Discrete Event ◽  
The Novel ◽  
Agent Based ◽  
District Council ◽  
Event Model ◽  
Using Data ◽  
The One ◽  
Democratic Elections

In 2020, the ongoing COVID-19 pandemic caused major limitations for any aspect of social life and in specific for all events that require a gathering of people. While most events of this kind can be postponed or cancelled, democratic elections are key elements of any democratic regime and should be upheld if at all possible. Consequently, proper planning is required to establish the highest possible level of safety to both voters and scrutineers. In this paper, we present the novel and innovative way how the municipal council and district council elections in Vienna were planned and conducted using an agent-based simulation model. Key target of this process was to avoid queues in front of polling stations to reduce the risk of related infection clusters. In cooperation with a hygiene expert, we defined necessary precautions that should be met during the election in order to avoid the spread of COVID-19. In a next step, a simulation model was established and parametrized and validated using data from previous elections. Furthermore, the planned conditions were simulated to see whether excessive queues in front of any polling stations could form, as these could on the one hand act as an infection herd, and on the other hand, turn voters away. Our simulation identified some polling stations where long queues could emerge, however, splitting up these electoral branches resulted in a smooth election across all of Vienna. Looking back, the election did not lead to a significant increase of COVID-19 incidences. Therefore, it can be concluded that careful planning led to a safe election, despite the pandemic.

scholarly journals Dynamic versus Continuous Interventions: Optimizing Lockdown Policies for COVID-19

2021 ◽  
Author(s):  
Kaan Akinci ◽  
Javier Fdez ◽  
Elena Peña-Tapia ◽  
Olaf Witkowski
Keyword(s):  
Transmission Rate ◽  
Optimal Strategies ◽  
Specific Model ◽  
Evolutionary Strategies ◽  
Model Parameters ◽  
Seir Model ◽  
Agent Based ◽  
Slowing Down ◽  
Using Data ◽  
Gradual Decay

In the context of the ongoing COVID-19 pandemic, while millions of people await the administration of a vaccine, social distancing remains the leading approach towards the effect commonly known as “flattening the curve” of infections. Over the last year, governmental administrations throughout the globe have implemented various lockdown policies in hopes of slowing down the transmission of the disease. However, the current lack of consensus on when and how these policies should be implemented reflects the need for further studies regarding these questions. In this paper, we tackle the issue of lockdown policy management, in particular in terms of lockdown placement (how often, when, and how long these periods should be), in order to minimize the peak of infections in a specific population. We introduce a novel combination of classic mathematical disease modelling using the equation-based SEIR model, and Evolutionary Strategies (ES) for optimizing the peak of infections. The method is evaluated using data collected in different countries, and a particular focus is placed on the study of the effect of specific model parameters on lockdown optimization, such as the transmission rate (β), of which 4 alternative modelling functions have been proposed and analyzed. Our results indicate that this transmission rate parameter significantly influences the resulting optimal strategies. In particular, the presence of a gradual decay of the rate of transmission during lockdown leads to longer, more sparsely placed confinement periods while an abrupt, instantaneous drop in the amount of contacts per person favors shorter but more frequent lockdowns. Although these results are limited by the scope of action provided by the simplicity of the SEIR model, they suggest that the influence of the evolution of the rate of transmission along the disease should be assessed in further studies with alternative optimization strategies (agent-based) and models (SEIRSHUD).

scholarly journals An agent-based simulation model for analyzing the impact of teacher behavior in case of evacuation from daycare classrooms

2018 ◽  
Author(s):  
Raimundo J. C. Ferro Junior ◽  
Thayanne F. Da Silva ◽  
João P. B. Andrade ◽  
Gustavo A. L. De Campos
Keyword(s):  
Simulation Model ◽  
Classroom Environment ◽  
Student Behavior ◽  
Simulation Models ◽  
Teacher Behavior ◽  
Behavioral Characteristics ◽  
Agent Based ◽  
Evacuation Plans ◽  
Using Data ◽  
The Impact

Agent-based simulations can be used to study and formulate evacuation plans, however the traditional simulation models for this context are not suitable for daycare and school settings where the population of these settings has unique physical and behavioral characteristics. This paper proposes a simulation model based on classroom evacuation agents from daycare centers that takes into account the physical and behavioral characteristics of the students and that is able to provide data to evaluate the impact of teachers’ behavior in the evacuation process. The model was built using the Netlogo tool, using as basis the structures of the Brazilian classroom environment and using data from the literature on human behavior. The tests were performed in different settings of parameters for environment, population, student behavior and evacuation strategies. The experiments showed that the model was able to reproduce results consistent with the expected values and scenarios described in the literature, as well as being an effective tool to evaluate the impact of teacher behavior in the evacuation process, especially in rooms where students have a high degree of dependency.

PSVIII-11 Agent-based simulation model to evaluate the economic performance of reproductive programs in beef cattle

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 428-429
Author(s):  
Oscar A Ojeda-Rojas ◽  
Angela Maria M Gonella-Diaza ◽  
Gustavo L Sartorello ◽  
Augusto H Hauber Gameiro
Keyword(s):  
Beef Cattle ◽  
Simulation Model ◽  
Economic Performance ◽  
Reproductive Strategies ◽  
Operating Cost ◽  
Agent Based Simulation ◽  
Time Intervals ◽  
Agent Based ◽  
Timed Artificial Insemination ◽  
Using Data

Abstract The objective of this study was to create a stochastic, agent-based simulation model to compare the economic performance of reproductive strategies in beef cattle. The model was parameterized using data from a real herd and the scientific literature. The scenarios evaluated were: natural mating (NM) only (ONM); one timed artificial insemination (TAI) plus NM (1TAI+NM); two TAI plus NM, with 24, 32, and 40 days between TAI (2TAI/24+NM, 2TAI/32+NM, and 2TAI/40+NM, respectively); three TAI without NM, with 24, 32, and 40 days (3TAI/24, 3TAI/32, and 3TAI/40, respectively), and three TAI plus NM, with 24 and 32 days (3TAI/24+NM and 3TAI/32+NM, respectively). The initial female herd was 400 and remained constant. The bull population varies from 0 to 15, depending on the scenario. The outcomes for each scenario are assessed on 32 farms, using a 5000-day time horizon at one-day time intervals and an animal-by-animal basis. The 3TAI/24+NM scenario resulted in the highest incomes (US$ 96,479.2 ± 709.8), while ONM had the least value (US$ 79,753.4 ± 741.9). The total operating cost was highest for 3TAI/24+NM (US$ 101,720.6 ± 79.2) and lowest for ONM (US$ 90,898.6 ± 59.2). However, when the total operating cost was evaluated per kg of weaned calf, the highest and lowest costs were for ONM (US$ 2.8 ± 0.0/kg) and 2TAI/24+NM (US$ 2.17 ± 0.0/kg), respectively. The 2TAI/24+NM (US$ -4,651.3 ± 630.7) scenario presented the best net margin, while the lowest result was for 3TAI/40 (US$ -12,590.0 ± 746.3). Our model suggests that reproductive strategies that use TAI have better economic performance than those under ONM. However, when three TAI were performed with 40 days, the benefit was lower, and even for some analyzes, it was worse than the ONM. The 2TAI/24+NM scenario outperformed the others because of the contrast between its high income with moderate costs.

scholarly journals Analysis of Cooperative Perception in Ant Traffic and Its Effects on Transportation System by Using a Congestion-Free Ant-Trail Model

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2393
Author(s):  
Prafull Kasture ◽  
Hidekazu Nishimura
Keyword(s):  
Average Velocity ◽  
Transportation System ◽  
Self Organization ◽  
Model Parameters ◽  
Agent Based ◽  
Model Simulations ◽  
Mathematical Representations ◽  
Traffic Conditions ◽  
Traffic Jams ◽  
Ant Trail

We investigated agent-based model simulations that mimic an ant transportation system to analyze the cooperative perception and communication in the system. On a trail, ants use cooperative perception through chemotaxis to maintain a constant average velocity irrespective of their density, thereby avoiding traffic jams. Using model simulations and approximate mathematical representations, we analyzed various aspects of the communication system and their effects on cooperative perception in ant traffic. Based on the analysis, insights about the cooperative perception of ants which facilitate decentralized self-organization is presented. We also present values of communication-parameters in ant traffic, where the system conveys traffic conditions to individual ants, which ants use to self-organize and avoid traffic-jams. The mathematical analysis also verifies our findings and provides a better understanding of various model parameters leading to model improvements.

scholarly journals Formulating the history matching problem with consistent error statistics

2021 ◽  
Author(s):  
Geir Evensen
Keyword(s):  
Simulation Model ◽  
Measurement Errors ◽  
History Matching ◽  
Critical Role ◽  
Bayes Theorem ◽  
Conditional Probability Density ◽  
Model Parameters ◽  
Rate Data ◽  
Matching Problem ◽  
Reservoir Prediction

AbstractIt is common to formulate the history-matching problem using Bayes’ theorem. From Bayes’, the conditional probability density function (pdf) of the uncertain model parameters is proportional to the prior pdf of the model parameters, multiplied by the likelihood of the measurements. The static model parameters are random variables characterizing the reservoir model while the observations include, e.g., historical rates of oil, gas, and water produced from the wells. The reservoir prediction model is assumed perfect, and there are no errors besides those in the static parameters. However, this formulation is flawed. The historical rate data only approximately represent the real production of the reservoir and contain errors. History-matching methods usually take these errors into account in the conditioning but neglect them when forcing the simulation model by the observed rates during the historical integration. Thus, the model prediction depends on some of the same data used in the conditioning. The paper presents a formulation of Bayes’ theorem that considers the data dependency of the simulation model. In the new formulation, one must update both the poorly known model parameters and the rate-data errors. The result is an improved posterior ensemble of prediction models that better cover the observations with more substantial and realistic uncertainty. The implementation accounts correctly for correlated measurement errors and demonstrates the critical role of these correlations in reducing the update’s magnitude. The paper also shows the consistency of the subspace inversion scheme by Evensen (Ocean Dyn. 54, 539–560 2004) in the case with correlated measurement errors and demonstrates its accuracy when using a “larger” ensemble of perturbations to represent the measurement error covariance matrix.

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