scholarly journals Simulating Spatio-Temporal Patterns of Bicycle Flows with an Agent-Based Model

2021 ◽  
Vol 10 (2) ◽  
pp. 88
Author(s):  
Dana Kaziyeva ◽  
Martin Loidl ◽  
Gudrun Wallentin
Keyword(s):  
Regional Scale ◽  
Evidence Base ◽  
Wide Field ◽  
Traffic Patterns ◽  
Agent Based Model ◽  
Validation Data ◽  
Agent Based ◽  
Mobility Behavior ◽  
Spatio Temporal ◽  
Models Validation

Transport planning strategies regard cycling promotion as a suitable means for tackling problems connected with motorized traffic such as limited space, congestion, and pollution. However, the evidence base for optimizing cycling promotion is weak in most cases, and information on bicycle patterns at a sufficient resolution is largely lacking. In this paper, we propose agent-based modeling to simulate bicycle traffic flows at a regional scale level for an entire day. The feasibility of the model is demonstrated in a use case in the Salzburg region, Austria. The simulation results in distinct spatio-temporal bicycle traffic patterns at high spatial (road segments) and temporal (minute) resolution. Scenario analysis positively assesses the model’s level of complexity, where the demographically parametrized behavior of cyclists outperforms stochastic null models. Validation with reference data from three sources shows a high correlation between simulated and observed bicycle traffic, where the predictive power is primarily related to the quality of the input and validation data. In conclusion, the implemented agent-based model successfully simulates bicycle patterns of 186,000 inhabitants within a reasonable time. This spatially explicit approach of modeling individual mobility behavior opens new opportunities for evidence-based planning and decision making in the wide field of cycling promotion

scholarly journals An Agent-Based Evaluation of Varying Evacuation Scenarios in Merapi: Simultaneous and Staged

Geosciences ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 317
Author(s):  
Jumadi Jumadi ◽  
Steve J. Carver ◽  
Duncan J. Quincey
Keyword(s):  
Traffic Congestion ◽  
Large Scale ◽  
Relative Effectiveness ◽  
Regional Scale ◽  
Traffic Density ◽  
Peak Time ◽  
Agent Based Model ◽  
Merapi Volcano ◽  
Agent Based ◽  
Spatio Temporal

Mass evacuation should be conducted when a disaster threatens within a regional scale. It is reported that 400,000 people were evacuated during the last eruption of Merapi Volcano in 2010. Such a large-scale evacuation can lead to chaos or congestion, unless well managed. Staged evacuation has been investigated as a solution to reducing the degree of chaos during evacuation processes. However, there is a limited conception of how the stages should be ordered in terms of which group should move first and which group should follow. This paper proposes to develop evacuation stage ordering based on the geographical character of the people at risk and examine the ordering scenarios through an agent-based model of evacuation. We use several geographical features, such as proximity to the hazard, road network conditions (accessibility), size of the population, and demographics as the parameters for ranking the order of each population unit in GIS. From this concept, we produced several scenarios of ranking based on different weightings of the parameters. We applied the scenarios in an agent-based model of volcanic evacuation experiment to observe the results. Afterwards, the results were evaluated based on the ability to reduce the risk and spatio-temporal traffic density along road networks compared to the result of simultaneous evacuation to establish the relative effectiveness of the outcome. The result shows that the staged scenario has a better ability to reduce the potential traffic congestion during the peak time of the evacuation compared to the simultaneous strategy. However, the simultaneous strategy has better performance regarding the speed of reducing the risk. An evaluation of the relative performance of the four varying staged scenarios is also presented and discussed in this paper.

scholarly journals Real-time Analysis of City Scale Transportation Networks in New Orleans Metropolitan Area using an Agent Based Model Approach

2019 ◽  
Vol 271 ◽  
pp. 06007
Author(s):  
Millard McElwee ◽  
Bingyu Zhao ◽  
Kenichi Soga
Keyword(s):  
New Orleans ◽  
Real Time ◽  
High Performance ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Regional Scale ◽  
Agent Based Model ◽  
Agent Based ◽  
Real Time Analysis

The primary focus of this research is to develop and implement an agent-based model (ABM) to analyze the New Orleans Metropolitan transportation network near real-time. ABMs have grown in popularity because of their ability to analyze multifaceted community scale resilience with hundreds of thousands of links and millions of agents. Road closures and reduction in capacities are examples of influences on the weights or removal of edges which can affect the travel time, speed, and route of agents in the transportation model. Recent advances in high-performance computing (HPC) have made modeling networks on the city scale much less computationally intensive. We introduce an open-source ABM which utilizes parallel distributed computing to enable faster convergence to large scale problems. We simulate 50,000 agents on the entire southeastern Louisiana road network and part of Mississippi as well. This demonstrates the capability to simulate both city and regional scale transportation networks near real time.

scholarly journals Modelling interventions to control COVID-19 outbreaks in a refugee camp

2020 ◽  
Author(s):  
R Tucker Gilman ◽  
Siyana Mahroof-Shaffi ◽  
Christian Harkensee ◽  
Andrew T Chamberlain
Keyword(s):  
Refugee Camp ◽  
Evidence Base ◽  
Intervention Strategies ◽  
Refugee Camps ◽  
Agent Based Model ◽  
Agent Based ◽  
Medical Facilities ◽  
Nonpharmaceutical Interventions ◽  
Planning Intervention

Refugee camp populations are expected to be vulnerable to COVID-19 due to overcrowding, unsanitary conditions, and inadequate medical facilities. Because there has been no COVID-19 outbreak in a refugee camp to date, the potential for nonpharmaceutical interventions to slow the spread of COVID-19 in refugee camps remains untested. We used an agent-based model to simulate COVID-19 outbreaks in the Moria refugee camp, and we studied the effects of feasible interventions. Subdividing the camp ('sectoring') "flattened the curve," reducing peak infection by up to 70% and delaying peak infection by up to several months. The use of face masks coupled with efficient isolation of infected individuals reduced the overall incidence of infection and sometimes averted epidemics altogether. These interventions must be implemented quickly to be effective. Lockdowns had little effect on COVID-19 dynamics. Our findings provide an evidence base for camp managers planning intervention strategies against COVID-19 or future epidemics.

scholarly journals A Conceptual Design of Spatio-Temporal Agent-Based Model for Volcanic Evacuation

Systems ◽  
2017 ◽  
Vol 5 (4) ◽  
pp. 53 ◽  
Author(s):  
Jumadi ◽  
Steve Carver ◽  
Duncan Quincey
Keyword(s):  
Conceptual Design ◽  
Agent Based Model ◽  
Agent Based ◽  
Spatio Temporal

Pest control at a regional scale: Identifying key criteria using a spatially explicit, agent‐based model

2019 ◽  
Vol 56 (7) ◽  
pp. 1515-1527 ◽  
Author(s):  
Audrey Lustig ◽  
Alex James ◽  
Dean Anderson ◽  
Micheal Plank
Keyword(s):  
Pest Control ◽  
Regional Scale ◽  
Spatially Explicit ◽  
Agent Based Model ◽  
Agent Based
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