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Estamos lanzando una nueva serie de videotutoriales gratuitos para utilizar el software de modelización de la movilidad Aimsun Next. No es necesario tener experiencia: estos tutoriales están diseñados para que los sigan los principiantes. En esta introducción a la serie, Dimitris Triantafyllos expone los temas que trataremos.
¿Eres nuevo en Aimsun Next?
¡Empecemos! En este vídeo, te mostraremos cómo descargar el software y te llevaremos a una visita guiada por la página de bienvenida y la interfaz de usuario. Para una guía paso a paso de la instalación de Aimsun Next, ve aquí: www.aimsun.com/es/aimsun-next-installation-guide/
Create a complete network from scratch: learn how to open Aimsun Next and create a new project, then import a map from OpenStreetMap and apply background images.
The goal of these model-building exercises is to start from zero and create a complete traffic-network model with all the data you need to run a microscopic simulation.
Learn how to create the geometry of a road network.
The goal of these model-building exercises is to start from zero and create a complete traffic-network model with all the data you need to run a microscopic simulation.
The goal of these model-building exercises is to start from zero and create a complete traffic-network model with all the data you need to run a microscopic simulation.
The goal of these model-building exercises is to start from zero and create a complete traffic-network model with all the data you need to run a microscopic simulation.
In Aimsun Next there are two ways to represent demand: one is through traffic states and the other is through origin-destination matrices (often shortened to ‘OD matrices’). In this video we will look at creating traffic states.
The goal of these model-building exercises is to start from zero and create a complete traffic-network model with all the data you need to run a microscopic simulation.
The goal of these model-building exercises is to start from zero and create a complete traffic-network model with all the data you need to run a microscopic simulation.
The goal of these model-building exercises is to start from zero and create a complete traffic-network model with all the data you need to run a microscopic simulation.
The goal of these model-building exercises is to start from zero and create a complete traffic-network model with all the data you need to run a microscopic simulation.
The goal of these model-building exercises is to start from zero and create a complete traffic-network model with all the data you need to run a microscopic simulation.
How to visualise results in Aimsun Next, from viewing data for a single vehicle to creating thematic maps to present results with different labels and diagrams.
• Obtain different visual simulation outputs and view statistics.
• View data from a single vehicle while running a microscopic simulation.
• Use the Time Series Viewer to view various data during a simulation.
• Define view styles and view modes to present results in the main 2D view using colors, shapes, and labels.
• Create a print layout to present your data and compare data from different simulations.
The experiments in these exercises use models from the microscopic simulator. If you have a license for the Pro Meso edition of Aimsun Next instead, you should create the experiments as described but use the mesoscopic simulator rather than the microscopic option.
Using the example of a football match in Barcelona, we’ll observe the impact of spectators leaving the stadium after the game on traffic in the network, then we’ll find traffic management actions to mitigate that impact.
• Create policies for changing the destinations of vehicles in response to network conditions and events.
• Define strategies and policies to divert traffic, close turns, and reduce congestion. These strategies will involve police actions, VMS signs, traffic control plans, and conditional triggers.
• Create periodic incidents in a road section.
• We’ll use the example of a football match in Barcelona, where departing spectators impact traffic between 20:00 and 21:00h. Our task is to observe if all vehicles can leave in a timely manner and how we can mitigate their impact on traffic in the network.
Crating post-event mitigation strategies in the simulation
• Create policies for changing the destinations of vehicles in response to network conditions and events.
• Define strategies and policies to divert traffic, close turns, and reduce congestion. These strategies will involve police actions, VMS signs, traffic control plans, and conditional triggers.
• Create periodic incidents in a road section.
• We’ll use the example of a football match in Barcelona, where departing spectators impact traffic between 20:00 and 21:00h. Our task is to observe if all vehicles can leave in a timely manner and how we can mitigate their impact on traffic in the network.
• Create policies for changing the destinations of vehicles in response to network conditions and events.
• Define strategies and policies to divert traffic, close turns, and reduce congestion. These strategies will involve police actions, VMS signs, traffic control plans, and conditional triggers.
• Create periodic incidents in a road section.
• We’ll use the example of a football match in Barcelona, where departing spectators impact traffic between 20:00 and 21:00h. Our task is to observe if all vehicles can leave in a timely manner and how we can mitigate their impact on traffic in the network.
How to use strategic modeling tools in Aimsun Next.
• Prepare and execute a generation/attraction experiment to create GA vectors using land-use and travel-behavior data.
• Run a trip distribution and a modal-split experiment to create OD matrices and assign the resultant demand to the traffic network.
• Use a four-step-model experiment to link these processes together.
The basic macroscopic modeling tools in Aimsun Next
• Look at traffic assignment, OD-matrix adjustment, subnetworks, and how to balance OD matrices.
• Assign an OD matrix to a network to see how the flow distribution is assigned and how results are obtained.
• Adjust the OD matrix using data from detectors.
• Define a subnetwork and obtain its traversal OD matrix.
• Balance an OD matrix using the Furness method.
Run a microscopic simulation that contains embedded pedestrians.
• Create a pedestrian area with all the data required to run a microsimulation that includes embedded pedestrians.
• Define an area that pedestrians can walk through and areas where there is interaction between vehicles and pedestrians.
• Define pedestrian centroids, pedestrian OD matrices, create traffic demand, and run a simulation scenario to see the impact of pedestrians on traffic.
• Scenario Data Comparison
• Network Attributes Override
• Scenario Revision
• Geometry Configuration
• Using a Static Assignment to Verify Real and Simulated Data
• Defining a Subnetwork Study Area and Running a Static Traversal
• Checking Detector Coverage
• Extending the OD Matrices Based on Traffic Measurements
• Adjusting the Subnetwork OD Matrices
• Creating a Profiled Demand with a Static OD Departure Adjustment
• Running a Dynamic User Equilibrium (DUE) Experiment
• Running a Microscopic Stochastic Route Choice (SRC) Scenario
• Running a Meso-micro Hybrid SRC Scenario
• Running a Macro-meso Hybrid Simulation
• Priority
• Actuated Control
• Priority
• Actuated Control
Una vez que hayas instalado Aimsun Next, mira la lista de reproducción de tutoriales en el canal de YouTube de Aimsun: los vídeos están diseñados para acompañar los tutoriales escritos y los ejercicios prácticos a los que puedes acceder a través de la página de bienvenida de nuestro software.
Para iniciar un tutorial:
Si quieres saltarte los ejercicios pero ver los resultados finales previstos y el modelo terminado, haz clic en Red terminada y abre el modelo de red terminado.
Para una formación más profunda o avanzada, consulta nuestro calendario de cursos de formación.
Si tienes alguna duda, ponte en contacto con nosotros escribiendo a info@aimsun.com
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