TransModeler is transportation modeling and simulation software used by transportation planners and engineers. It allows for the modeling and analysis of multimodal transportation networks including highways, transit, pedestrians, and bicycles.
TransModeler: Transportation Modeling and Simulation Software
TransModeler is transportation modeling and simulation software used by transportation planners and engineers. It allows for the modeling and analysis of multimodal transportation networks including highways, transit, pedestrians, and bicycles.
What is TransModeler?
TransModeler is a leading transportation modeling and simulation software used by transportation planners, engineers, and researchers to model and analyze transportation networks. It provides advanced mesoscopic, microscopic, and macroscopic modeling capabilities to accurately evaluate transportation projects and policies.
State-of-the-art mesoscopic and microscopic simulation
Integrated mode choice modeling capabilities
Sophisticated visualization and animation
Customizable reporting
Python scripting API
With its integrated architecture and workflow, TransModeler is designed to model entire multimodal transportation networks and simulate detailed traffic operations. It allows users to analyze various planning scenarios and impacts at a refined level of detail. TransModeler is used worldwide by leading transportation agencies to model and optimize network capacity, new infrastructure projects, transit systems, tolling strategies, and active transportation investments.
TransModeler Features
Features
Multimodal transportation network modeling
Traffic assignment and simulation
Public transit modeling and analysis
Pedestrian and bicycle network modeling
Microscopic, mesoscopic and macroscopic modeling
Demand modeling and forecasting
Environmental impact analysis
Pricing
Subscription-Based
Pros
Powerful and flexible modeling capabilities
Integrated modules for different analyses
Customizable user interface
Industry standard software with wide adoption
Cons
Steep learning curve
Requires large amounts of data
Limited capabilities for pedestrian and bicycle modeling
Can be computationally intensive for large networks
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