Training

Emme Flow is a code-free automation system to assemble and run travel demand models and related workflows without the need to write code. Learn to build reusable and customized workflows from Emme procedures, instrument control flow using iteration and branching, manipulate variables with expressions, and add inline documentation and organization. Participants will learn with hands-on examples.

Develop your own Modeller tools with customized front ends and logging. Leverage tools from the Emme Standard toolbox, use Emme APIs calls, third-party Python libraries and your own code. Share your tools in toolboxes with open or compiled code.

A comprehensive course intended for modellers who will develop, maintain or analyze travel demand models with Agent. Participants will develop an understanding of contrasting travel demand model structures including zonal aggregate, hybrid, tour-based and simple activity-based models as implemented in Agent. A series of demonstrations and hands-on exercises covers the specification of location-, temporal- and other choice models, management of model segmentation, chaining models, time-space constraints, schema definition, relational expressions, tour tracing, calibration and other results visualization and diagnostics.

This course introduces Dynameq features and workflows for DTA model assessment and calibration. Demonstrations and hands-on exercises drawn from real-world applications are used to cover diagnostic outputs, assessment of traffic counts, network calibration parameters, gridlock avoidance, route choice, and simulation-based matrix adjustment. Preparation and assessment of future scenarios is also discussed.

A comprehensive introductory course intended for modellers who will develop, maintain, or analyze traffic simulation models with Dynameq. Participants will develop a basic understanding of traffic simulation and dynamic user-equilibrium traffic assignment (DTA) and get an in-depth look at features for developing and visualizing DTA models. Demonstrations and hands-on exercises drawn from real-world applications are used to cover network representation, transit lines and traffic control, validation of model inputs, and visualization and interpretation of model results and vehicle animations.

This course provides a concise introduction to multimodal networks, visualization and analysis in Emme multimodal transport planning software, including network editing, mapping (2D and 3D) and scenario comparison and evaluation. Participants will develop proficiency with Emme Desktop to edit network data and to create rich maps, charts and tables for reporting, and with Emme Modeller tools for scenario and data management. This course does not cover network modeling procedures.

Emme provides native and comprehensive Python scripting to develop Emme model workflows and results analysis, customize maps and charts, automate reporting, work efficiently with network, matrix and other Emme data, build your own Modeller tools with customized front-ends, monitor and audit model runs and much more. Learn everything you need to know about scripting Emme in this hands-on Python course, delivered as an interactive workshop featuring dozens of useful scripting applications. Participants learn how to author and run scripts in Emme Notebook as well as how to deploy scripts for standalone operation. Participants meeting the course prerequisites will be ready to apply techniques and code snippets on their own projects immediately.

This hands-on, concise introduction to Python syntax, data structures, control flow and style is intended to help prepare participants to follow the Emme Scripting class and to develop the knowledge and skills needed to continue independent exploration of Python scripting capability in Emme.

A condensed introductory course on traffic assignment and analysis using Emme multimodal transport planning software. Gain an understanding of the principles of user equilibrium assignment and how to specify delay functions, generalized costs, multiple classes and common analyses including accessibility matrices, select-link and other path analysis. 

A condensed introductory course on transit assignment and analysis using Emme multimodal transport planning software. Gain an understanding of the principles of Emme transit assignment and how to specify delay functions, generalized costs, multiple transit classes, basic fare integration and common analyses including basic network and accessibility matrix results.

Deepen your knowledge of private transport modelling with Emme. Gain practical experience with common applications and methods, including ramp-to-ramp tolls, distance-based capped tolls, path-based assignment and analysis, assignment with warm start, stochastic traffic assignment, demand adjustment, identifying the best count locations, subarea extraction and exporting to Dynameq.

Deepen your knowledge of public transport modelling with Emme including select-line, traversal, path-based and other analysis procedures, fare modelling, crowded transit assignment, GTFS import and schedule-based transit assignment.

A comprehensive introduction to the essentials of transportation planning and travel demand forecasting with Emme, including theoretical context and hands-on experience with tools for data management and network editing, demand modelling, multimodal network modelling, mapping, analysis and scenario evaluation. Participants will develop proficiency with Emme Desktop to edit network data and to create rich maps, charts and tables for reporting, and with Emme Modeller to prepare and run standard model procedures, including matrix operations and traffic and transit assignments, to monitor runs in the logbook, and to work with tool specifications, snapshots and notebooks. A basic four-step travel demand model is introduced.

Learn how to modify existing worksheets or create brand new ones to fit your needs by adding/reordering layers, saving predefined expressions, syncing parameters, creating layers with user-defined GUI, using substitutions, defining new parameters and attributes, adding legends and more.