Understanding and predicting travel demand and travel patterns is one of the most important but also most challenging tasks of transport engineers, planners, and geographers. Travel forecasting models are complex and nearly impossible to use in the classroom. The Internet provides a more attractive frontier for computer simulation in transport education. The University of Minnesota has previously developed a set of web-based simulation modules for use in the classroom to improve instruction for the Introduction to Transport Engineering course. These open source, Java-based, platform-independent learning modules have been deployed for testing and are in use in over a dozen transport classes across the United States and Australia and are publicly available at (http://street.umn.edu). This research will systematize, update, extend, and test in the classroom this new educational platform.
In STREET, one module, ADAM (Agent-based Demand and Assignment Model) simulates travel patterns on a modally-independent street network based on microscopic decision- making by each traveler. It uses a stylized network, which is therefore smaller (but computationally faster) than realistic urban networks. The platform should be extended in several directions. One directions is to:• Expand the demand model with public transit lines (a series of nodes and links with some parameters for headway, etc.) using a hypernetworks approach, following the standard GTFS specification for transit network coding; • Implement a realistic set of preferences for travelers regarding weights associated with alternative transport technologies, so that mode share emerges from the route choices of individual travelers; • Incorporate land use parameters more explicitly for trip generation and attraction; • Compute network measures as described. By adding/deleting nodes or links, the network structure implicitly changes, so do network measures and observed travel behavior.
The opportunity ID for this research opportunity is 2229