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Simulating Transport for Realistic Engineering Education and Training

Summary

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.

Supervisors

Professor David Levinson, Dr Mohsen Ramezani Ghalenoei.

Research location

Civil Engineering

Program type

Masters/PHD

Synopsis

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.

Additional information

  • Use of research technique / methodology / technology
  • Potential topics of interest for the research opportunity
  • Current PHD and/or Masters topics
  • Eligibility criteria / candidate profile
  • Scholarship(s)  /  funding available

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Opportunity ID

The opportunity ID for this research opportunity is 2229

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