Modeling Autonomous Vehicles with Passive Data

Given the long runways inherent to custom surveys, if the data needed to model autonomous vehicle (AV) behavior have not already been collected, then AV's will arrive before models designed to study their effects. In this study, we use demand from a passive data model coupled with the replanning algorithms in [MATSim](http://matsim.org/) to examine how transportation networks might be affected in the near term by autonomous vehicles.

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Introducing CityCast

To help cities figure out where they need roads, transit, buildings, sidewalks, and other infrastructure, transportation and urban planners must understand three components of mobility:

  1. households and the people in them,
  2. firms and their employees and customers, and
  3. the movements between them.

Traditionally, planners collect information about these using small-sample household surveys.

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Greg Macfarlane joins TF!

Greg Macfarlane joins TF!

I am excited to welcome Greg Macfarlane to Transport Foundry! Greg will be based in Raleigh, NC, where he’ll help with travel modeling and data science.

Greg joins TF from WSP where he worked as a travel modeler and technical principal. Prior to WSP, he was a postdoctoral researcher at Georgia Institute of Technology, where he also earned his PhD in Transportation Systems Engineering and MS in Economics. Greg is certified as a Professional Engineer.

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Beginning our Series on Forecasting with Data-Driven Models

After presenting a data-driven modeling prototype at the 2016 Innovations in Travel Modeling Conference, some asserted that it could not be used in forecasting. Nothing could be further from the truth. We admit, though, that forecasting would perhaps not be in the way we have traditionally done so. Josie Kressner and I put together a presentation to illustrate the wide range of approaches that can be used with data-driven models (or any other type of model, for that matter) at the TRB Transportation Planning Applications Conference this week. Cramming it all into a 15-minute presentation necessitated firehosing the audience with ideas, but few concrete examples were included. So we are going to write a series of blog posts here that discuss each of them in more detail.

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TRB ITM Presentation — Recap

Two weeks ago today, Rick Donnelly of WSP | Parsons Brinckerhoff and Josie Kressner of Transport Foundry presented a case study at the 2016 Transportation Research Board (TRB) Innovations in Travel Modeling Conference in Denver, Colorado. Co-author Greg Macfarlane of WSP | Parsons Brinckerhoff also presented this work last week at the North Carolina Association of Metropolitan Planning Organizations (NCAMPO) in Greensboro, North Carolina.

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Introducing censusr

Here at Transport Foundry we regularly use data from the U.S. Census Bureau to validate our input data and our simulation engine outputs. To make this task easier for us, we wrote an R package that downloads data from the U.S. Census Bureau directly into a user's R environment. We have published this package – censusr – on CRAN under an open-source license. We hope others can use it to streamline their analyses. Contributions to the source code are welcome on GitHub.

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