Accessibility vs Demand: Bringing a Map to a Modeling Fight

When planners quantitatively prioritize transit investments, they can use either accessibility measures or ridership measures, or some combination of the two. Accessibility calculators count how easily people are able to reach opportunities (e.g. jobs) by transit, and ridership models attempt to forecast likely passengers. Historically, agencies have used both methods for different tasks, though recent criticism of ridership models coupled with the availability of new web-based accessibility calculators has made a comparison of each method important for planners to understand.

As part of Transportation Camp DC 2018, we challenged Conveyal to debate the strengths of accessibility calculators, and Anson Stewart came forward. I defended ridership models. During the debate, we showed off Conveyal's Analysis software as well as gave the first demonstration of CityCast, our web-based travel demand model that estimates ridership.

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Modeling Private Autonomous Vehicles

We're exploring how to model demand for autonomous vehicles (AV's) with a passive data model like
CityCast. We've structured our investigations following the thoughts of Lauren Isaac, who proposes that the positive or negative effects of AV's largely rest on whether AV's end up being privately  owned or shared among several users. We covered the shared scenarios in our last post,
and we're now back as promised with private scenarios.
 

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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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