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Posted by on in TRB 2016

Today is my last day at the conference and while it wasn't so jam-packed as the others, it might have been my favorite.

This morning I and a client from the MPO in Colorado Springs (PPACG), Craig Casper, presented our poster on the sensitivity of project rankings to underlying land use and growth assumption.

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While standing in front of a poster for 2 hours has the potential to be a little dull, this time around I actually benefited from a remarkable window into the MPO planning world. Not only did I get to learn more about the multicriteria ranking process used by PPACG (to which our TREDIS analysis provided just one input), I also got to watch members from agencies across the country share and discuss best practices for planning and project evaluation.

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MPOs, like many public sector agencies, grapple with conflicting demands from many different constituents and oversight entities. They also struggle, like the rest of us, with the reality of uncertain future conditions and the limitations of technical forecasting methods. But rather than throw our hands up in the air, we work ever so incrementally towards better information--from both models and public input processes--and ultimately towards better and more transparent decisions.

Not such a bad thought to leave with.

Until next year!

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If you get enough biases, you might get a mosaic of an approximate reality

On the challenges of reported economic impacts (Transportation and Economic Development Committee meeting)

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Posted by on in TRB 2016

This afternoon I attended Session 343 on the Role of Transit in Creating a More Equitable Society. Each presentation focused in one way or another on assessing the adequate provision of transit service from a spatial and socioeconomic perspective, as well as the role of transit and automobiles in facilitating access to jobs, services, health care, groceries, day care, and education.

What struck me about the session--and in particular the discussion afterwards--is that we're getting to the point as a community of practitioners where data and analytical capabilities are no longer the barriers to implementation they once were. And so now we have the chance to really talk about which measures are most instructive--to researchers, policy makers, the public--as opposed to which are most simply possible.

Do we want aggregate indicators that take into account the needs of many different population groups? Or do we want individual analyses reproduced over and over again for small scale market segments of the population, with each telling a more specific but more compelling story? Do we want to try to match individuals to their specific needs (e.g. low wage health workers to low-wage healthcare jobs), or should we opt for more generalized but also easier to understand aggregate measures (e.g. total job accessibility from residential areas)?

One presentation even surprised me with a new twist on some very basic concepts. We all know the burden of transportation comes from actual out-of-pocket costs and from time costs. Nevertheless, most accessibility analyses are pivoted off of travel times, alone. Researchers from McGill (Presentation 16-3715) demonstrated how simply adding fare cost to travel time information can yield a considerably different picture of job accessibility:

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A, on the left: job accessibility using a "1 hourly wage" threshold (a combination of fare costs and monetized travel time, using minimum wage hourly rates); B, on the right: simple 1 hr travel time thresholds. (Montreal).

 

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Posted by on in TRB 2016
Presentation on the effect of on-time performance on ridership and revenue (Mark Feldman, Session 233):

The meaning of on-time performance depends on who you ask. Amtrak defines on-time performance using a minutes late threshold that varies by length of the route. But if you're a traveler, 20 minutes late may be a huge deal, or not matter much at all-- it all depends on the purpose and length of your trip, and the flexibility of your plans.

This is a challenge more broadly: internal agency performance measures do not always map to the aspects of performance that driver user behavior and ultimately the broader effects on society and the economy. Both sides of the coin are critical to our ability to prioritize improvements.
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