Content Type: News Item

A new NITC report offers a multimodal framework for transportation impact analysis – a welcome tool for professionals in many cities seeking more detailed data about non-drivers.

Improving Trip Generation Methods for Livable Communities, a research project headed by Kelly Clifton of Portland State University and Nico Larco of the University of Oregon, is the latest effort in an ongoing collaboration to create more open sourced, widely available data about non-motorized road users.

Over the last decades, cities have become more invested in fostering the conditions to support walking, biking and public transit.

The land development process presents a unique challenge.

Prior to a zoning change or new development, someone has to determine what its impact on the transportation system will be, and whether upgrades will be necessary to accommodate travelers to the new destination. Trip generation is the first step in the conventional transportation forecasting process.

Current trip generation methods used by engineers across the country tend to focus on motorized modes.

Without reliable trip generation rates for anyone but drivers, the transportation impact is difficult to predict. Certain land uses will draw far more walkers,...

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Content Type: Blog entry

By Kristina M. Currans

In August 2014, the Institute of Transportation Engineers released the 3rd edition of the Trip Generation Handbook, a 352-page text that has traditionally, until only recently, provided guidance on estimating vehicle trips generated from new development. Among other updates, this new edition includes new chapters summarizing the most recent research and methods developed allowing users to account for people (not just vehicles) in trip generation estimation practices. This industry’s transition to estimating and understanding the “people” traveling to development has been in high demand from communities looking to accommodate multimodal travel, but there still remain a number of limitations in the guidelines presented.

One such limitation is that we currently have few data that allow us to directly estimate person trip rates. Instead we are often required to: (1) estimate vehicle trips using suburban vehicle trip rate data, defined as a “base rate”; (2) convert the vehicle trips into an estimated person trips using an assumed mode share and vehicle occupancy rate from the ITE’s suburban vehicle-oriented data, and; (3) reallocate the estimated person trips into different modes (bike, walk, drive, transit, etc.) based on the urban context of the development. This “direct mode share adjustment” forces the user, first, to assume that person trip rates do not vary across urban contexts and, second, to...

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