NYBPM: Model Background

The concept for a new travel forecasting model to meet federal guidelines started to take shape in late 1990s, advancing to the present model which was developed in phases:


Phase 1: In 2001, the state of the art Best Practice Model (BPM) for 1996 Base Year was developed using information from a regional Household Interview Survey, roadway speed, traffic counts, and external cordon data.
Phase 2: The 1996 Base was replaced by the New York Best Practice Model (NYBPM) 2002 Base. The highway network attributes were updated based on latest traffic volume counts and data, and the transit network attributes were updated based on the Hub-Bound Travel Report. The NYBPM 2002 Base Year was released for distribution in 2004.
Phase 3: In 2009, NYBPM 2005 Base replaced NYBPM 2002 Base  incorporating the latest data available including: 2000 Census and SED data, 2005 Screenline data; and 2005 Hub-Bound Report and 2006 ACS for calibration and validation.
Phase 4: The 2010 Base Year update completed and released in early 2015. This new version includes: A new census based TAZ system, Transportation Network update, Truck and commercial van model, External model, 2040 forecast validation, Development of Time of day choice model, and Improvement of destination choice model.It reflects the update of all regional data, cost, as well as development of additional model procedures.
Phase 5: The NYBPM 2012 Base Year Update will meet the travel demand forecasting and modeling needs in the region and sub-region and will be available to NYMTC members and interested parties. It is expected to be ready for implementation by the end of 2018.


Characteristics

The NYBPM is an activity/tour-based model for regional demand forecasting with the following characteristics:

  • Use of tour (or paired journeys) as the basic unit of modeling
  • Using the conceptual framework of daily activity agenda of individuals accounting for intra-household interactions between members and constraints on peoples' travel in terms of both time and space
  • Use of micro-simulation approach to generate forecasts that are discrete choices for individuals
  • Stop frequency and stop locations are modeled

Non motorized mode is analyzed as a separate mode.