Temporal Sampling Intervals and Service Frequency Harmonics in Transit Accessibility Evaluation

Recent working paper by my Accessibility Observatory colleagues:

Box plots for sampling strategy performance over all blocks at each sampling frquency. Boxes show inter-quartile range (25th – 50th percentile) with horizontal medial line; whiskers extend 1.5×IQR above and below. Outliers are plotted individually. Mean is indicated by a dot.
Box plots for sampling strategy performance over all blocks at each sampling frquency. Boxes show inter-quartile range (25th – 50th percentile) with horizontal medial line; whiskers extend 1.5×IQR above and below. Outliers are plotted individually. Mean is indicated by a dot.

Abstract

In the context of public transit networks, repeated calculation of accessibility at multiple departure times provides a more robust representation of local accessibility. However, these calculations can require significant amounts of time and/or computing power. One way to reduce these requirements is to calculate accessibility only for a sample of time points over a time window of interest, rather than every one. To date, many accessibility evaluation project have employed temporal sampling strategies, but the effects of different strategies have not been investigated and their performance has not been compared. Using detailed block-level accessibility calculated at 1-minute intervals as a reference dataset, four different temporal sampling strategies are evaluated. Systematic sampling at a regular interval performs well on average but is susceptible to spatially-clustered harmonic error effects which may bias aggregate accessibility results. A constrained random walk sampling strategy provides slightly worse average sample error, but eliminates the risk of harmonic error effects.

Evaluating Perturbation Impact on Key Travel Models

Recently published:

CensusSDC

Introduction: Census Transportation Planning Products (CTPP) are a set of tabulations of the American Community Survey (ACS) designed for transportation planners to estimate, calibrate, and validate transportation forecasting models. Statistical Disclosure Control (SDC) treatments were used for perturbing the ACS 2006-2010 microdata prior to generating the CTPP. The SDC approach maintains ACS respondent confidentiality when releasing aggregate work trip records, since perturbing selected data items reduced the disclosure risk that singly, or in combination, could be used to identify respondents. The table generator for the CTPP processed the SDC-treated microdata for a subset of the CTPP pre-specified tables. The SDC treatment process was developed from an extensive research study, National Cooperative Highway Research Program (NCHRP) 08- 79, which was undertaken in 2010-2011 to develop random perturbation procedures that would produce small area data (e.g., residence to workplace flows for areas approximately the size of Block Groups) that would not violate the Census Bureau’s Title 13 of the U.S. Code, under which the Census Bureau collects its data. Details about the research can be found in NCHRP (2011). During the NCHRP 08-79 research, Westat, under contract to the National Academy of Sciences, worked closely with the transportation planners as represented by the Transportation Research Board represented by the Transportation Research Board and Vanasse Hangen Brustlin (VHB), Inc.

The CTPP tables have been divided into two sets: Set A and Set B. The “Set A” tables were produced from un-perturbed data and “Set B” tables, were produced from perturbed data. The guidelines for generating the Set A and Set B tables were provided to the ACS operations (ACSO) staff. This general approach used perturbed data where tables would have been subjected to DRB disclosure rules, and used the original ACS five-year data for tables where there were no disclosure thresholds. It was designed to retain as much observed ACS data as possible.