Release Note

BTS is withholding the scheduled release of the passenger and combined indexes for January. The passenger index is a statistical estimate of airline passenger travel and other components based on historical trends up to December 2019. As a result, the estimates have yet to fully account for the impact of the coronavirus. Air freight is also a statistical estimate. Since air freight makes up a smaller part of the freight index, the freight TSI is being released as scheduled.

Description

Statisticians use the process of seasonal-adjustment to uncover trends in data. Monthly data, for instance, are influenced by the number of days and the number of weekends in a month as well as by the timing of holidays and seasonal activity. These influences make it difficult to see underlying changes in the data. Statisticians use seasonal adjustment to control for these influences.

Controlling of seasonal influences allows measurement of real monthly changes; short and long term patterns of growth or decline; and turning points. Data for one month can be compared to data for any other month in the series and the data series can be ranked to find high and low points. Any observed differences are “real” differences; that is, they are differences brought about by changes in the data and not brought about by a change in the number of days or weekends in the month, the occurrence or non-occurrence of a holiday, or seasonal activity.

Release Note

BTS is withholding the scheduled release of the passenger and combined indexes for January. The passenger index is a statistical estimate of airline passenger travel and other components based on historical trends up to December 2019. As a result, the estimates have yet to fully account for the impact of the coronavirus. Air freight is also a statistical estimate. Since air freight makes up a smaller part of the freight index, the freight TSI is being released as scheduled.

Description

Statisticians use the process of seasonal-adjustment to uncover trends in data. Monthly data, for instance, are influenced by the number of days and the number of weekends in a month as well as by the timing of holidays and seasonal activity. These influences make it difficult to see underlying changes in the data. Statisticians use seasonal adjustment to control for these influences.

Controlling of seasonal influences allows measurement of real monthly changes; short and long term patterns of growth or decline; and turning points. Data for one month can be compared to data for any other month in the series and the data series can be ranked to find high and low points. Any observed differences are “real” differences; that is, they are differences brought about by changes in the data and not brought about by a change in the number of days or weekends in the month, the occurrence or non-occurrence of a holiday, or seasonal activity.

The Washington site used the reliability guide from Project L02, analysis tools for forecasting reliability and estimating impacts from Project L07, Project L08, and Project C11 as well as the guide on reliability performance measures from the Project L05 product. The Washington site focused on the I-5 and I-405 corridors from Lynnwood to Tukwila (approximately 30 miles long for each corridor running through the Puget Sound metropolitan region), and the SR-522 urban arterial near Seattle. The pilot testing demonstrated that the SHRP 2 Reliability data and analytical products clearly addressed the practical challenges that transportation agencies face when monitoring and analyzing travel time reliability. However, most tools require significant improvements at the application level. Project L38D was intended to evaluate a suite of projects to determine their readiness for implementation. Those projects had a logical structure consisting of data collection, analysis, and project prioritization. The datasets in this zip file, which is 90.5 MB in size, are in support of SHRP 2 reliability project L38D, "Pilot testing of SHRP 2 reliability data and analytical products: Washington." The project report can be accessed via the following URL: https://rosap.ntl.bts.gov/view/dot/3610 This zip file contains 20 Comma Separated Values (CSV) files, which can be opened using most text editing programs.

The datasets in this zip file are in support of Intelligent Transportation Systems Joint Program Office (ITS JPO) report FHWA-JPO-14-134, "Dynamic Mobility Applications Policy Analysis: Policy and Institutional Issues for Integrated Dynamic Transit Operations (IDTO)": https://rosap.ntl.bts.gov/view/dot/3553 The compressed zip file totals 0.6 GB in size. The zip file have been uploaded as-is; no further documentation was supplied by NTL, excepted as noted: All located .docx files were converted to .pdf document files which are an archival format. These pdfs were then added to the zip file alongside the original .docx files. The zip file can be unzipped using any zip compression/decompression software. This zip file contains files in the following formats: .pdf document files which can be read using any pdf reader; .cvs text files which can be read using any text editor; .docx document files which can be read in Microsoft Word and some other word processing programs; . xlsx spreadsheet files which can be read in Microsoft Excel and some other spreadsheet programs [software requirements]. These files were last accessed in 2017. To view the related publication see: https://rosap.ntl.bts.gov/view/dot/3553 Other related publications can also be found in the ITSJPO collection in ROSA P: https://rosap.ntl.bts.gov/cbrowse?pid=dot%3A239&parentId=dot%3A239