Data from the U.S. Department of Commerce, Bureau of Economic Analysis, National Income Product Accounts on real personal consumption expenditures on bicycles and accessories and data from the U.S. Department of Commerce, Census Bureau, USA Trade Online on U.S. imports of non-motorized bicycles and other cycles, including delivery tricycles. Data by month.

The objective of this project was to develop system designs for programs to monitor travel time reliability and to prepare a guidebook that practitioners and others can use to design, build, operate, and maintain such systems. Generally, such travel time reliability monitoring systems are built on top of existing traffic monitoring systems. The focus of this project was on travel time reliability. The data from the monitoring systems developed in this project – from both public and private sources –included, wherever cost-effective, information on the seven sources of non-recurring congestion. This data was used to construct performance measures or to perform various types of analyses useful for operations management as well as performance measurement, planning, and programming. The work on Project L02 began with a research phase. The primary results of Project L02 research were a guidebook and a final report. An additional contribution of the research showed that it may be possible to add the variability of travel time from one segment to another. Another contribution of the research was the development of a queuing point model, an application that analytically determines travel time reliability over a freeway segment. The supporting zip file contains case study datasets for SHRP 2 Report S2-L02-RR1: Establishing Monitoring Programs for Travel Time Reliability, https://rosap.ntl.bts.gov/view/dot/4039 The files are in comma separated value (.csv) format. The compressed zip file is 84.15 MB. These files can be unzipped using any zip compression/decompression software. The .csv files can be read with any basic text editor.

This table shows the number and percent of people in the United States potentially exposed to different levels of noise from combined aviation and road sources.

The Bureau of Transportation Statistics (BTS) Border Crossing Data provide summary statistics for inbound crossings at the U.S.-Canada and the U.S.-Mexico border at the port level. Data are available for trucks, trains, containers, buses, personal vehicles, passengers, and pedestrians. Border crossing data are collected at ports of entry by U.S. Customs and Border Protection (CBP). The data reflect the number of vehicles, containers, passengers or pedestrians entering the United States. CBP does not collect comparable data on outbound crossings. Users seeking data on outbound counts may therefore want to review data from individual bridge operators, border state governments, or the Mexican and Canadian governments.

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.