- API data.bts.gov | Last Updated 2019-05-24T12:30:48.000Z
The goal of the SHRP 2 Project L33 Validation of Urban Freeway Models was to assess and enhance the predictive travel time reliability models developed in the SHRP 2 Project L03, Analytic Procedures for Determining the Impacts of Reliability Mitigation Strategies. SHRP 2 Project L03, which concluded in 2010, developed two categories of reliability models to be used for the estimation or prediction of travel time reliability within planning, programming, and systems management contexts: data-rich and data-poor models. The objectives of Project L33 were the following: • The first was to validate the most important models – the “Data Poor” and “Data Rich” models with new datasets. • The second objective was to assess the validation outcomes to recommend potential enhancements. • The third was to explore enhancements and develop a final set of predictive equations. • The fourth was to validate the enhanced models. • The last was to develop a clear set of application guidelines for practitioner use of the project outputs. The datasets in these 5 zip files are in support of SHRP 2 Report S2-L33-RW-1, Validation of Urban Freeway Models, https://rosap.ntl.bts.gov/view/dot/3604 The 5 zip files contain a total of 60 comma separated value (.csv) files. The compressed zip files total 3.8 GB in size. The files have been uploaded as-is; no further documentation was supplied. These files can be unzipped using any zip compression/decompression software. The files can be read in any simple text editor. [software requirements] Note: Data files larger than 1GB each. Direct data download links: L03-01: https://doi.org/10.21949/1500858 L03-02: https://doi.org/10.21949/1500868 L03-03: https://doi.org/10.21949/1500869 L03-04: https://doi.org/10.21949/1500870 L03-05: https://doi.org/10.21949/1500871
Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies [supporting datasets]data.bts.gov | Last Updated 2019-05-24T12:42:20.000Z
The objective of this project was to develop technical relationships between reliability improvement strategies and reliability performance metrics. This project defined reliability, explained the importance of travel time distributions for measuring reliability, and recommended specific reliability performance measures. The research reexamined the contribution of the various causes of nonrecurring congestion on urban freeway sections, however, some attention was also given to rural highways and urban arterials). Numerous actions that can potentially reduce nonrecurring congestion were identified with an indication of their relative importance. Models for predicting nonrecurring congestion were developed using three methods, all based on empirical procedures: The first involved before and after studies; the second was termed a 'data poor' approach and resulted in a parsimonious and easy-to-apply set of models; the third was entitled a 'data rich model' and used cross-section inputs including data on selected factors known to directly affect nonrecurring congestion. An important conclusion of the study is that actions to improve operations, reduce demand, and increase capacity all can improve travel time reliability. The 3 attached zip files contains comma separated value (.csv) files of data to support SHRP 2 report S2-L03-RR-1, Analytical procedures for determining the impacts of reliability mitigation strategies.Zip size is 1.83 MB. Files were accessed in Microsoft Excel 2016. Data will be preserved as is. To view publication see: https://rosap.ntl.bts.gov/view/dot/3605
- API data.bts.gov | Last Updated 2020-07-17T12:50:26.000Z
Customer on-time performance is the percentage of customers who arrive at their detraining stations on time. An Acela train is late when it arrives at a station more than 10 minutes after its scheduled time; a Northeast Regional or State-Supported train is late when it arrives more than 15 minutes after its scheduled time. For years before FY 2018, on-time performance is the percentage of total train arrivals on-time at each station, with every arrival weighted equally. Amtrak reports system performance in monthly Host Railroad Reports.
- API data.bts.gov | Last Updated 2020-08-06T06:05:29.000Z
Monthly Transportation Statistics is a compilation of national statistics on transportation. The Bureau of Transportation Statistics brings together the latest data from across the Federal government and transportation industry. Monthly Transportation Statistics contains over 50 time series from nearly two dozen data sources.
- API data.bts.gov | Last Updated 2020-07-06T21:35:01.320Z
Investment in transportation
- API data.bts.gov | Last Updated 2020-07-16T15:55:17.000Z
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.
Effectiveness of Different Approaches to Disseminating Traveler Information on Travel Time Reliability [supporting datasets]data.bts.gov | Last Updated 2019-05-24T12:39:55.000Z
Travel time reliability information includes static data about traffic speeds or trip times that capture historic variations from day to day, and it can help individuals understand the level of variation in traffic. Unlike real-time travel time information, which provides a current snapshot of trip conditions and travel time, reliability information can be used to plan and budget in advance for a trip. Travel time reliability information can improve urban mobility by conveying reliability-related information to system users so that they can make informed decisions about their travel. Data files in this zipped package include macro-enabled Microsoft Excel spreadsheets. These spreadsheets operate as interactive games. To save them into open formats would destroy this functionality. Therefore the macro-enabled spreadsheets are left as-is. There were opened prior to ingest in this repository using Microsoft Excel 2010. This dataset supports SHRP 2 report S2-L14-RW-1, Effectiveness of different approaches to disseminating traveler information on travel time reliability. Zip contains 628 MB. Files were accessed with Microsoft Excel 2016. Data will be preserved as it is. For the publication see: https://rosap.ntl.bts.gov/view/dot/3607
- API data.bts.gov | Last Updated 2020-06-16T20:36:52.000Z
The Federal Highway Administration's National Highway Construction Cost Index (NHCCI) is a quarterly price index intended to measure the average changes in the prices of highway construction costs over time and to convert current-dollar highway construction expenditures to real dollar expenditures.
- API data.bts.gov | Last Updated 2020-07-06T21:13:51.850Z
Economic concepts related to transportation investment