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An Effective And Efficient Transportation Network Indicator Summary
stat.montgomerycountymd.gov | Last Updated 2018-07-02T19:09:26.000ZAn Effective And Efficient Transportation Network Indicator Summary. To see details for each benchmark county, go to https://reports.data.montgomerycountymd.gov/dataset/An-Effective-And-Efficient-Transportation-Network-/qxyx-qs79
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RICAPS On-road Transportation Emissions roll-up
datahub.smcgov.org | Last Updated 2018-06-13T15:39:17.000ZData by city showing transportation contribution to greenhouse gas emissions in the County. This data is part of the Regionally Integrated Climate Action Planning Suite (RICAPS) program. The majority of cities used the “in-boundary” methodology that relies on data from the Highway Performance Monitoring System. The inventories for South San Francisco and Unincorporated County use the “origin-destination” methodology from that relies on data from Metropolitan Transportation Commission (MTC). So, directly comparing vehicle miles traveled (VMT) across all cities is not statistically possible. Each city in San Mateo County has the opportunity to develop its own Climate Action Plan (CAP) using tools developed by C/CAG in conjunction with DNV KEMA https://www.dnvgl.com/ and Hara. http://www.verisae.com/default.aspx. This project was funded by grants from the Bay Area Air Quality Management District (BAAQMD) and Pacific Gas and Electric Company (PG&E). Climate Action Plans developed from these tools will meet BAAQMD's California Environmental Quality Act (CEQA) guidelines for a Qualified Greenhouse Gas Reduction Strategy. For more information, please see the RICAPS site: http://www.smcenergywatch.com/progress_report.html
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RICAPS On-road Transportation Emissions roll-up 2
datahub.smcgov.org | Last Updated 2019-05-22T23:00:49.000ZData by city showing transportation contribution to greenhouse gas emissions in the County. This data is part of the Regionally Integrated Climate Action Planning Suite (RICAPS) program. The majority of cities used the “in-boundary” methodology that relies on data from the Highway Performance Monitoring System. The inventories for South San Francisco and Unincorporated County use the “origin-destination” methodology from that relies on data from Metropolitan Transportation Commission (MTC). So, directly comparing vehicle miles traveled (VMT) across all cities is not statistically possible. Each city in San Mateo County has the opportunity to develop its own Climate Action Plan (CAP) using tools developed by C/CAG in conjunction with DNV KEMA https://www.dnvgl.com/ and Hara. http://www.verisae.com/default.aspx. This project was funded by grants from the Bay Area Air Quality Management District (BAAQMD) and Pacific Gas and Electric Company (PG&E). Climate Action Plans developed from these tools will meet BAAQMD's California Environmental Quality Act (CEQA) guidelines for a Qualified Greenhouse Gas Reduction Strategy. For more information, please see the RICAPS site: http://www.smcenergywatch.com/progress_report.html
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Vital Signs: Transit Cost-Effectiveness – by operator
data.bayareametro.gov | Last Updated 2018-07-06T18:04:51.000ZVITAL SIGNS INDICATOR Transit Cost-Effectiveness (T13) FULL MEASURE NAME Net cost per transit boarding (cost per boarding minus fare per boarding) LAST UPDATED May 2017 DESCRIPTION Transit cost-effectiveness refers to both the total and net costs per transit boarding, both of which are adjusted to reflect inflation over time. Net costs reflect total operating costs minus farebox revenue (i.e. operating costs that are not directly funded by system users). The dataset includes metropolitan area, regional, mode, and system tables for net cost per boarding, total cost per boarding, and farebox recovery ratio. DATA SOURCE Federal Transit Administration: National Transit Database http://www.ntdprogram.gov/ntdprogram/data.htm Bureau of Labor Statistics: Consumer Price Index http://www.bls.gov/data/ CONTACT INFORMATION vitalsigns.info@mtc.ca.gov METHODOLOGY NOTES (across all datasets for this indicator) Simple modes were aggregated to combine the various bus modes (e.g. rapid bus, express bus, local bus) into a single mode to avoid incorrect conclusions resulting from mode recoding over the lifespan of NTD. For other metro areas, operators were identified by developing a list of all urbanized areas within a current MSA boundary and then using that UZA list to flag relevant operators; this means that all operators (both large and small) were included in the metro comparison data. Financial data was inflation-adjusted to match 2015 dollar values using metro-specific Consumer Price Indices.
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Vital Signs: Commute Mode Choice (by Place of Residence) – Bay Area
data.bayareametro.gov | Last Updated 2020-05-20T21:50:47.000ZVITAL SIGNS INDICATOR Commute Mode Choice (T1) FULL MEASURE NAME Commute mode share by residential location LAST UPDATED April 2020 DESCRIPTION Commute mode choice, also known as commute mode share, refers to the mode of transportation that a commuter uses to travel to work, such as driving alone, biking, carpooling or taking transit. The dataset includes metropolitan area, regional, county, city and census tract tables by place of residence. DATA SOURCE U.S. Census Bureau: Decennial Census (1960-2000) - via MTC/ABAG Bay Area Census http://www.bayareacensus.ca.gov/transportation/Means19802000.htm U.S. Census Bureau: American Community Survey Form B08301 (2006-2018; place of residence) www.api.census.gov CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) For the decennial Census datasets, the breakdown of auto commuters between drive alone and carpool is not available before 1980. "Other" includes bicycle, motorcycle, taxi, and other modes of transportation. For the American Community Survey datasets, 1-year rolling average data was used for metros, region, and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Regional mode shares are population-weighted averages of the nine counties’ modal shares. "Auto" includes drive alone and carpool for the simple data tables and is broken out in the detailed data tables accordingly, as it was not available before 1980. “Transit” includes public operators (Muni, BART, etc.) and employer-provided shuttles (e.g., Google shuttle buses). "Other" includes motorcycle, taxi, and other modes of transportation; bicycle mode share was broken out separately for the first time in the 2006 data and is shown in the detailed data tables. Census tract data is not available for tracts with insufficient numbers of residents or workers. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary MSAs for the nine other major metropolitan areas.
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The Omnibus Surveys - Omnibus Monthly Survey 2002 Dec SAS Data
datahub.transportation.gov | Last Updated 2018-12-19T00:13:38.000ZThe Omnibus Surveys are a convenient way to get very quick input on transportation issues; to see who uses what, how they use it, and how users view it, and what they think about it; and to gauge public satisfaction with the transportation system and government programs.The series of surveys include: A monthly household survey of 1,000 households each month, which collects data on core questions about general travel experiences, satisfaction with the system, and some demographic data. Targeted surveys to address special transportation issues, as the U.S. Department of Transportation (DOT) operating administrations need them
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The Omnibus Surveys - Omnibus Monthly Survey 2002 May EXCEL Data
datahub.transportation.gov | Last Updated 2018-12-19T00:13:36.000ZThe Omnibus Surveys are a convenient way to get very quick input on transportation issues; to see who uses what, how they use it, and how users view it, and what they think about it; and to gauge public satisfaction with the transportation system and government programs.The series of surveys include: A monthly household survey of 1,000 households each month, which collects data on core questions about general travel experiences, satisfaction with the system, and some demographic data. Targeted surveys to address special transportation issues, as the U.S. Department of Transportation (DOT) operating administrations need them
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Contribution to High Asia Runoff from Ice and Snow (CHARIS) Melt Model Output, 2001 - 2014, Version 1
data.nasa.gov | Last Updated 2022-01-17T05:15:59.000ZThis data set contains input and output data for temperature index (TI) model runs completed for the Contributions to High Asia Runoff from Ice and Snow (CHARIS) project at NSIDC in 2018 and 2019. The input data are the area of snow on land, snow on ice, and exposed glacier ice as well as surface air temperature. These inputs are used to model the volumes of melt runoff from the snow on land, snow on ice, and exposed glacier ice in certain areas of High Mountain Asia.
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Vital Signs: Time in Congestion - Corridor (Updated October 2018)
data.bayareametro.gov | Last Updated 2018-10-24T00:31:33.000ZVITAL SIGNS INDICATOR Time Spent in Congestion (T7) FULL MEASURE NAME Time Spent in Congestion LAST UPDATED October 2018 DATA SOURCE MTC/Iteris Congestion Analysis No link available CA Department of Finance Forms E-8 and E-5 http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-8/ http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-5/ CA Employment Division Department: Labor Market Information http://www.labormarketinfo.edd.ca.gov/ CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) Time spent in congestion measures the hours drivers are in congestion on freeway facilities based on traffic data. In recent years, data for the Bay Area comes from INRIX, a company that collects real-time traffic information from a variety of sources including mobile phone data and other GPS locator devices. The data provides traffic speed on the region’s highways. Using historical INRIX data (and similar internal datasets for some of the earlier years), MTC calculates an annual time series for vehicle hours spent in congestion in the Bay Area. Time spent in congestion is defined as the average daily hours spent in congestion on Tuesdays, Wednesdays and Thursdays during peak traffic months on freeway facilities. This indicator focuses on weekdays given that traffic congestion is generally greater on these days; this indicator does not capture traffic congestion on local streets due to data unavailability. This congestion indicator emphasizes recurring delay (as opposed to also including non-recurring delay), capturing the extent of delay caused by routine traffic volumes (rather than congestion caused by unusual circumstances). Recurring delay is identified by setting a threshold of consistent delay greater than 15 minutes on a specific freeway segment from vehicle speeds less than 35 mph. This definition is consistent with longstanding practices by MTC, Caltrans and the U.S. Department of Transportation as speeds less than 35 mph result in significantly less efficient traffic operations. 35 mph is the threshold at which vehicle throughput is greatest; speeds that are either greater than or less than 35 mph result in reduced vehicle throughput. This methodology focuses on the extra travel time experienced based on a differential between the congested speed and 35 mph, rather than the posted speed limit. To provide a mathematical example of how the indicator is calculated on a segment basis, when it comes to time spent in congestion, 1,000 vehicles traveling on a congested segment for a 1/4 hour (15 minutes) each, [1,000 vehicles x ¼ hour congestion per vehicle= 250 hours congestion], is equivalent to 100 vehicles traveling on a congested segment for 2.5 hours each, [100 vehicles x 2.5 hour congestion per vehicle = 250 hours congestion]. In this way, the measure captures the impacts of both slow speeds and heavy traffic volumes. MTC calculates two measures of delay – congested delay, or delay that occurs when speeds are below 35 miles per hour, and total delay, or delay that occurs when speeds are below the posted speed limit. To illustrate, if 1,000 vehicles are traveling at 30 miles per hour on a one mile long segment, this would represent 4.76 vehicle hours of congested delay [(1,000 vehicles x 1 mile / 30 miles per hour) - (1,000 vehicles x 1 mile / 35 miles per hour) = 33.33 vehicle hours – 28.57 vehicle hours = 4.76 vehicle hours]. Considering that the posted speed limit on the segment is 60 miles per hour, total delay would be calculated as 16.67 vehicle hours [(1,000 vehicles x 1 mile / 30 miles per hour) - (1,000 vehicles x 1 mile / 60 miles per hour) = 33.33 vehicle hours – 16.67 vehicle hours = 16.67 vehicle hours]. Data sources listed above were used to calculate per-capita and per-worker statistics. Top congested corridors are ranked by total vehicle hours of delay, meaning that the highlighted corridors reflect a combination of slow speeds and heavy t
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MODIS/Terra+Aqua BRDF/Albedo Gap-Filled Snow-Free Daily L3 Global 30ArcSec CMG V006
data.nasa.gov | Last Updated 2022-01-17T05:41:21.000ZThe Daily Moderate Resolution Imaging Spectroradiometer (MODIS) (Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) 30 arc second, Global Gap-Filled, Snow-Free, (MCD43GF) Version 6 is derived from the 30 arc second Climate Modeling Grid (CMG) MCD43D Version 6 product suite, with additional processing to provide a gap-filled, snow-free product. The highest quality full inversion values were used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the quality layer for each band. To generate a snow-free product, pixels with ephemeral snow were removed using the MCD43D41 (https://doi.org/10.5067/MODIS/MCD43D41.006) snow flags. The underlying MCD43D utilizes a BRDF model derived from all available high quality cloud clear reflectance data over a 16 day moving window centered on and emphasizing the daily day of interest (the ninth day of each retrieval period as reflected in the Julian date in the filename). This 30arc second BRDF model is then used to produce the Albedo and NBAR products (MCD43D). These BRDF model parameters are computed for MODIS spectral bands 1 through 7 (0.47 um, 0.55 um, 0.67 um, 0.86 um, 1.24 um, 1.64 um, 2.1 um), as well as the shortwave infrared band (0.3-5.0um), visible band (0.3-0.7 um), and near-infrared (0.7-5.0 um) broad bands. The MCD43GF product includes 67 layers containing black-sky albedo (BSA) at local solar noon, isotropic (ISO), volumetric (VOL), geometric (GEO), quality (QA), Nadir BRDF-Adjusted Reflectance (NBAR) at local solar noon, and white-sky albedo (WSA). Due to the large file size, each data layer is distributed as a separate HDF file. Users are encouraged to download the quality layers for each of the 10 bands to check quality assessment information before using the BRDF/Albedo data. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications (https://www.umb.edu/spectralmass/terra_aqua_modis/v006). The MCD43 product is not recommended for solar zenith angles beyond 70 degrees. The MODIS BRDF/Albedo products have achieved stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation. Improvements/Changes from Previous Versions Observations are weighted to estimate the BRDF/Albedo on the ninth day of the 16-day period. * MCD43 products use the snow status weighted to the ninth day instead of the majority snow/no-snow observations from the 16-day period. * Better quality at high latitudes from use of all available observations for the acquisition period. Collection 5 used only four observations per day. * The MCD43 products use L2G-lite surface reflectance as input. * In cases where insufficient high-quality reflectances are obtained, a database with archetypal BRDF parameters is used to supplement the observational data and perform a lower quality magnitude inversion. This database is continually updated with the latest full inversion retrieval for each pixel. * CMG Albedo is estimated using all the clear-sky observations within the 1,000 m grid as opposed to aggregating from the 500 m albedo. Important Quality Information The incorrect representation of the aerosol quantities (low average high) in the C6 MYD09 and MOD09 surface reflectance products may have impacted down stream products particularly over arid bright surfaces (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/displayCase.cgi?esdt=MOD09&caseNum=PM_MOD09_20010&caseLocation=cases_data&type=C6). This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the