This commuter mode share data shows the estimated percentages of commuters in Champaign County who traveled to work using each of the following modes: drove alone in an automobile; carpooled; took public transportation; walked; biked; went by motorcycle, taxi, or other means; and worked at home. Commuter mode share data can illustrate the use of and demand for transit services and active transportation facilities, as well as for automobile-focused transportation projects.

Driving alone in an automobile is by far the most prevalent means of getting to work in Champaign County, accounting for over 69 percent of all work trips in 2023. This is the same rate as 2019, and the first increase since 2017, both years being before the COVID-19 pandemic began.

The percentage of workers who commuted by all other means to a workplace outside the home also decreased from 2019 to 2021, most of these modes reaching a record low since this data first started being tracked in 2005. The percentage of people carpooling to work in 2023 was lower than every year except 2016 since this data first started being tracked in 2005. The percentage of people walking to work increased from 2022 to 2023, but this increase is not statistically significant.

Meanwhile, the percentage of people in Champaign County who worked at home more than quadrupled from 2019 to 2021, reaching a record high over 18 percent. It is a safe assumption that this can be attributed to the increase of employers allowing employees to work at home when the COVID-19 pandemic began in 2020.

The work from home figure decreased to 11.2 percent in 2023, but which is the first statistically significant decrease since the pandemic began. However, this figure is still about 2.5 times higher than 2019, even with the COVID-19 emergency ending in 2023.

Commuter mode share data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Means of Transportation to Work.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (18 September 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (10 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (14 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).

This dataset supports measure M.A.1 of SD 2023. The source of the data is the American Community Survey. Each row is the five year estimate for Means of Transportation to Work for Austin. This dataset can be used to gain insight into the estimated mode split for the commute to work in Austin.

View more details and insights related to this measure on the story page: https://data.austintexas.gov/stories/s/hm3r-8jfy

The research project 'Vehicle choice for long-distance personal travel' was carried out on behalf of Transportforskningsdelegationen (TFD) and the aim was to clarify which factors influence the choice of means of transport for long-distance private personal travel. The survey was conducted as a sample survey in two stages. Stage 1 consisted of a postal questionnaire, the purpose of which was to identify individuals who had undertaken a longer private trip during a certain period of time and were thus assumed to have made a choice of means of transport. Stage 2 consisted of a personal interview with a selection of these individuals. Long-distance travel refers here to trips of at least 100 kilometres one-way. Private travel means all trips, except business, work commuting, educational and military service trips. Longer private trips that connect to charter trips abroad are also included. The survey only studies trips within the Nordic countries. The interview contained questions about the purpose of the trip, number of nights spent away, type of means of transport and which company you travelled with. The respondent was also asked to indicate whether they had considered other means of transport and what importance cost, and travel time had on the choice of means of transport.

Here you can find symbols and pictograms for all transport modes to use in your apps, products and other projects. Symbols and icons are available in various formats, while all can be found as vector files that can be opened directly in software such as Adobe Illustrator.

How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our mobility statistics program.

The "Trips by Distance" data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day.

Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air.

The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed.

These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

These data are made available under a public domain license. Data should be attributed to the "Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland and the United States Bureau of Transportation Statistics."

Daily data for a given week will be uploaded to the BTS website within 9-10 days of the end of the week in question (e.g., data for Sunday September 17-Saturday September 23 would be updated on Tuesday, October 3). All BTS visualizations and tables that rely on these data will update at approximately 10am ET on days when new data are received, processed, and uploaded.

The methodology used to develop these data can be found at: https://rosap.ntl.bts.gov/view/dot/67520.

This EnviroAtlas dataset portrays the percent of workers who commute to work using various modes, and the percent who work from home within each Census Block Group (CBG) during 2008-2012. Data were compiled from the Census ACS (American Community Survey) 5-year Summary Data. The commute modes are the travel methods workers use to get from home to work. The commute modes mapped include private vehicle use (drive alone or carpooling), public transit, bicycling, and walking. Workers who work from home were also reported. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This dataset details stations for each agency and mode for stations reported to the National Transit Database in report years 2022 and 2023. These data include the type of facility and the decade in which it was built.

In many cases, stations are reported by each mode and type of service that uses them. For example, a single station used by bus - directly operated, bus - purchased transportation, and commuter bus - directly operated would be reported three times. For more detail, please see the NTD Policy Manual.

Rural reporters do not report passenger stations and are not included in this file. Modes Demand Response, Demand Response - Taxi, Vanpool, and Publico also do not report stations and are also excluded.

NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 and 2023 Facility Inventory database files.

In years 2015-2021, you can find this data in the "Stations" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data.

If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.

Below are frequency comparisons of different models with experiment Note Modeshapes aren't very descriptive for higher modes. There is coupling between them so this is just an approximate naming scheme. See modeshape plots for more details. PDF files are provided with figures of the modeshapes for selected FEM TET10 model (Nov 2011) (CASE 10) Hex8 Modeshapes (CASE 4) TET10 no modelcart (CASE 5) HIRENASD TET model with modelcart - new OML HIRENASD HEX 8 Wing only model Mode 1 Mode 1 Mode 2 Mode 2 Mode 3 Mode 3 Mode 4 Mode 4 Mode 5 Mode 5 Mode 6 Mode 6 Mode 7 Mode 7 Mode 8 Mode 8 Mode 9 Mode 9 Mode 10 Mode 10 Mode 11 Mode 12

1H-pyrrolo[3,2-h]quinoline [Gorski, 2012] was optimized in ORCA v3.0.3 [Neese, 2012; http://orcaforum.cec.mpg.de] using RPBE [Perdew, 1992 and 1996] with the def2-TZVP basis sets [Weigend, 1998], and the def2-TZVP/J auxiliary bases [Weigend, 2006] for the RI approximation [Vahtras, 1992]. The nuclear Hessian, normal modes, and harmonic vibrational frequencies were then computed using analytical (ANFREQ) and numerical (NUMFREQ) methodologies. The numerical Hessians were computed with nuclear (Cartesian) displacement increments ranging from 0.0001 Bohr to 0.1 Bohr. The geometry optimization was conducted using the parameters of the TIGHTOPT simple input keyword; KS-SCF and CP-SCF calculations used VERYTIGHTSCF thresholds.

An analysis of the deviation of normal modes and harmonic frequencies for each numerical Hessian computation from the analytical Hessian results was presented as a single-figure presentation (SFP) at the 2016 Virtual Winterschool on Computational Chemistry (http://winterschool.cc). This SFP can be found at doi:10.5281/zenodo.44807.

For the initial OPT and ANFREQ, the following files are provided:
PQ_OPT_AFQ.engrad -- Gradient data
PQ_OPT_AFQ.gbw -- Wavefunction
PQ_OPT_AFQ.hess -- Hessian data
PQ_OPT_AFQ.out -- Computation output
PQ_OPT_AFQ.trj -- Optimization trajectory (multi-frame OpenBabel XYZ)
PQ_OPT_AFQ.txt -- ORCA input file
PQ_OPT_AFQ.xyz -- Optimized geometry

For each following NUMFREQ, the following files are provided, where the number at the end of the filename indicates the nuclear displacement increment in Bohrs:
PQ_NFQ_0.####.hess -- Hessian data
PQ_NFQ_0.####.out -- Computation output
PQ_NFQ_0.####.txt -- ORCA input file

Since ORCA does not report non-mass-weighted normal modes, these are provided separately for each calculation as modes_0.####.csv (modes_A.csv for the analytical Hessian.)

The dot products of each normal mode from the numerical Hessian computations with the corresponding mode in the analytical Hessian calculation (modes ordered as presented in the ORCA output) are provided in modes_dot_products.csv. The MAD of these data are plotted in the LH figure of the above-referenced SFP.

For those numerical Hessian computations with normal modes out of sequence relative to the analytical calculation, permutation matrices to bring them back in accord with the analytical Hessian modes are included as swaps_0.####.csv.

A table of the calculated vibrational frequencies for each computation, re-ordered as necessary to bring the normal modes in accord with the analytical Hessian run, is included as freqs_swapped.csv. The MAD and maximum absolute deviation of these data are plotted in the RH figure of the above-referenced SFP.

The Southern Annular Mode (SAM) is an index that describes climate variation around the South Pole and Antarctica, as far north as New Zealand. It indicates short-term climate variations that can influence New Zealand’s climate. Such climate variations can impact on our environment, industries, and recreational activities. The variation is caused by the movement of a low-pressure belt that generates westerly winds. During a negative phase, the low pressure belt moves north, towards the equator. In New Zealand, this can cause increased westerly winds, unsettled weather, and storm activity over most of the country. Over the southern oceans, there are relatively less westerly winds and less storm activity. During a positive phase, the low pressure belt moves south towards Antarctica. In New Zealand, this can cause relatively light winds and more settled weather. Over the southern oceans, there is increased westerly winds and storm activity. This dataset relates to the "Southern annular mode" measure on the Environmental Indicators, Te taiao Aotearoa website.

The Tropical Atmosphere Ocean (TAO) array of moored buoys spans the tropical Pacific. Moorings within the array measure surface meteorological and upper-ocean parameters. This collection contains full-resolution, delayed-mode data, which the National Buoy Data Center processed and submitted to NODC in netCDF-formatted files.

m, n = 2, 1 tearing mode onset empirical probability and machine learning analyses of a multiscenario DIII-D database of over 14 000 H- mode discharges show that the normalized plasma beta, the rotation profile, and the magnetic equilibrium shape have the strongest impact on the 2,1 tearing mode stability, in qualitative agreement with neoclassical tearing modes (m and n are the poloidal and toroidal mode numbers, respectively). In addition, 2,1 tearing modes are most likely to destabilize when n > 1 tearing modes are already present in the core plasma. The covariance matrix of tearing sensitive plasma parameters takes a nearly block-diagonal form, with the blocks incorporating thermodynamic, current and safety factor profile, separatrix shape, and plasma flow parameters, respectively. This suggests a number of paths to improved stability at fixed pressure and edge safety factor primarily by preserving a minimum of 1 kHz differential rotation, increasing the minimum safety factor above unity, using upper single null magnetic configuration, and reducing the core impurity radiation. In addition, lower triangularity, lower elongation, and lower pedestal pressure may also help to improve stability. The electron and ion temperature, collisionality, resistivity, internal inductance, and the parallel current gradient appear to only weakly correlate with the 2,1 tearing mode onsets in this database.

The number of people having to commute to work (usual place of work or no fixed workplace address) has risen considerably over the past five years from 13 450 900 in 2001 to 14 714 300 in 2006 or 9.4%. While the car is still the most frequently used mode of transportation for getting to work, there was a decrease in the proportion of drivers in the past five years, from 73.8% of workers in 2001 to 72.3% in 2006. In 2006, 11% of Canadian workers used public transit to get to work, compared to 10.5% in 2001 and 10.1% in 1996.

This represents the Service data reported to the NTD by transit agencies to the NTD.

In versions of the data tables from before 2014, you can find data on service in the file called "Transit Operating Statistics: Service Supplied and Consumed."

If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.

In this table you will find information about the movement behaviour of the Dutch population (excluding mobility of home residents) on Dutch territory.The movement behaviour is described as the number of movements per person per day, the distance per person per day and the duration of travel per person per day, broken down by sex, personal characteristics (e.g. income of the household to which the person belongs, age, educational level), means of transport and regions. These are regular trips and trips, i.e. non-holiday related and non-professional. Miles travelled abroad do not count. This means that cross-border movements at the border have been cut off and therefore the kilometres travelled abroad have not been taken into account. Also holiday-related domestic movements and occupational movements are not included in this table.

Data available from 2010.

Status of the figures: The figures in this table are final.

Changes as of 17 November 2016: None, this table has been discontinued.

When will there be new figures? This table is followed by the table "Personal mobility in the Netherlands; personal characteristics and modes of transport, region”. See paragraph 3.

What’s Included

Introducing the Bahasa Shopping List Image Dataset - a diverse and comprehensive collection of handwritten text images carefully curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the Bahasa language.

Containing more than 2000 images, this Bahasa OCR dataset offers a wide distribution of different types of shopping list images. Within this dataset, you'll discover a variety of handwritten text, including sentences, and individual item name words, quantity, comments, etc on shopping lists. The images in this dataset showcase distinct handwriting styles, fonts, font sizes, and writing variations.

To ensure diversity and robustness in training your OCR model, we allow limited (less than three) unique images in a single handwriting. This ensures we have diverse types of handwriting to train your OCR model on. Stringent measures have been taken to exclude any personally identifiable information (PII) and to ensure that in each image a minimum of 80% of space contains visible Bahasa text.

The images have been captured under varying lighting conditions, including day and night, as well as different capture angles and backgrounds. This diversity helps build a balanced OCR dataset, featuring images in both portrait and landscape modes.

All these shopping lists were written and images were captured by native Bahasa people to ensure text quality, prevent toxic content, and exclude PII text. We utilized the latest iOS and Android mobile devices with cameras above 5MP to maintain image quality. Images in this training dataset are available in both JPEG and HEIC formats.

In addition to the image data, you will receive structured metadata in CSV format. For each image, this metadata includes information on image orientation, country, language, and device details. Each image is correctly named to correspond with the metadata.

This metadata serves as a valuable resource for understanding and characterizing the data, aiding informed decision-making in the development of Bahasa text recognition models.

We are committed to continually expanding this dataset by adding more images with the help of our native Bahasa crowd community.

If you require a customized OCR dataset containing shopping list images tailored to your specific guidelines or device distribution, please don't hesitate to contact us. We have the capability to curate specialized data to meet your unique requirements.

Additionally, we can annotate or label the images with bounding boxes or transcribe the text in the images to align with your project's specific needs using our crowd community.

This image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage this shopping list image OCR dataset to enhance the training and performance of text recognition, text detection, and optical character recognition models for the Bahasa language. Your journey to improved language understanding and processing begins here.

The Highway Capacity Manual (HCM) historically has been among the most important reference guides used by transportation professionals seeking a systematic basis for evaluating the capacity, level of service, and performance measures for elements of the surface transportation system, particularly highways but also other modes. The objective of this project was to determine how data and information on the impacts of differing causes of nonrecurrent congestion (incidents, weather, work zones, special events, etc.) in the context of highway capacity can be incorporated into the performance measure estimation procedures contained in the HCM. The methodologies contained in the HCM for predicting delay, speed, queuing, and other performance measures for alternative highway designs are not currently sensitive to traffic management techniques and other operation/design measures for reducing nonrecurrent congestion. A further objective was to develop methodologies to predict travel time reliability on selected types of facilities and within corridors. This project developed new analytical procedures and prepared chapters about freeway facilities and urban streets for potential incorporation of travel-time reliability into the HCM. The methods are embodied in two computational engines, and a final report documents the research. This zip file contains comma separated value (.csv) files of data to support SHRP 2 report S2-L08-RW-1, Incorporating travel time reliability into the Highway Capacity Manual. 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/3606

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Paleoclimatology Modeling. The data include parameters of instrumental|paleoclimatic modeling with a geographic location of Global. The time period coverage is from 529 to -58 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

The data, collected in 2024, provides a comprehensive snapshot of travel patterns and preferences within the mosque site area in Solo. This data, gathered across five distinct locations within the mosque complex, delves into the motivations and choices of individuals visiting the site.

The dataset encompasses a range of factors influencing travel decisions. It meticulously records travel characteristics, such as the primary purpose of the trip, the distance traveled to reach the mosque, and the duration of the journey. Additionally, it captures the parking fee incurred by visitors, offering insights into the economic considerations associated with travel to the mosque.

Beyond travel details, the dataset also profiles the respondents themselves. It captures demographic information, including gender, age, and occupation, providing a nuanced understanding of the diverse population visiting the mosque. Furthermore, it delves into economic indicators, such as monthly income and vehicle ownership, revealing the socioeconomic factors that influence travel choices.

This rich dataset serves as a valuable resource for understanding travel behavior within the mosque site area. By analyzing the collected data, researchers can gain valuable insights into the factors influencing travel choices, identify potential areas for improvement in accessibility and convenience, and develop strategies to enhance the overall experience for visitors.