The National Travel Survey 2013/2014 (RVU 2013/2014) is the seventh national survey of travel behaviour conducted in Norway. The first was done in 1985, the next five in 1992, 1998, 2001, 2005 and 2009. The survey provides information on transport resources and travel activity. It covers personal travel of all types, including short trips taken on a daily basis and longer journeys undertaken less frequently, as well as all modes of transport, including walking. Data on the travel activities of the population is important for both national and local transport planning. The results are used as the basis for the development of transport models, estimates, exposure calculations in road safety work, and in a number of research projects. National travel surveys are also important for background comparisons and evaluations of surveys tied to more spesific modes of transportation or geographical regions. The survey consists of three data files: "Personfil", "Reisefil" (yesterday's travels) and "Lange Reiser" (travelling over 100 km or travels to/from abroad in the last month). THis file contains all three.

The metadata set does not comprise any description or summary. The information has not been provided.

The 2014 Southern Nevada Household Travel Survey collected information from residents in the Las Vegas area to update the regional travel demand model and assess travel behavior. The Regional Transportation Commission of Southern Nevada contracted with Westat to conduct the survey. The survey was conducted in two phases—from March to May 2014 and from August to October 2014. Participants provided demographic and travel data (via a travel log). A 10% subsample (1,694 participants) was randomly selected to take part in a wearable global positioning system (GPS) technology-based component of the study, the purpose of which was to assess the level of trip under-reporting in the self-reported travel logs. Participants in the GPS portion of the study were instructed to record trips in their travel logs on the first day only, while passively recording their travel for three full days.

Since the beginning of 2005, the Travel Survey of Residents of Canada (TSRC) has been conducted to measure domestic travel in Canada. It replaces the Canadian Travel Survey (CTS). Featuring several definitional changes and a new questionnaire, this survey provides estimates of domestic travel that are more in line with the international guidelines recommended by the World Tourism Organization (WTO) and the United Nations Statistical Commission. In 2011, TSRC underwent a redesign. Please refer to the document entitled Differences Between the 2011 Redesigned TSRC and the 2010 TSRC available in the "Documentation" section of this survey, for an explanation of the differences between TSRC from 2006-2010 and TSRC in 2011. The Travel Survey of Residents of Canada is sponsored by Statistics Canada, the Canadian Tourism Commission, and the provincial governments. It measures the size of domestic travel in Canada from the demand side. The objectives of the survey are to provide information about the volume of trips and expenditures for Canadian residents by trip origin, destination, duration, type of accommodation used, trip reason, mode of travel, etc.; to provide information on travel incidence and to provide the socio-demographic profile of travellers and non-travellers. Estimates allow quarterly analysis at the national, provincial and tourism region level (with varying degrees of precision) on: total volume of same-day and overnight trips taken by the residents of Canada with destinations in Canada, same-day and overnight visits in Canada, main purpose of the trip/key activities on trip, spending on same-day and overnight trips taken in Canada by Canadian residents in total and by category of expenditure, modes of transportation (main/other) used on the trip, person-visits, household-visits, spending in total and by expense category for each location visited in Canada, person- and household-nights spent in each location visited in Canada, in total and by type of accommodation used, use of travel packages and associated spending and source of payment (household, government, private employer), demographics of adults that took or did not take trips, and travel party composition. The main users of the TSRC data are Statistics Canada, the Canadian Tourism Commission, the provinces, and tourism boards. Other users include the media, businesses, consultants and researchers.

On behalf of the San Francisco Municipal Transportation Agency (SFMTA), Corey, Canapary & Galanis undertook a Travel Decision Survey within the City and County of San Francisco, as well as the eight surrounding Bay Area counties of Alameda, Contra Costa, San Mateo, Marin, Santa Clara, Napa, Sonoma, and Solano. The primary goals of this study were to: 1. Assess percent mode share for travel in San Francisco for evaluation of the SFMTA Strategic Objective 2.3: Mode Share target of 50% non-private auto travel by FY 2018 with a 95% confidence level and margin of error ±5% or less. 2. Evaluate the above statement based on number of trips to, from, and within San Francisco by Bay Area residents. Trips by visitors to the Bay Area and for commercial purposes are not included. 3. Provide additional trip details, including trip purpose for each trip in the mode-share question series. 4. Collect demographic data on the population of Bay Area residents who travel to, from, and within San Francisco. 5. Collect data on travel behavior and opinions that support other SFMTA strategy and project evaluation needs.

BackgroundA modal shift to cycling has the potential to reduce greenhouse gas emissions and provide health co-benefits. Methods, models, and tools are needed to estimate the potential for cycling uptake and communicate to policy makers the range of impacts this would have.Methods and findingsThe Impacts of Cycling Tool (ICT) is an open source model with a web interface for visualising travel patterns and comparing the impacts of different scenarios of cycling uptake. It is currently applied to England. The ICT allows users to visualise individual and trip-level data from the English National Travel Survey (NTS), 2004–2014 sample, 132,000 adults. It models scenarios in which there is an increase in the proportion of the population who cycle regularly, using a distance-based propensity approach to model which trips would be cycled. From this, the model estimates likely impact on travel patterns, health, and greenhouse gas emissions. Estimates of nonoccupational physical activity are generated by fusing the NTS with the English Active People Survey (APS, 2013–2014, 559,515 adults) to create a synthetic population. Under ‘equity’ scenarios, we investigate what would happen if cycling levels increased equally among all age and gender categories, as opposed to in proportion to the profile of current cyclists. Under electric assist bike (pedelecs or ‘e-bike’) scenarios, the probability of cycling longer trips increases, based on the e-bike data from the Netherlands, 2013–2014 Dutch Travel Survey (50,868 adults).Outcomes are presented across domains including transport (trip duration and trips by mode), health (physical activity levels, years of life lost), and car transport–related CO2 emissions. Results can be visualised for the whole population and various subpopulations (region, age, gender, and ethnicity). The tool is available at www.pct.bike/ict. If the proportion of the English population who cycle regularly increased from 4.8% to 25%, then there would be notable reductions in car miles and passenger related CO2 emissions (2.2%) and health benefits (2.1% reduction in years of life lost due to premature mortality). If the new cyclists had access to e-bikes, then mortality reductions would be similar, while the reduction in car miles and CO2 emissions would be larger (2.7%). If take-up of cycling occurred equally by gender and age (under 80 years), then health benefits would be marginally greater (2.2%) but reduction in CO2 slightly smaller (1.8%). The study is limited by the quality and comparability of the input data (including reliance on self-report behaviours). As with all modelling studies, many assumptions are required and potentially important pathways excluded (e.g. injury, air pollution, and noise pollution).ConclusionThis study demonstrates a generalisable approach for using travel survey data to model scenarios of cycling uptake that can be applied to a wide range of settings. The use of individual-level data allows investigation of a wide range of outcomes, and variation across subgroups. Future work should investigate the sensitivity of results to assumptions and omissions, and if this varies across setting.

為估算各縣市之公共運輸使用比率，了解民眾未搭乘公共運輸工具(以下簡稱公共運具)之原因，並探究民眾搭乘各項公共運具之滿意度，交通部統計處爰創辦「民眾日常使用運具狀況調查」。本調查係委託全方位市場調查公司，於103年10月1日至12月31日間，以電話訪問方式，訪查臺灣地區年滿15歲以上民眾。 本次調查有效樣本計3萬8,267人，在95%信心水準下，抽樣誤差為±0.5%(各縣市有效樣本，除連江縣387人，抽樣誤差為±5.0個百分點外，其餘縣市均至少1,384人，抽樣誤差均在±2.7個百分點以內)。

This statistic presents worldwide national attractiveness factors in 2014. The survey was conducted in six countries, including Great Britain. 61 percent of respondents stated cultural and historic attractions were important to them in this respect. These factors were closely followed by countryside and landscapes (60 percent) and people (59 percent). Half of respondents also placed importance on each of the following: cities, arts and a reputation for safety.

Abstract

There is increasing concern in national statistical offices about coverage of informal economic activities. PCBS has given priority to transport activities, due to their importance to the Palestinian economy. The informal transport survey complements the 2014 formal transport sector survey. The PCBS began by exerting tremendous effort to establish a sampling frame. All land transport stops in major Palestinian cities were defined and data about the number and characteristics of operating vehicles were collected in order to stratify the population into homogenous stratum.

The survey covers activities of the informal sector according to (ISIC-4) for both: Non-scheduled passenger land transport (4922) Freight transport by road (4923)

Objectives: Objectives of this survey are the following. 1. Number of transport vehicles and persons engaged by activity. 2. Value of output and intermediate consumption 3. Value added components. 4. Fixed assets. 5. Other selected variables.

Geographic coverage

Palestine

Analysis unit

vehicles

Universe

The survey covers activities of the informal sector according to (ISIC-4) for both: Non-scheduled passenger land transport (4922) Freight transport by road (4923)

Vehicles:divided according to its activity to: ·Taxi passengers. ·Private passengers. ·Freight transport by road.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design: The design used is a random cluster stratified sample: Quota sample proportional to the size of the station. The sample size amounted to (2,683) vehicles of the total (11,060) vehicles that comprise the survey frame. Sample Clusters: Barking divided to clusters on the following levels: 1. Transport kind: Vehicles divided according to its activity to: - Taxi passengers. - Privet passengers. - Freight transport by road.

1. Vehicles model: Vehicles divided according to its model to:
2. Model 2005 below.
3. Model 2006 up

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire of the transport survey- outside sector was designed to take into account major economic variables pertaining to the examined phenomenon and it meets the needs of the Palestinian National Accounts. Which contains the following questions: · questions about vehicle. · Persons engaged and their compensations. · Value of output from main activity. · Intermediate consumption. · Taxes on production. · Fixed assets.

Cleaning operations

Data Entry Training: The data entry training begins before the data entry process, the training is of two parts theoretically and practically.

Data Entry Administrative: The Information System Directorate administrates the whole process with all its requirements. The data entry team is of data entry employees and a supervisor.

Editing of Data Entry: There are tow steps: First: Throughout the data entry itself since the program itself is available to correct mistakes in data entry. Second: Listing of questionnaires, which are, still have mistakes in data entry.

Data Tabulation: Primary tables are exerted after the process of data entry and editing. A process of editing data is being taken to have at the end a final correct data tables.

Response rate

The Response ratio is (95.4%)

Sampling error estimates

Sampling Errors

Data of this survey affected by statistical errors due to use the sample, Therefore, the emergence of certain differences from the real values expect obtained through censuses. It had been calculated variation of the most important indicators exists and the facility with the report. And the dissemination levels of the data were particularized at the regional level in the Palestinian Territories. Non- Sampling Errors Non-statistical errors are probable in all stages of the project, during data collection or processing. This is referred to as non-response errors, response errors, interview in errors, and data entry errors. To avoid errors and reduce their effects, great efforts were made to train the fieldworkers intensively. They were trained in how to carry out the interview, what to discuss and what to avoid. Also data entry staff was trained on the entry program that was examined before starting the data entry process, preparing data entry program before data collection for checking readiness of the program for data entry, a set of validation rules were applied on the program for checking consistency of data, weekly data files were received by project management for checking accuracy and consistency, notes of correction are provided for data entry management for correction. To have a fair idea about the situation and to limit obstacles, there was continuous contact with the fieldwork team through regular visits to the field and regular meetings with them during the different field visits. Problems faced by fieldworkers were discussed to clarify any issues.

The 2014-2015 Puget Sound Regional Travel Study collected information about household and individual travel patterns for residents throughout a four-county region in Washington State. Study results were used to update the region's travel and land-use models and to calibrate local traffic and travel models. The study also helped the Puget Sound Regional Council (PSRC) and its regional partners develop plans that accommodate the diverse travel needs and preferences of residents. The Resource Systems Group administered the study on behalf of PSRC. Global positioning system (GPS)-equipped smartphones were used to provide data pertaining to the daily travel of 547 individual participants. Because the region's university students may have been underrepresented in the initial 2014 household travel study, the PSRC added a college-population travel survey in fall 2014. In spring 2015, a second household data collection effort was conducted to increase the frequency of data collection and to collect GPS data as well as a sample of longitudinal data from households that completed the 2014 survey.

2008-2012 National Travel Mode by Selected Social and Economic Characteristics 2008–2012. Contains estimates, percentages and margins of error. Data is at the country level.

Brian McKenzie, "Modes Less Traveled—Bicycling and Walking to Work in the United States: 2008–2012", U.S. Census Bureau, American Community Survey Reports, Issued May 2014, ACS-25. (Table 3)

Data accessed from: https://www2.census.gov/library/publications/2014/acs/acs-25.pdf

This statistic shows a ranking of worst national characteristics in the United Kingdom (UK) in 2014, based on a survey conducted in five countries and divided by whether or not respondents had been to the United Kingdom. 22 percent of both those who had visited the UK and those who had not thought UK residents were ignorant of other cultures.

2008-2012 National Travel Mode to Work by Selected Commuting Characteristics. Contains estimates, percentages and margins of error. Data is at the country level.

Brian McKenzie, "Modes Less Traveled—Bicycling and Walking to Work in the United States: 2008–2012", U.S. Census Bureau, American Community Survey Reports, Issued May 2014, ACS-25. (Table 4)

Data accessed from: https://www2.census.gov/library/publications/2014/acs/acs-25.pdf

2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the Greater Fairbanks Transportation Survey Final Report. ## Transportation Data For details on available travel survey data and variable definitions, see the data dictionary. NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the drive cycle data dictionary. Transportation data are available as zipped files. Download Winzip.

Abstract

Statistics South Africa collects data on foreign tourism from the South African Department of Home Affairs. Data on domestic tourism is also needed to measure its contribution to the national economy. The Domestic Tourism Survey (DTS) is aimed at addressing this need by collecting data on the travel behaviour and expenditure of South African residents travelling within and outside the borders of South Africa. This survey provides data on domestic tourism activity during the period January to December 2014.

Geographic coverage

The survey had national coverage

Analysis unit

Households and individuals

Universe

The target population of the survey consists of all private households and residents in workers' hostels in the nine provinces of South Africa. The survey does not cover other collective living quarters such as students' hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data

Sampling procedure

For the Domestic Tourism Survey SSA used a household survey master sample of 3 080 primary sampling units from the 80 787 enumeration areas (EAs) created for the 2001 Population Census. The master sample used a two-stage, stratified design with probability-proportional-to-size (PPS) sampling of PSUs from within strata, and systematic sampling of dwelling units (DUs) from the sampled primary sampling units (PSUs). A self-weighting design at provincial level was used and MS stratification was divided into two levels, primary and secondary stratification. Primary stratification was defined by metropolitan and non-metropolitan geographic area type. During secondary stratification, the 2001 data were summarised at PSU level. The following variables were used for secondary stratification; household size, education, occupancy status, gender, industry and income.

Mode of data collection

Face-to-face

Research instrument

Household Questionnaire: This includes sections on: Household characteristcs, household listing, education, tourism employment, trips taken, day trips, overnight trips, barriers to taking trips, business and professional trips, recreation entertainment, sports trips, nature based trips, religious trips, medical trips, type of transport, expenditure on trips, social activites