These surveys were conducted to collect data on travel origins and destinations, trip purposes, and travel characteristics of New York City Transit, Metro-North Railroad, and Long Island Rail Road customers with the aim of upgrading the MTA's travel forecasting tools and gaining a better understanding of how people travel. --LIRR origin-destination survey (2012-14) --Metro-North origin-destination survey (2007) --Metro-North origin-destination survey (2017) --MTA New York City travel survey (2008) --MTA New York City travel survey (2018)

The travel dataset provides detailed information on various trips taken by travelers, including their destination, travel dates, duration of the trip in days, traveler demographics (name, age, gender, and nationality), as well as the type and cost of accommodation and transportation. This dataset can be used to gain insights into travel patterns, preferences, and behaviors of different types of travelers. It can also be helpful for travel-related businesses, such as travel agencies, to create tailored marketing strategies and travel packages that meet the needs and preferences of different travelers. Column details: • Trip ID: A unique identifier for each trip taken by a traveler. • Destination: The name of the city or country visited by the traveler. • Start date: The date the traveler started the trip. • End date: The date the traveler ended the trip. • Duration (days): The number of days the traveler spent on the trip. • Traveler name: The name of the traveler. • Traveler age: The age of the traveler at the time of the trip. • Traveler gender: The gender of the traveler. • Traveler nationality: The nationality of the traveler. • Accommodation type: The type of accommodation the traveler stayed in, such as hotel, hostel, or Airbnb. • Accommodation cost: The cost of the accommodation for the entire trip. • Transportation type: The mode of transportation used by the traveler, such as plane, train, or car. • Transportation cost: The cost of transportation for the entire trip.

Dataset

This dataset was created by James McGowen

Released under CC0: Public Domain

Contents

It contains the following files:

Context

Monthly data on train occupancy over a period of years.

Content

The data ranges over the years from 1999 to 2011. The Time column has the dates in a particular format. The total occupancy column contains the percentage of occupancy of the trains in that particular month.

Inspiration

Predict the occupancy for the future and help the department of railways to better manage the trains.

Dataset

This dataset was created by Leo Lu

Released under MIT

Contents

The walkshed here uses a ½ mile walk from high-frequency Metrorail station entrances, which represent the distance most people are willing to travel to reach a Metrorail station. For this analysis, high-frequency is defined as an average of one train every 5 minutes or faster.

This line feature class represents the routes of the Metro Rail system within the City of Miami. This dataset was obtained from the Miami-Dade County Open Data Hub and clipped to contain only those features within the City of Miami. For a countywide layer, please refer to Miami-Dade County Open Data Hub at https://gis-mdc.opendata.arcgis.com/.The Transportation dataset enhances transportation equity and access by mapping transit routes, hubs, and service reliability across Miami. It supports inclusive mobility for children, older adults, and people with disabilities, while offering low-cost or free travel options and on-demand services prioritizing senior centers, government buildings, and medical facilities. Data Refresh Frequency: This dataset is refreshed on a weekly basis, regardless of whether any updates have occurred in the source data. Users should note that the data is reprocessed and reloaded each week to ensure availability and consistency, even in the absence of changes.

Dataset

This dataset was created by Eduardo Chades

Released under Other (specified in description)

Contents

It contains the following files:

Abstract copyright UK Data Service and data collection copyright owner.The Opinions and Lifestyle Survey (OPN) is an omnibus survey that collects data from respondents in Great Britain. Information is gathered on a range of subjects, commissioned both internally by the Office for National Statistics (ONS) and by external clients (other government departments, charities, non-profit organisations and academia).One individual respondent, aged 16 or over, is selected from each sampled private household to answer questions. Data are gathered on the respondent, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules. Each regular OPN survey consists of two elements. Core questions, covering demographic information, are asked together with non-core questions that vary depending on the module(s) fielded.The OPN collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living. The OPN has expanded to include questions on other topics of national importance, such as health and the cost of living.For more information about the survey and its methodology, see the gov.uk OPN Quality and Methodology Information (QMI) webpage.Changes over timeUp to March 2018, the OPN was conducted as a face-to-face survey. From April 2018 to November 2019, the OPN changed to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for module customers.In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held under Secure Access conditions in SN 8635, ONS Opinions and Lifestyle Survey, Covid-19 Module, 2020-2022: Secure Access. (See below for information on other Secure Access OPN modules.)From August 2021, as coronavirus (COVID-19) restrictions were lifted across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remained sustainable. Secure Access OPN modulesBesides SN 8635 (the COVID-19 Module), other Secure Access OPN data includes sensitive modules run at various points from 1997-2019, including Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See the individual studies for further details and information on how to apply to use them. Main Topics: The non-core questions for this month were: Train satisfaction (Module MAK): this module was asked on behalf of the Department for Transport. The questions aim to gather information on how frequently people travel by train and their opinions on short distance and long distance train services. Multi-stage stratified random sample Face-to-face interview

Dataset

This dataset was created by kekzzz

Contents

Abstract copyright UK Data Service and data collection copyright owner.The Opinions and Lifestyle Survey (OPN) is an omnibus survey that collects data on a range of subjects commissioned by both the ONS internally and external clients (limited to other government departments, charities, non-profit organisations and academia). Data are collected from one individual aged 16 or over, selected from each sampled private household. Personal data include data on the individual, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules. The questionnaire collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living, on individuals and households in Great Britain. From April 2018 to November 2019, the design of the OPN changed from face-to-face to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers. In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held under Secure Access conditions under SN 8635, ONS Opinions and Lifestyle Survey, Covid-19 Module, 2020-2022: Secure Access. From August 2021, as coronavirus (COVID-19) restrictions were lifting across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remains sustainable. The OPN has since expanded to include questions on other topics of national importance, such as health and the cost of living. For more information about the survey and its methodology, see the ONS OPN Quality and Methodology Information webpage. Secure Access Opinions and Lifestyle Survey data Other Secure Access OPN data cover modules run at various points from 1997-2019, on Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See Opinions and Lifestyle Survey: Secure Access for details. Main Topics:Each month's questionnaire consists of two elements: core questions, covering demographic information, are asked each month together with non-core questions that vary from month to month. The non-core questions for this month were:Train satisfaction (Module MAK): this module was asked on behalf of the Department for Transport. The questions aim to gather information on how frequently people travel by train and their opinions on short distance and long distance train services. Multi-stage stratified random sample Face-to-face interview

Train patronage dataset provides Opal Train Trips by month, operator, contract area and card type. An Opal trip is where an Opal card is used to tap-on and tap-off, including where a single tap-on or tap-off is recorded. All other travel is not included.

Peak Train Load Survey 2016 provides estimates of train loads during the AM and PM peak periods - derived from a survey held on Tuesdays, Wednesdays and Thursdays from 1 March 2016 to 17 March 2016. Since the introduction of Opal, this has been replaced by Peak Train Load Estimates from 2017 using the Rail Opal Assignment Model (ROAM).

Peak Train Estimates use data extracted from the Rail Opal Assignment Model (ROAM). This model assigns Opal journeys to services based on the rail daily working timetable and train punctuality data. The customer load is aggregated to train services and assigned to rail lines.

Train station entries and exits dataset is based on the average of three day sample, representing 'a typical day' of customer entries and exits at each train station.

Context

This GPS trajectory dataset was collected in (Microsoft Research) Geolife project by 178 users in a period of over four years (from April 2007 to October 2011). A GPS trajectory of this dataset is represented by a sequence of time-stamped points, each of which contains the information of latitude, longitude and altitude. This dataset contains 17,621 trajectories with a total distance of 1,251,654 kilometers and a total duration of 48,203 hours. These trajectories were recorded by different GPS loggers and GPS-phones, and have a variety of sampling rates. 91 percent of the trajectories are logged in a dense representation, e.g. every 1~5 seconds or every 5~10 meters per point.

Content

This dataset recoded a broad range of users’ outdoor movements, including not only life routines like go home and go to work but also some entertainments and sports activities, such as shopping, sightseeing, dining, hiking, and cycling.

Data Format - Trajectory file Every single folder of this dataset stores a user’s GPS log files, which were converted to PLT format. Each PLT file contains a single trajectory and is named by its starting time. To avoid potential confusion of time zone, we use GMT in the date/time property of each point, which is different from our previous release. - PLT format: Line 1…6 are useless in this dataset, and can be ignored. Points are described in following lines, one for each line. Field 1: Latitude in decimal degrees. Field 2: Longitude in decimal degrees. Field 3: All set to 0 for this dataset. Field 4: Altitude in feet (-777 if not valid). Field 5: Date - number of days (with fractional part) that have passed since 12/30/1899. Field 6: Date as a string. Field 7: Time as a string. Note that field 5 and field 6&7 represent the same date/time in this dataset. You may use either of them. Example: 39.906631,116.385564,0,492,40097.5864583333,2009-10-11,14:04:30 39.906554,116.385625,0,492,40097.5865162037,2009-10-11,14:04:35 - Transportation mode labels Possible transportation modes are: walk, bike, bus, car, subway, train, airplane, boat, run and motorcycle. Again, we have converted the date/time of all labels to GMT, even though most of them were created in China. Example: Start Time End TimeTransportation Mode 2008/04/02 11:24:21 2008/04/02 11:50:45 bus 2008/04/03 01:07:03 2008/04/03 11:31:55 train 2008/04/03 11:32:24 2008/04/03 11:46:14 walk 2008/04/03 11:47:14 2008/04/03 11:55:07 car

First, you can regard the label of both taxi and car as driving although we set them with different labels for future usage. Second, a user could label the transportation mode of a light rail as train while others may use subway as the label. Actually, no trajectory can be recorded in an underground subway system since a GPS logger cannot receive any signal there. In Beijing, the light rails and subway systems are seamlessly connected, e.g., line 13 (a light rail) is connected with line 10 and line 2, which are subway systems. Sometimes, a line (like line 5) is comprised of partial subways and partial light rails. So, users may have a variety of understanding in their transportation modes. You can differentiate the real train trajectories (connecting two cities) from the light rail trajectory (generating in a city) according to their distances. Or, just treat them the same.

More: User Guide: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/User20Guide-1.2.pdf

Citation

Please cite the following papers when using this GPS dataset. [1] Yu Zheng, Lizhu Zhang, Xing Xie, Wei-Ying Ma. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of International conference on World Wild Web (WWW 2009), Madrid Spain. ACM Press: 791-800.

[2] Yu Zheng, Quannan Li, Yukun Chen, Xing Xie, Wei-Ying Ma. Understanding Mobility Based on GPS Data. In Proceedings of ACM conference on Ubiquitous Computing (UbiComp 2008), Seoul, Korea. ACM Press: 312-321. [3] Yu Zheng, Xing Xie, Wei-Ying Ma, GeoLife: A Collaborative Social Networking Service among User, location and trajectory. Invited paper, in IEEE Data Engineering Bulletin. 33, 2, 2010, pp. 32-40.

Inspiration

This trajectory dataset can be used in many research fields, such as mobility pattern mining, user activity recognition, location-based social networks, location privacy, and location recommendation.