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)

Origin Destination Survey 2017 - Household Data
This dataset falls under the category Raw Mobility Data.
It contains the following data: Refers to the variables that characterise travel in the Aburra Valley obtained through the Origin Destination Survey carried out by the Metropolitan Area of the Aburra Valley in 2017.
This dataset was scouted on 2022/01/24 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://datosabiertos.metropol.gov.co/dataset/encuesta-origen-destino-2017-datos-por-viajes

This data provides core information on truck travel and commodity flows on the provincial highway network and other significant truck corridors. It provides average daily trip activity, commodity weight, and value. The origin and destination data is gathered by counties in Ontario and states outside of Ontario. Data is summarized by 32 commodity groups.

This dataset provides an estimate of subway travel patterns based on scaled-up OMNY and MetroCard return swipe data. It provides estimated passenger volumes for all populated origin-destination (O-D) pairs aggregated by month, day of the week, and hour of day. It also provides the name, ID, and approximate latitude and longitude of the origin and destination subway complexes.

The table Origin-Destination is part of the dataset LEHD Origin-Destination Employment Statistics (LODES) -- New York, available at https://redivis.com/datasets/rw0s-5nc9rxp6k. It contains 460120831 rows across 13 variables.

This statistic gives a ranking of the leading origin-destination (O-D) air passenger markets in 2019, by number of passengers. During that time, the U.S. domestic market was the world's largest single O-D air travel market, with 614 million passengers,

Translink Public Transport Origin-Destination Trips

In addition to this data, the Translink PT Performance Dashboard provides visual and interactive performance data on patronage, on-time-running, fines and warnings, passenger injuries, and customer experience metrics.

Data pre-2022 is stored in a separate dataset here

The Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) provides data on the geographic employment patterns by employment location and residential location. Variables tracked include age of worker, earning, industry, sex, race, ethnicity, educational attainment. Forty-nine states, the District of Columbia, Puerto Rico and the U.S. Virgin Islands are active participants, although there are currently no public-use statistics for Puerto Rico or the U.S. Virgin Islands. Data files are state-based and organized into three types: Origin-Destination (OD), Residence Area Characteristics (RAC), and Workplace Area Characteristics (WAC) at census block geographic detail. Data is available for most states for the years 2002 onwards.

This information was provided by the Municipality of Milan for the x-thons of MobiDataLab.

Origin-destination matrix that shows the relationships between the places of origin and destination of the trips made by the population using public transport on the island of Tenerife. It is a structured collection of data that records all the information of a person's journey by public transport, from the initial stop to the final, including transfers. The data in this matrix comes from the public transport service provided by: Tenerife Interurban Transport - TITSA, Metropolitano de Tenerife - MTSA and La Esperanza Transport - TLE.

The table Workplace Area Characteristics is part of the dataset LEHD Origin-Destination Employment Statistics (LODES) -- New York, available at https://redivis.com/datasets/rw0s-5nc9rxp6k. It contains 51973750 rows across 53 variables.

It includes all heavy rail and light rail rapid transit lines. These travel times are calculated from the departure time at the origin stop to the arrival time at the destination stop. Due to track circuit or other data issues, data is not guaranteed to be complete for any origin-destination pair or date.Data Dictionary:NameDescriptionData TypeExampleservice_dateDate for which travel times should be returned.Date43830route_idGTFS-compatible route for which travel times should be returned.StringOrangedirection_idGTFS-compatible direction for which travel times should be returned.Integer0from_stop_idGTFS-compatible stop representing the origin stop in a pair.String70205to_stop_idGTFS-compatible stop representing the destination stop in a pair.String70154start_time_secProperty of “Travel Times”. Expressed in "seconds after midnight." The time associated with the departure event of the vehicle from the origin stop of the pair.Integer45763end_time_secProperty of “Travel Times”. Expressed in "seconds after midnight." The time associated with the arrival event of the vehicle to the destination stop of the pair.Integer46411travel_time_secProperty of “Travel Times”. Difference between start_time_sec and end_time_sec. The actual travel time between the origin stop and the destination stop, in seconds.Integer648MassDOT/MBTA shall not be held liable for any errors in this data. This includes errors of omission, commission, errors concerning the content of the data, and relative and positional accuracy of the data. This data cannot be construed to be a legal document. Primary sources from which this data was compiled must be consulted for verification of information contained in this data.

CITYDATA.ai crowdsources and curates mobile app location data across +1500 cities worldwide to simulate the presence and movement of people. Data Specs:

Horizontal Accuracy

Range: 2-25 m Average: 10 meters

Location Query Granularity:

Minimum area: 25 m Maximum area: No limit

Monthly Active Users (MAU): 1.1 Billion MAUs globally Average signal density per device per month: 41.3 Data capturing frequency: Event based (for significant events like significant change in location or speed based on app configuration) Data transmission frequency: Daily, Weekly Demographics data availability: Generated from goverment census data (available upon request)

LEHD Origin-Destination Employment Statistics (LODES) used by OnTheMap are available for download below. Version 7 of LODES was enumerated by 2010 census blocks. Previous versions of LODES were enumerated with 2000 census blocks. Data files are state-based and organized into three types: Origin-Destination (OD), Residence Area Characteristics (RAC), and Workplace Area Characteristics (WAC), all at census block geographic detail. Data is available for most states for the years 2002–2013. To browse the LODES data files in their directory structure or to access them with a FTP program (must be able to access HTTP), go to http://lehd.ces.census.gov/data/lodes/.

Check out the data dictionary at http://celebratingcities.github.io/docs.html

Valley Metro conducted an on-board transit survey during spring 2015. This project gathered updated travel behavior data from transit users that encompassed all rail and bus fixed-route services in the Phoenix metropolitan planning area. The study’s goal was to compile statistically accurate information about transit riders and how they use the transit system.

Regional matrix Origin Estimated destination of movements by origin, destination, time slot, reason and predominant mode of movement (2030).

The matrix refers to an average working day and considers 1525 areas (of which 1450 are inland areas of the region). Travel data shall be distinguished by origin, destination, time slot, reason and predominant mode of travel. The OD regional matrix includes 8 modes (driver car, passenger car, rubber TPL, iron TPL, motorcycle, bike, feet and more) and 5 motifs (work, study, occasional, business, homecoming).

Rail industry origin and destination of transported commodities, including provinces and territories, regions, US and Mexico, annual.

The table Residence Area Characteristics is part of the dataset LEHD Origin-Destination Employment Statistics (LODES) -- New York, available at https://redivis.com/datasets/rw0s-5nc9rxp6k. It contains 140311326 rows across 43 variables.

The Canadian Freight Analysis Framework integrates data from several sources to create a comprehensive picture of freight flows across the country by geography, commodity and mode of transport. The framework database estimates tonnage, value, and tonne-kilometres by origin and destination, by commodity type, and by mode.

This dataset estimates human mobility through origin destination (OD) movement flow among the Statistical Area 2 (SA2) regions in Queensland (QLD), connected by public transport (PT) networks. The SA2 regions of Queensland connected by buses, trains, trams and ferries have been used to evaluate OD movement flows. The passenger OD movement data among different stations (or the station-based OD flow) are first estimated using a statistical estimation methodology. The stations-based OD flow data are then translated into region-based OD matrices using the state-of-art method. For more information please see the original metadata file here. Human mobility data is a key ingredient in various areas and domains of research including epidemiology, policy and administration, criminology, transportation, logistics and supply chains, environmental management and, pollution and contamination. High quality human mobility data provided by telecommunication companies collected from call data records (CDRs) is available at prohibitive cost with restrictive licensing, keeping it out of reach for the majority of research community. On the other hand, there is an abundance of high-quality public data, reporting different aspects of mobility. Examples are the public transport patronage and information about the usage of the Australian road network. These datasets are collected by different organisations and government departments and are presented in various formats. For instance, data may be collected at different spatial (e.g. at state or postcode levels) and temporal scales and be presented in the form of passenger counts or aggregated movement flows. This dataset addresses the general lack of national scale comprehensive human mobility dataset in Australia by transforming available mobility data into a consistent format that is suitable for analysis in a broad range of research areas. Merging the various individual datasets into Australia's first comprehensive, national-scale human mobility data asset drastically improves the quality and coverage of existing datasets. The Mobility Australia project received investment (https://doi.org/10.47486/DP702) from the Australian Research Data Commons (ARDC). The ARDC is funded by the National Collaborative Research Infrastructure Strategy (NCRIS). The original data tables were structured in a matrix-like format. AURIN employed a methodology to merge diverse datasets into a comprehensive one, categorising based on transportation types (e.g., trains, buses, rails, ferries), years (e.g., 2019, 2020, 2021, etc.), and temporal scales (e.g., weekly, monthly, yearly). Subsequently, AURIN spatially enabled the original data by employing the 2021 edition of the Australian Statistical Geography Standard (ASGS). The flow between origin and destination pairs is visually represented using line geometry.