This dataset collection contains data available on SF open data regarding Public Transportation in the city.

Currently, there are two datasets- The first contains the list of routes taken by the SF Muni Public Transit and the second contains the list of SF Muni Public Transit stops.

1.SF Muni Routes, Source: https://data.sfgov.org/Transportation/Muni-Simple-Routes/9exe-acju. Contains Current Muni routes for simple cartographic and spatial analyses as of July 10, 2023

2.SF Muni Stops, Source: https://data.sfgov.org/Transportation/Muni-Stops/i28k-bkz6. Contains Current Muni stops for geospatial analysis as of July 10, 2023

This dataset was created to analyze the growth and development of metro systems across Indian cities and various cities around the globe. By gathering data from various online sources, including Wikipedia, I aimed to explore patterns in metro network expansion, ridership trends, and system characteristics. The process involved extensive data cleaning to remove inconsistencies and ensure clarity, making it ready for exploratory data analysis (EDA) and visualization. The goal was to gain valuable insights into metro systems' performance, growth trajectory, and factors influencing their success, providing a foundation for future urban transportation planning and analysis.

Visualizations of unadjusted and seasonally adjusted transportation data.

Data used in the Bureau of Transportation Statistics' new interactive version of Transportation Economic Trends (TET). TET highlights transportation's role in the economy and explores changes (trends) over time through a series of interactive charts. TET also explains related concepts and data sources for a general audience. Interactive visualizations available at: https://data.transportation.gov/stories/s/28tb-cpjy

The Bureau of Transportation Statistics (BTS), part of the Department of Transportation (DOT) is the preeminent source of statistics on commercial aviation, multimodal freight activity, and transportation economics, and provides context to decision makers and the public for understanding statistics on transportation. BTS assures the credibility of its products and services through rigorous analysis, transparent data quality, and independence from political influence. BTS promotes innovative methods of data collection, analysis, visualization, and dissemination to improve operational efficiency, to examine emerging topics, and to create relevant and timely information products that foster understanding of transportation and its transformational role in society. The Bureau’s National Transportation Library (NTL) is the permanent, publicly accessible home for research publications from throughout the transportation community; the gateway to all DOT data; and the help line for the Congress, researchers, and the public for information about transportation.

This research focuses on a study area in Fort Lauderdale--a two-block stretch of Las Olas Blvd. between Southeast 9th Ave. and Southeast 11th Ave. where researchers expect mean high tides up to 36 inches higher in the year 2100. The project investigates a community planning process in which a combination of high- and low-tech visualization methods—a Geographic Information System (GIS) and a human artist—was used to increase public participation and draw out local knowledge which helps the decision-making process for the future. Mixed reality technologies such as Microsoft Hololens (augmented reality) and Samsung VR Gear (virtual reality) offer immersive educational and engagement experiences, which may convey information in a more meaningful way. Using a quasi-experimental methodology of before-and-after surveys, we compare the degree to which virtual reality technologies improve (or impede) constituents’ absorption of information regarding sea-level rise risks to roadway infrastructure in their communities.

Description: CityTrans is a dataset representing a city's transportation system. It includes various types of data related to the city's transportation infrastructure, such as road networks, public transportation routes, traffic flow, commuter patterns, and environmental factors. The dataset is designed to provide insights into urban mobility, aid in transportation planning, and serve as a testbed for innovative transportation solutions.

Features of the CityTrans dataset:

Road Networks:

• Graph representation of the road network, including nodes (intersections) and edges (road segments).
• Attributes for each road segment, such as length, speed limits, and number of lanes.
• Historical traffic data for different time periods, allowing analysis of traffic congestion patterns.

Public Transportation:

• Bus routes and schedules, including stops, timings, and passenger load data.
• Subway/Metro lines, stations, and their connections.
• Fare data, including ticket prices and types.

Traffic Flow:

• Real-time and historical traffic flow information, including vehicle counts and speeds on different road segments.
• Traffic congestion hotspots and peak hours.
• Random delays introduced to simulate traffic congestion.

Commuter Patterns:

• Origin-destination matrices indicating the number of commuters traveling between different areas of the city.
• Realistic departure and arrival times for different modes of transport, considering peak hours and congestion.
• Random delays added to arrival times based on traffic congestion.

Environmental Factors:

• Random weather data, including temperature, precipitation, and wind speed for different locations and times.
• Random air quality data, reflecting pollution levels at various locations.

The CityTrans dataset aims to provide a comprehensive view of the city's transportation system. It enables researchers, planners, and developers to analyze and visualize the city's transportation patterns, develop predictive models, create interactive visualizations, and explore innovative solutions for urban mobility challenges.

By making this dataset available, it fosters collaboration and allows others to leverage the data for research, testing algorithms, and creating innovative solutions to address transportation-related issues in urban environments.

Long-term Pavement performance, construction, traffic, and environmental data for more than 2500 pavement sections in the United States and Canada. More than a dozen experimental designs address specially constructed and existing asphalt and concrete pavements, and maintenance and rehabilitation strategies. Data collection has been on-going since 1990. About one third of the pavement sections are still under study. New warm-mix asphalt concrete pavement overlay sections are currently being recruited and constructed.

📊 Delhi Metro Ridership & Operational Statistics Dataset

Subtitle

A comprehensive dataset representing ridership, ticket revenue, and operational performance of the Delhi Metro one of the largest urban transit systems in the world.

About the Dataset

The Delhi Metro is a rapid transit system serving the National Capital Region (NCR) of India. It plays a crucial role in reducing traffic congestion and providing sustainable public transportation to millions of passengers every day.

This dataset captures multiple performance indicators of the Delhi Metro network over time, including:

Total metro trips operated Daily total passengers Ticket revenue Average passenger distance traveled per trip Top stations based on passenger demand Total stations operational

These data points help in analyzing metro usage patterns, operational efficiency, and transit demand in the region.

Why This Dataset is Useful

This dataset enables research in:

Urban transport planning Revenue & demand forecasting Passenger travel behavior analysis Transportation infrastructure optimization Dashboard development & data storytelling Academic machine learning projects

Potential Use Cases

• Time-series forecasting of passengers and revenue
• Peak-hour identification using station-based data
• Policy evaluation on fare changes and new lines
• Visualization dashboards (e.g., Plotly/Dash, Power BI, Tableau)

Source

Data has been collected, cleaned, and aggregated using publicly available metro operational insights, news reports, and transit performance summaries released by the Delhi Metro Rail Corporation (DMRC).

File Details

(Adjust fields based on your actual dataset columns — I can refine if you share final structure.)

Licensing

License: CC BY 4.0 (Users must provide attribution when using the dataset)

If you want, I can also add:

Thumbnail Image for Kaggle Dataset Tags & Categories for better discoverability Example Notebooks (Exploration + Forecast models) Dashboard Preview Screenshots

As of Report Year (RY) 2023, FTA requires that reporters with fixed route modes create and maintain a public domain general transit feed specification (GTFS) dataset that reflects their fixed route service. This specification allows for the mapping and other geospatial data visualization and analyses of key transit elements such as stops, routes, and trips. At least one GTFS weblink is provided by the transit agency for each fixed route bus mode and type of service. These include all Rail modes as well as Bus, Bus Rapid Transit, Commuter Bus, Ferryboat and Trolleybus.

GTFS requires that an overarching compressed file contain, at a minimum, seven underlying text files: (a) Agency; (b) Stops; (c) Routes; (d) Trips; (e) Stop Times; (f) Calendar or Calendar Dates.txt; and (g) Feed Info.txt. An eighth file, Shapes.txt, is an optional file. FTA collects and publishes these links for further analysis using related GTFS files. FTA is not responsible for managing the websites that host these files, and users with questions regarding the GTFS data are encouraged to contact the transit agency.

In many cases, publicly hosted weblinks could not be provided (i.e., due to constraints within the transit agency), but the agency was able to produce a zip file of the required GTFS data. Demand Response, Vanpool, and other non-fixed route modes are excluded. The column "Alternate Format" indicates that the agency provided FTA a weblink in an alternate format with some justification for doing so. The file "Waived" indicates that no GTFS files were produced and FTA granted the agency a waiver from the requirement in Report Year 2024.

Note: if the Validated Date is earlier than the Modified Date for a specific row, then the weblink has not yet been reviewed. If you encounter issues with a weblink that has been validated, please contact NTDHelp@dot.gov, as this may indicate an error with the host website.

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

This project is based on the data accumulated by the electric bus data platform. It plans to plan and analyze application indicators and analysis axes according to the demand and the degree of data collection, select the data of 8 routes and 11 charging stations, conduct operational data analysis indicator case studies and applications, visualize and display the analysis results, explore AI applications, and propose operational and policy suggestions based on the analysis results to improve the operation and management of electric buses.

The Hubway trip history data includes every trip taken through Nov 2013 ? with date, time, origin and destination stations, plus the bike number and more. Data from 2011/07 through 2013/11 The Hubway trip history data Every time a Hubway user checks a bike out from a station, the system records basic information about the trip. Those anonymous data points have been exported into the spreadsheet. Please note, all private data including member names have been removed from these files. What can the data tell us? The CSV file contains data for every Hubway trip from the system launch on July 28th, 2011, through the end of September, 2012. The file contains the data points listed below for each trip. We ve also posed some of the questions you could answer with this dataset - we re sure you.ll have lots more of your own. Duration - Duration of trip. What s the average trip duration for annual members vs. casual users? Start date - Includes start date and time. What are the peak Hubway hours?

Introduction

This dataset provides a comprehensive assessment of public transport connectivity across Germany by analyzing both walking distances to the nearest public transport stops as well as the quality of public transport connections for daily usage scenarios with housing-level-granularity on a country-wide scale. The data was generated through a novel approach that integrates multiple open data sources, simulation models, and visual analytics techniques, enabling researchers, policymakers, and urban planners to identify gaps and opportunities for transit network improvements. ewline

Why does it matter?

Efficient and accessible public transportation is a critical component of sustainable urban development. However, many transit networks struggle to adequately serve diverse populations due to infrastructural, financial, and urban planning limitations. Traditional transit planning often relies on aggregated statistics, expert opinions, or limited surveys, making it difficult to assess transport accessibility at an individual household level. This dataset provides a data-driven and reproducible methodology for unbiased country-wide comparisons.

Find more information at https://mobility.dbvis.de.

Key Facts, Download, Citation

Title OPTIMAP: A Dataset for Open Public Transport Infrastructure and Mobility Accessibility Profiles

Acronym OPTIMAP

Download https://mobility.dbvis.de/data-results/OPTIMAP_v2025-02-01.parquet (478MB, parquet)

License Datenlizenz Deutschland - Namensnennung - Version 2.0 (dl-de-by/2.0)

Please cite the dataset as:Maximilian T. Fischer, Daniel Fürst, Yannick Metz, Manuel Schmidt, Julius Rauscher, and Daniel A. Keim. OPTIMAP: A Dataset for Open Public Transport Infrastructure and Mobility Accessibility Profiles. Zenodo, 2025. doi: 10.5281/zenodo.14772646.

or, when using Bibtex

@dataset{MobilityProfiles.DatasetGermany.2025, author = {Fischer, Maximilian T. and Fürst, Daniel and Metz, Yannick and Schmidt, Manuel and Rauscher, Julius and Keim, Daniel A.}, title = {OPTIMAP: A Dataset for Open Public Transport Infrastructure and Mobility Accessibility Profiles}, year = 2025, publisher = {Zenodo}, doi = {10.5281/zenodo.14772646}}

Dataset Description

The dataset in the PARQUET format includes detailed accessibility measures for public transport at a fine-grained, housing-level resolution. It consists of four columns:

lat, lng (float32): GPS coordinates (EPSG:4326) of each house in Germany, expensively compiled from the house coordinates (HK-DE) data provided by the 16 federal states under the EU INSPIRE regulations.

MinDistanceWalking (int32): An approximate walking distance (in meters) to the nearest public transport stop from each registered building in Germany.

scores_OVERALL (float32): A simulated, demographic- and scenario-weighted measure of public transport quality for daily usage, considering travel times, frequency, and coverage across various daily scenarios (e.g., commuting, shopping, medical visits). The results are represented in an artificial time unit to allow comparative analysis across locations.

Methodology

The dataset was generated using a combination of open geospatial data and advanced transport simulation techniques.

Data Sources: Public transit information from the German national access point (DELFI NeTEx), housing geolocation data from various state authorities, and routing information from OpenStreetMap.

Walking Distance Calculation: The shortest path to the nearest transit stop was computed using the Dijkstra algorithm on a graph network of publicly available pathways sourced from OSM, considering the ten aerial-nearest public transport stops.

Public Transport Quality Estimation: The dataset incorporates a scenario-based simulation model, analyzing weight-averaged travel times and connection frequency to typical daily POIs such as the individually nearest train stations, kindergartens, schools, institutions of higher education, fitness centers, cinemas, places of worship, supermarkets, shopping malls, restaurants, doctors, parks, and cultural institutions. It includes walking distances to the start and from the destination public transport stops as well as the averaged travel and waiting times on the shortest route calculated via a modified Dijkstra algorithm. The results are aggregated using a demographically- and scenario-weighted metric to ensure comparability. The value is in the unit of time, although it should not be interpreted directly as real minutes.

Visualization and Validation: A WebGL-based interactive tool and static precomputed maps were developed to allow users to interactively explore transport accessibility metrics dynamically, available at https://mobility.dbvis.de.

Potential Applications

The dataset enables multiple use cases across research, policy, and urban planning:

Public Accessibility Studies: Provides insights into transport equity by evaluating mobility gaps affecting different demographic groups, different regional areas, and comparing county and state efforts in improving public transport quality.

Urban Planning and Transport Policy: Supports data-driven decision-making for optimizing transit networks, adjusting service schedules, or identifying underserved areas.

Smart City Development: Assists in integrating mobility analytics into broader smart city initiatives for efficient resource allocation and sustainability planning.

Academic Research: Facilitates studies in transportation engineering, urban geography, and mobility behavior analysis.

Conclusion

By offering high-resolution public transport accessibility data at housing-level granularity, this dataset contributes to a more transparent and objective understanding of urban mobility challenges. The integration of simulation models, demographic considerations, and scalable analytics provides a novel approach to evaluating and improving public transit systems. Researchers, city officials, and policymakers are encouraged to leverage this dataset to enhance transport infrastructure planning and accessibility.

This dataset contains both the approximate walking distances in meters and a weighted overall quality score in an artificial time unit for each individual house in Germany. More advanced versions are currently not publicly available. This base dataset is publicly available and adheres to open data licensing principles, enabling its reuse for scientific and policy-oriented studies.

Source Data Licenses

While not part of this dataset, the scientific simulation used to create the results leverages public transit information via the National Access Point (NAP) DELFI as NeTEx, provided via GTFS feeds of Germany (CC BY 4.0).

Also, routing information used during the processing was based on Open Street Map contributors (CC BY 4.0).

Primarily, this dataset contains original and slightly processed housing locations (lat, lng) that were made available as part of the EU INSPIRE regulations, based on Directive (EU) 2019/1024 (of the European Parliament and of the Council of 20 June 2019 on open data and the re-use of public sector information (recast)).

In Germany, the respective data is provided individually by the 16 federal states, with the following required attributions and license indications:

BB: EU INSPIRE / © GeoBasis-DE/LGB, dl-de-by/2.0 (data modified)

BE: EU INSPIRE / © Geoportal Berlin / Hauskoordinaten, dl-de-by/2.0 (data modified)

BW: EU INSPIRE / © LGL, www.lgl-bw.de, dl-de-by/2.0 (data modified)

BY: EU INSPIRE / © Bayerische Vermessungsverwaltung, CC BY 4.0 (data modified)

HB: EU INSPIRE / © Landesamt GeoInformation Bremen, CC BY 4.0 (data modified)

HE: EU INSPIRE / © HVBG, dl-de-by-zero/2.0 (data modified)

HH: EU INSPIRE / © FHH (LGV), dl-de-by/2.0 (data modified)

MV: EU INSPIRE / © LAiV M-V, CC BY 4.0 (data modified)

NI: EU INSPIRE / © LGLN 2024, CC BY 4.0 (data modified)

NW: EU INSPIRE / © Geobasis NRW, dl-de-by-zero/2.0 (data modified)

RP: EU INSPIRE / © GeoBasis-DE / LVermGeoRP 2024, dl-de-by/2.0 (data modified)

SH: EU INSPIRE / © GeoBasis-DE/LVermGeo SH, CC BY 4.0 (data modified)

SL: EU INSPIRE / © GeoBasis DE/LVGL-SL (2024), dl-de-by/2.0 (data modified)

SN: EU INSPIRE / © GeoSN, dl-de-by/2.0 (data modified)

ST: EU INSPIRE / © GeoBasis-DE / LVermGeo LSA, dl-de-by/2.0 (data modified)

TH: EU INSPIRE / © GDI-Th, dl-de-by/2.0 (data modified)

Original Research

The methodology and techniques are described in an original research article published in 2024. When referring to our approach, please cite the following publication:Yannick Metz, Dennis Ackermann, Daniel A. Keim, and Maximilian T. Fischer. Interactive Public Transport Infrastructure Analysis through Mobility Profiles: Making the Mobility Transition Transparent. In: 2024 IEEE Visualization in Data Science (VDS). VDS. IEEE, 2024, p. 9. doi: 10.1109/VDS63897.2024.00006

or, when using bibtex:

@inproceedings{MobilityProfiles.VDS.2024, author = {Metz, Yannick and Ackermann, Dennis and Keim, Daniel A. and Fischer, Maximilian T.}, title = {Interactive Public Transport Infrastructure Analysis through Mobility Profiles: Making the Mobility Transition Transparent}, booktitle = {2024 IEEE Visualization in Data Science (VDS)}, doi = {10.1109/VDS63897.2024.00006}, pages = {9}, publisher = {IEEE}, series = {VDS}, year = {2024}}

NOTE: As weblinks are updated continually by transit agencies, some links submitted in Report Year 2023 may no longer work. The most up-to-date weblinks dataset is refreshed on a monthly basis: https://data.transportation.gov/Public-Transit/General-Transit-Feed-Specification-Weblinks/2u7n-ub22/about_data As of Report Year (RY) 2023, FTA requires that reporters with fixed route modes create and maintain a public domain general transit feed specification (GTFS) dataset that reflects their fixed route service. This specification allows for the mapping and other geospatial data visualization and analyses of key transit elements such as stops, routes, and trips. At least one GTFS weblink is provided by the transit agency for each fixed route bus mode and type of service. These include all Rail modes as well as Bus, Bus Rapid Transit, Commuter Bus, Ferryboat and Trolleybus. GTFS requires that an overarching compressed file contain, at a minimum, seven underlying text files: (a) Agency; (b) Stops; (c) Routes; (d) Trips; (e) Stop Times; (f) Calendar or Calendar Dates.txt; and (g) Feed Info.txt. An eighth file, Shapes.txt, is an optional file. FTA collects and publishes these links for further analysis using related GTFS files. FTA is not responsible for managing the websites that host these files, and users with questions regarding the GTFS data are encouraged to contact the transit agency. In many cases, publicly hosted weblinks could not be provided (i.e., due to constraints within the transit agency), but the agency was able to produce a zip file of the required GTFS data. Demand Response, Vanpool, and other non-fixed route modes are excluded. The column "Alternate Format" indicates that the agency provided FTA a weblink in an alternate format with some justification for doing so. The file "Waived" indicates that no GTFS files were produced and FTA granted the agency a waiver from the requirement in Report Year 2023. NTD Data Tables organize and summarize data from the 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2023 General Transit Feed Specification database file. If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.

The Smart Mobility and Traffic Optimization Dataset integrates data from cyber-physical networks (CPNs) and social networks (SNs) to improve traffic management and smart mobility solutions. By combining real-time traffic patterns, vehicle telemetry, ride-sharing demand, public transport efficiency, social media sentiment, and environmental factors, this dataset provides a comprehensive foundation for optimizing urban mobility.

Designed to support machine learning models, the dataset enables accurate predictions of traffic congestion, mobility optimization, and smart city planning. It incorporates key metrics such as vehicle density, road occupancy, weather conditions, social media feedback, and emissions data to generate actionable insights.

Key Features: Traffic Data: Includes vehicle count, speed, road occupancy, and traffic light status, offering a granular view of real-time traffic conditions. Weather & Accidents: Integrates weather conditions and accident reports to assess their impact on congestion levels. Social Network Sentiment: Analyzes public opinions and complaints about mobility and congestion, extracted from social media platforms. Smart Mobility Factors: Examines ride-sharing demand, parking availability, and public transport delays, aiding in urban mobility planning. Environmental Impact: Monitors CO₂ emissions and pollution levels, ensuring eco-friendly traffic optimization. Target Variable: The dataset categorizes traffic congestion levels into three main groups: Low, Medium, or High, based on real-time traffic density, speed, and road occupancy.

This dataset is an essential resource for urban planners, smart city developers, and AI researchers, empowering them to create intelligent mobility solutions that reduce congestion, enhance efficiency, and improve overall urban sustainability.

This dataset summarizes characteristics of 11 land use efficiency visualization tools that address vehicle miles traveled, gentrification, and equity. Summary characteristics include the tools' purpose, year of data or publication, data sources, methods used, units of anlaysis, and evaluation of the tool and ease of use. Links to tools and documentation are included.

A web mapping application for regional freight data sharing, visualization, and analysis for the greater Chattanooga area. For information on data used in this application, please visit the Thrive Regional Infrastructure PortalThe interactive mapping application tools are listed below. Standard Tools: Zoom, Pan, Identify, Turn Layers On and Off, View Legend, Adjust TransparencyQueriesFiltersChartsInfo SummaryMeasureDrawPrintChange BasemapsDaily Traffic SummaryDirectionsShare MapAdd DataFor more information, please contact Shannon Millsaps smillsaps@thriveregion.org or TonyG@gatech.edu