Analysis of ‘Transit Ridership’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7e1abbc7-e6bd-4435-bcfd-e67428779e73 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Passenger and fare counts for Bloomington Transit bus routes

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Port Authority Monthly Average Ridership by Route’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/cd88d1d6-84a4-43c3-aefb-54b76f25221d on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains the average weekday, Saturday, and Sunday ridership by route for every month since January 2017. The aggregated ridership data allows quick study of trends over time and identification of problem areas for further analysis.

Bus, light rail (the "T"), and incline ridership data is included.

Routes and bus stops may change at every "pick" or quarterly schedule change in March, June, September, and November.

--- Original source retains full ownership of the source dataset ---

This dataset contains the average weekday, Saturday, and Sunday ridership by route for every month since January 2017. The aggregated ridership data allows quick study of trends over time and identification of problem areas for further analysis. Bus, light rail (the "T"), and incline ridership data is included. Routes and bus stops may change at every "pick" or quarterly schedule change in March, June, September, and November.

This research project examines the link between job access and stop/station level transit ridership. Job access, following recent literature, is measured as the number of jobs that can be reached within a 30-minute transit travel time, including transfers and walk time to access jobs once exiting a transit station. Cumulative opportunity job access measures of this sort – i.e. the number of jobs that can be reached within 30 minutes – have become common in the recent access literature, and those measures have often focused on access via transit. Yet there have been few studies that examine the link between transit job access and transit ridership, and of those none that examine the link at a station or stop level. We use station and stop level ridership data for the Los Angeles Metro bus and rail system and the BART rail system in the San Francisco Bay Area. We calculate transit job access as jobs that can be reached within 30 minutes, using the Remix software tool. Regression analysis of 1,000 randomly selected Los Angeles bus stops reveals a robust relationship between stop-level ridership and job access. The association between transit job access and bus stop ridership (embarkations and disembarkations at the stop) is statistically significant. Converting that association into an elasticity, if the number of jobs accessible within 30-minutes were to increase by 1 percent, on average stop-level ridership would increase between 0.6 to 0.8 percent. The same association, with similar magnitudes, exists for Metro rail stations and BART rail stations, but due the smaller sample sizes, those relationships are not statistically significant when control variables are added to the regression. Our findings show that job access is closely related to ridership at the bus stop level, suggesting transit agencies can increase job access by increasing bus frequency, reducing transfers, siting lines that connect job concentrations to residents, and by improving bus stop/rail station access/egress times.

This ridership is calculated from a variety of sources depending on the route, mode, and month in which the data was collected. These datasets provide a high-level overview of SEPTA’s ridership for basic analysis. They supplement – but do not replace – data provided for official financial or legal reporting requirements.

Routes included for each mode:

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Bus – all bus routes

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CCT – SEPTA’s paratransit service

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Heavy Rail – Broad Street Line, Market Frankford Line, and Norristown High Speed Line

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Regional Rail – All commuter rail lines

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Trackless Trolleys – Routes 59, 66, and 75

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Trolley – Routes 10, 11, 13, 15, 34, 36, 101, and 102

Please note the following about methodology and data sources:

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Ridership numbers are generated using Automatic Passenger Counters (APCs), through revenue derived estimates, or they are estimated using a combination of APC data and overall revenue ridership trends.

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In August 2020, SEPTA suffered a malware attack and APC data was not available for various periods of time depending on the mode:

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Bus mode data was unavailable from August 2020 through December 2020.

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Trolley mode data was unavailable from August 2020 through May 2023.

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Trackless trolley mode data was unavailable from August 2020 and remains unavailable.

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APC data for Regional Rail, Heavy Rail and CCT was never available during this time period.

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In September 2023 the formula to estimate ridership on Heavy Rail mode was adjusted to account for the high rates of fare evasion observed in a study performed in the second quarter of 2023. The spike in ridership between September 2023 and months prior can partially be attributed to this adjustment.· December 2019 ridership numbers were updated in January 2025 to reflect updated methodologies for capturing data when the alternate schedule is in effect between Christmas and New Years. The updated numbers more accurately represent the historic ridership numbers during the month of December.

App-based ridesharing services (RSSs), exemplified by platforms like Uber, play a pivotal role in modern transportation by offering convenient and on-demand services. The exploration of RSSs necessitates a comprehensive consideration of the inherent spatiotemporal variability within the data. Prior research, however, has tended to analyze the spatial and temporal dimensions separately, with many studies omitting the temporal aspect. This study addresses the gap by using geovisualization techniques to illustrate emerging hot spot analysis in New York City in 2022, derived from space–time data mining. Overall, despite temporal variations in overall RSSs ridership, certain taxi zones maintain distinct ridership patterns. Across the five New York City boroughs (Manhattan, Bronx, Queens, Brooklyn, and Staten Island), Midtown Manhattan and the Brooklyn areas adjacent to Queens exhibit saturated intensifying hot spots, signaling a notable increase in RSSs ridership throughout 2022, surrounded by sporadic hot spots. Conversely, peripheral areas of New York City reveal diminishing cold spots, indicating a decrease in their intensity as cold spots. Furthermore, the study conducts separate spatial and temporal profiling. By presenting the spatiotemporal trends of RSSs, this research complements existing literature and provides valuable insights for more informed interventions. The study also highlights certain limitations that could be addressed in future endeavors.

Analysis of ‘CTA - Ridership - Bus Routes - Daily Totals by Route’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/1d3ad9db-6abe-4b80-b447-3b889b450ebc on 28 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset shows total daily ridership on a per-route basis dating back to 2001. Daytypes are as follows: W=Weekday, A=Saturday, U=Sunday/Holiday. See attached readme file for more detailed information.

--- Original source retains full ownership of the source dataset ---

This dataset contains the average weekday, Saturday, and Sunday ridership by route for every month since January 2017. The aggregated ridership data allows quick study of trends over time and identification of problem areas for further analysis.

Bus, light rail (the "T"), and incline ridership data is included.

Routes and bus stops may change at every "pick" or quarterly schedule change in March, June, September, and November.

Total number of bus and LRT origin-to-destination trips, regardless of number of transfers/boardings required to complete the trip. Also known as "linked-trips"

Please note: The data and the method used to estimate ridership changed significantly in 2019. Prior to 2019, ridership was estimated based on an analysis of monthly sales of various fare media (for example, monthly passes, ticket books, electronic fare boxes and cash). In 2019, a new ridership methodology was established which uses data from Automated Passenger Counters on transit vehicles to estimate ridership. Therefore, it is important to note that ridership data prior to 2019 may not be comparable to post-2019 ridership data.

Data Description: This data set contains the daily count of Streetcar ridership throughout the City of Cincinnati.

Data Creation: Streetcar Ridership data is created via an Automated Passenger Counter (APC) system installed in the doorways of each streetcar vehicle. APC devices uses infrared lights to approximate the number of entering and exiting riders.

Data Created By: Southwest Ohio Regional Transit Authority (SORTA)

Refresh Frequency: Daily

CincyInsights: The City of Cincinnati maintains an interactive dashboard portal, CincyInsights in addition to our Open Data in an effort to increase access and usage of city data. This data set has an associated dashboard available here: https://insights.cincinnati-oh.gov/stories/s/n7hm-3f4b

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

The MTA Monthly Ridership / Traffic Data dataset, starting from January 2008, tracks monthly passenger counts and traffic patterns on the MTA transit system. It provides insights into ridership trends, seasonal fluctuations, and transit usage over time.

Case studies for the comparison between AMIs and ridership data.

This dataset measures first point of payment when boarding at the start of a journey using the TTC. Attributes: Name | Description | Comments ---|---|--- 2019 - 1985 | This matrix shows the number of riders recorded under each column from 1985 to 2019. Each figure should have three zeros after each number. | Add three zeros to the end of each number. 112,360 is actually 112,360,000 tokens received from riders. Fare Media | This lists the different forms of fares (payment) accepted by the TTC. | Tokens, tickets, PRESTO, monthly/weekly passes, cash Who | This lists the different types of types of fares payable by different groups of riders | Adult - Tokens, Tickets, PRESTO, Regular Monthly Pass, etc. Children - Tickets, PRESTO, cash Where | This identifies the types of vehicles being used to transport riders | Bus, subway, SRT, streetcar When | This indicates by year the number of riders during a weekday or weekend/holiday |

Details of the common baseline adopted to estimate relative changes in ridership data and the QI.

This data is from a 2019 analysis of transit ridership in the United States. The purpose of the analysis was to codify US transit agencies into comparable groups using publicly available data. The data is split into dedicated right of way modes, and mixed right of way modes.

Provides agency-wide totals for service and cost efficiency metrics for data reported to the National Transit Database in the 2022 and 2023 report years. This view is based off of the "2022 - 2023 NTD Annual Data - Metrics" dataset, which displays the same data at a lower level of aggregation (by mode). This view displays the data at a higher level (by agency).

Only Full Reporters report data on Passenger Miles. The columns containing ratios have been calculated as the average across all reporting modes, not as the ratio of summed data. Thus, each transit agency received equal weight, regardless of that agency's total ridership.

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 view and its parent dataset are based on the 2022 and 2023 Federal Funding Allocation, Operating Expenses, and Service database files.

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

In versions of the NTD data tables from before 2014, you can find data on metrics in the files called "Fare per Passenger and Recovery Ratio" and "Service Supplied and Consumed Ratios."

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

The Bangalore Metro Rail Corporation Limited (BMRCL) publishes daily ridership data every 24 hours. Unfortunately, they do not provide historical data beyond one day. I have been collecting ridership data from the BMRCL website since 26th October, 2024 and will preserve it here for anyone who wishes to analyse this data. As the dataset evolves over time, it will lend itself to deeper analysis of metro traffic, ridership and access patterns.

Aggregated mobility indices (AMIs) derived from information and communications technologies have recently emerged as a new data source for transport planners, with particular value during periods of major disturbances or when other sources of mobility data are scarce. Particularly, indices estimated on the aggregate user concentration in public transport (PT) hubs based on GPS of smartphones, or the number of PT navigation queries in smartphone applications have been used as proxies for the temporal changes in PT aggregate demand levels. Despite the popularity of these indices, it remains largely untested whether they can provide a reasonable characterisation of actual PT ridership changes. This study aims to address this research gap by investigating the reliability of using AMIs for inferring PT ridership changes by offering the first rigorous benchmarking between them and ridership data derived from smart card validations and tickets. For the comparison, we use monthly and daily ridership data from 12 cities worldwide and two AMIs shared globally by Google and Apple during periods of major change in 2020–22. We also explore the complementary role of AMIs on traditional ridership data. The comparative analysis revealed that the index based on human mobility (Google) exhibited a notable alignment with the trends reported by ridership data and performed better than the one based on PT queries (Apple). Our results differ from previous studies by showing that AMIs performed considerably better for similar periods. This finding highlights the huge relevance of dealing with methodological differences in datasets before comparing. Moreover, we demonstrated that AMIs can also complement data from smart card records when ticketing is missing or of doubtful quality. The outcomes of this study are particularly relevant for cities of developing countries, which usually have limited data to analyse their PT ridership, and AMIs may offer an attractive alternative.

These data are related to "Spatial Patterns of Micromobility Ridership: A Multi-City Analysis" paper

Tracks transit ridership, including metrics like passenger counts, modes, time periods, and reporting frequency for performance analysis.