Supplementary material for the paper "Evaluating Costs and Operations of Public Bus Fleet Electrification". The following data is included: 01) Detailed statistics on the transport operators included in the processed GTFS data from DELFI and the Mobility Database 02) Data on routes, trips, itineraries, stops and possible deadhead trips for the selected transport operators 03) Vehicle schedules and crew schedules, as well as charging plans for all scenarios analyzed 04) Costs, energy consumption and other key figures for the scenarios analyzedThe .zip file includes documentation and explanations for all the data.

VITAL SIGNS INDICATOR Transit Cost-Effectiveness (T13)

FULL MEASURE NAME Net cost per transit boarding (cost per boarding minus fare per boarding)

LAST UPDATED May 2017

DESCRIPTION Transit cost-effectiveness refers to both the total and net costs per transit boarding, both of which are adjusted to reflect inflation over time. Net costs reflect total operating costs minus farebox revenue (i.e. operating costs that are not directly funded by system users). The dataset includes metropolitan area, regional, mode, and system tables for net cost per boarding, total cost per boarding, and farebox recovery ratio.

DATA SOURCE Federal Transit Administration: National Transit Database http://www.ntdprogram.gov/ntdprogram/data.htm

Bureau of Labor Statistics: Consumer Price Index http://www.bls.gov/data/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Simple modes were aggregated to combine the various bus modes (e.g. rapid bus, express bus, local bus) into a single mode to avoid incorrect conclusions resulting from mode recoding over the lifespan of NTD. For other metro areas, operators were identified by developing a list of all urbanized areas within a current MSA boundary and then using that UZA list to flag relevant operators; this means that all operators (both large and small) were included in the metro comparison data. Financial data was inflation-adjusted to match 2015 dollar values using metro-specific Consumer Price Indices.

LOW TRANSPORTATION COST INDEXSummaryThe Low Transportation Cost Index is based on estimates of transportation expenses for a family that meets the following description: a 3-person single-parent family with income at 50% of the median income for renters for the region (i.e. CBSA). The estimates come from the Location Affordability Index (LAI). The data correspond to those for household type 6 (hh_type6_) as noted in the LAI data dictionary. More specifically, among this household type, we model transportation costs as a percent of income for renters (t_rent). Neighborhoods are defined as census tracts. The LAI data do not contain transportation cost information for Puerto Rico.InterpretationValues are inverted and percentile ranked nationally, with values ranging from 0 to 100. The higher the transportation cost index, the lower the cost of transportation in that neighborhood. Transportation costs may be low for a range of reasons, including greater access to public transportation and the density of homes, services, and jobs in the neighborhood and surrounding community.

Data Source: Location Affordability Index (LAI) data, 2012-2016.Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 11.

References: www.locationaffordability.infohttps://lai.locationaffordability.info//lai_data_dictionary.pdf

To learn more about the Low Transportation Cost Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

This dataset details data on hours worked by public transportation employees, head counts of employees and wage rates for each applicable agency reporting to the National Transit Database in the 2022 and 2023 report years at the agency level.

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 dataset is based on the 2022 and 2023 Transit Agency Employees database files.

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

Please note: FTA updated this file November 15, 2023 to improve the calculation of Average Wage Rate. Previously, this total included Capital Labor Hours in the denominator. These hours are now omitted from the calculation given that the cost of capital projects do not appear in the numerator.

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

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 Fleet Mobility Services Department is responsible for providing safe and reliable mobile solutions to ensure the continuity of city services. In addition, Fleet’s strategy is to lead, design and incorporate "Sharing, Electric Vehicles, Telematics, and Autonomous Mobility Services" for City employees by providing cost-effective and accessible forms of modality to transport City employees. The primary goals are to reduce transportation costs, traffic congestion and under-utilized fleet assets while improving the health, environment, safety and livability of Austin. The cost per mile of City-owned fleet is below the industry average of $1.19. The data is maintained in Fleet's asset management system. Row level data displays the cost in dollars per mile of City-owned fleet. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/i7kr-sc6e

Russia Avg Consumer Price: Passenger Transportation: Automobile: Long Distance Bus Fare data was reported at 119.200 RUB/50 km in Jan 2019. This records an increase from the previous number of 117.630 RUB/50 km for Dec 2018. Russia Avg Consumer Price: Passenger Transportation: Automobile: Long Distance Bus Fare data is updated monthly, averaging 49.060 RUB/50 km from Jan 1995 (Median) to Jan 2019, with 289 observations. The data reached an all-time high of 119.200 RUB/50 km in Jan 2019 and a record low of 9.780 RUB/50 km in Jan 1997. Russia Avg Consumer Price: Passenger Transportation: Automobile: Long Distance Bus Fare data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PA018: Average Consumer Price: Passenger Transportation.

This dataset details service and cost efficiency metrics for agencies reporting to the National Transit Database in the 2022 and 2023 report years.

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 dataset is 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.

Utilizing National Transit Database data this calculation measures the cost of each passenger trip on MTA modes of transit. This is calculated by dividing the number of total fiscal year annual trips by each modes respective operating costs.

The City of Chicago has published trip-level data for every TNC trip since November 1, 2018. To the best of our knowledge, this dataset is the only one that includes trip fare variables. As we wrote this paper in Oct 2022, the dataset includes approximately 263 million trip records (rows) and 21 features (columns) for trips dated from November 1, 2018, through October 1, 2022. The features of this data include Trip ID, Trip Start Timestamp (rounded to the nearest 15 minutes), Trip End Timestamp (rounded to the nearest 15 minutes), Trip Seconds, Trip Miles, Pickup Census Tract, Dropoff Census Tract, Pickup Community Area, Drop Off Community Area, Trip Fare, Tip, Additional Charges, Total Trip Fare, Shared Trip Authorized, Trips Pooled, Pickup Centroid Latitude, Pickup Centroid Longitude, Pickup Centroid Location, Dropoff Centroid Latitude, Dropoff Centroid Longitude, Dropoff Centroid Location. As the dataset is too large to be processed without a supercomputer, we generated a random sample of 2 million trips from Nov 2018 to June 2022 with valid pickup and drop-down area information. To explore the data, we processed the features to extract date information from the timestamp. We created new variables, including each trip's average fare per mile (excluding tips and additional charges, mainly taxes). In dataset (1), the sampled TNC trips data was processed and summarized to include the average daily fare per mile (USD/mile), and exogenous variables that impact the price were added to the data including holidays (Christmas, thanksgiving, Independence Day, easter and new year) and other variables including gas prices, and climate (snow, precipitation, and average daily temperature). The City of Chicago also publishes taxi trips from 2013 to the present. To protect privacy but allow for aggregate analyses, the Taxi ID is consistent for any given taxi medallion number but does not show the number, and times are rounded to the nearest 15 minutes. Due to the data reporting process, not all but most trips are reported. Taxicabs in Chicago, Illinois, are operated by private companies and licensed by the city. About seven thousand licensed cabs are operating within the city limits. As the dataset is too large to be processed without a supercomputer, we generated a random sample of 2 million trips from Nov 2018 to June 2022 with valid pickup and drop-down area information. To explore the data, we processed the features to extract date information from the timestamp. We created new variables, including each trip's average fare per mile (excluding tips and additional charges, mainly taxes). In dataset (2), the taxi trips data was processed and summarized to include the average daily fare per mile (USD/mile), and exogenous variables that impact the price were added to the data including holidays (Christmas, thanksgiving, Independence Day, easter and new year) and other variables including gas prices, and climate (snow, precipitation, and average daily temperature).

Utilizing National Transit Database data this calculation measures the cost of each revenue vehicle mile for each MTA mode of transit. This is calculated by dividing the number of total fiscal year revenue vehicle miles for each mode by it's respective FY operating costs.

Vital Signs: Transit Cost-Effectiveness by Operator (2022) DRAFT

VITAL SIGNS INDICATOR Transit Cost-Effectiveness (T13)

FULL MEASURE NAME Net cost per transit boarding (cost per boarding minus fare per boarding)

LAST UPDATED June 2022

DESCRIPTION Transit cost-effectiveness refers to both the total and net costs per transit boarding, both of which are adjusted to reflect inflation over time. Net costs reflect total operating costs minus farebox revenue (i.e. operating costs that are not directly funded by system users). The dataset includes metropolitan area, regional, mode, and system tables for net cost per boarding, total cost per boarding, and farebox recovery ratio.

DATA SOURCE Federal Transit Administration: National Transit Database http://www.ntdprogram.gov/ntdprogram/data.htm

Bureau of Labor Statistics: Consumer Price Index http://www.bls.gov/data/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Simple modes were aggregated to combine the various bus modes (e.g. rapid bus, express bus, local bus) into a single mode to avoid incorrect conclusions resulting from mode recoding over the lifespan of NTD. For other metro areas, operators were identified by developing a list of all urbanized areas within a current MSA boundary and then using that UZA list to flag relevant operators; this means that all operators (both large and small) were included in the metro comparison data. Financial data was inflation-adjusted to match 2015 dollar values using metro-specific Consumer Price Indices.

Russia Avg Consumer Price: Passenger Transportation: Automobile: Urban: Bus Fare data was reported at 24.780 RUB/Trip in Jan 2019. This records an increase from the previous number of 23.710 RUB/Trip for Dec 2018. Russia Avg Consumer Price: Passenger Transportation: Automobile: Urban: Bus Fare data is updated monthly, averaging 23.180 RUB/Trip from Jan 2018 (Median) to Jan 2019, with 13 observations. The data reached an all-time high of 24.780 RUB/Trip in Jan 2019 and a record low of 22.730 RUB/Trip in Feb 2018. Russia Avg Consumer Price: Passenger Transportation: Automobile: Urban: Bus Fare data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PA018: Average Consumer Price: Passenger Transportation.

Context

Taxicabs in Chicago, Illinois, are operated by private companies and licensed by the city. There are about seven thousand licensed cabs operating within the city limits. Licenses are obtained through the purchase or lease of a taxi medallion which is then affixed to the top right hood of the car. Source: https://en.wikipedia.org/wiki/Taxicabs_of_the_United_States#Chicago

Content

This dataset includes taxi trips from 2013 to the present, reported to the City of Chicago in its role as a regulatory agency. To protect privacy but allow for aggregate analyses, the Taxi ID is consistent for any given taxi medallion number but does not show the number, Census Tracts are suppressed in some cases, and times are rounded to the nearest 15 minutes. Due to the data reporting process, not all trips are reported but the City believes that most are. See http://digital.cityofchicago.org/index.php/chicago-taxi-data-released for more information about this dataset and how it was created.

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:chicago_taxi_trips

https://cloud.google.com/bigquery/public-data/chicago-taxi

Dataset Source: City of Chicago

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source —https://data.cityofchicago.org — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Ferdinand Stohr from Unplash.

Inspiration

What are the maximum, minimum and average fares for rides lasting 10 minutes or more? Which drop-off areas have the highest average tip? How does trip duration affect fare rates for trips lasting less than 90 minutes?

https://cloud.google.com/bigquery/images/chicago-taxi-fares-by-duration.png" alt=""> https://cloud.google.com/bigquery/images/chicago-taxi-fares-by-duration.png

The Consumer Expenditure Estimates dataset was created by SimplyAnalytics using small area estimation techniques. The Consumer Expenditure (CE) Public Use Microdata (PUMD) samples thousands of respondents (referred to as consumer units, or "CUs") across Texas. Each CU is assigned a weight that reflects the relative proportion of all American CUs that they represent. To estimate expenditures at the Census block group and ZCTA5 levels, we use data from the American Community Survey 5-Year Estimates as a proxy for how CUs are distributed over small areas, and use this information to derive expenditure estimates for all CE spending categories. Due to limitations on the PUMD sample size, and to account for national-level weighting of all CUs, the estimates are further adjusted to account for regional fluctuations in cost of living.

This dataset contains GPS tracking data and performance metrics for motorcycle taxis (boda bodas) in Nairobi, Kenya, comparing traditional internal combustion engine (ICE) motorcycles with electric motorcycles. The study was conducted in two phases:Baseline Phase: 118 ICE motorcycles tracked over 14 days (2023-11-13 to 2023-11-26)Transition Phase: 108 ICE motorcycles (control) and 9 electric motorcycles (treatment) tracked over 12 days (2023-12-10 to 2023-12-21)The dataset is organised into two main categories:Trip Data: Individual trip-level records containing timing, distance, duration, location, and speed metricsDaily Data: Daily aggregated summaries containing usage metrics, economic data, and energy consumptionThis dataset enables comparative analysis of electric vs. ICE motorcycle performance, economic modelling of transportation costs, environmental impact assessment, urban mobility pattern analysis, and energy efficiency studies in emerging markets.Institutions:EED AdvisoryClean Air TaskforceStellenbosch UniversitySteps to reproduce:Raw Data CollectionGPS tracking devices installed on motorcycles, collecting location data at 10-second intervalsRider-reported information on revenue, maintenance costs, and fuel/electricity usageProcessing StepsGPS data cleaning: Filtered invalid coordinates, removed duplicates, interpolated missing pointsTrip identification: Defined by >1 minute stationary periods or ignition cyclesTrip metrics calculation: Distance, duration, idle time, average/max speedsDaily data aggregation: Summed by user_id and date with self-reported economic dataValidation: Cross-checked with rider logs and known routesAnonymisation: Removed start and end coordinates for first and last trips of each day to protect rider privacy and home locationsTechnical InformationGeographic coverage: Nairobi, KenyaTime period: November-December 2023Time zone: UTC+3 (East Africa Time)Currency: Kenyan Shillings (KES)Data format: CSV filesSoftware used: Python 3.8 (pandas, numpy, geopy)Notes: Some location data points are intentionally missing to protect rider privacy. Self-reported economic and energy consumption data has some missing values where riders did not report.CategoriesMotorcycle, Transportation in Africa, Electric Vehicles

VITAL SIGNS INDICATOR Transit Cost-Effectiveness (T13)

FULL MEASURE NAME Net cost per transit boarding (cost per boarding minus fare per boarding)

LAST UPDATED May 2017

DESCRIPTION Transit cost-effectiveness refers to both the total and net costs per transit boarding, both of which are adjusted to reflect inflation over time. Net costs reflect total operating costs minus farebox revenue (i.e. operating costs that are not directly funded by system users). The dataset includes metropolitan area, regional, mode, and system tables for net cost per boarding, total cost per boarding, and farebox recovery ratio.

DATA SOURCE Federal Transit Administration: National Transit Database http://www.ntdprogram.gov/ntdprogram/data.htm

Bureau of Labor Statistics: Consumer Price Index http://www.bls.gov/data/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Simple modes were aggregated to combine the various bus modes (e.g. rapid bus, express bus, local bus) into a single mode to avoid incorrect conclusions resulting from mode recoding over the lifespan of NTD. For other metro areas, operators were identified by developing a list of all urbanized areas within a current MSA boundary and then using that UZA list to flag relevant operators; this means that all operators (both large and small) were included in the metro comparison data. Financial data was inflation-adjusted to match 2015 dollar values using metro-specific Consumer Price Indices.

The Fleet Mobility Services Department is responsible for providing safe and reliable mobile solutions to ensure the continuity of city services. In addition, Fleet’s strategy is to lead, design and incorporate "Sharing, Electric Vehicles, Telematics, and Autonomous Mobility Services" for City employees by providing cost-effective and accessible forms of modality to transport City employees. The primary goals are to reduce transportation costs, traffic congestion and under-utilized fleet assets while improving the health, environment, safety and livability of Austin. The cost per mile of City-owned fleet is below the industry average of $1.19.