This table contains 8 series, with data for years 1995 - 2000 (not all combinations necessarily have data for all years), and was last released on 2001-10-04. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada ), Transit type (2 items: Urban transit; Passenger bus), Operating statistics (5 items: Total establishments reporting; Operating revenues; Passengers carried; Operating expenses; Revenue vehicle kilometres).

The 2025-2044 20-Year Needs Assessment outlines capital work the MTA needs to do over the next two decades to keep the region moving. The MTA developed a three-part plan to secure the foundation of the system and ensure another 100 years of service by reconstructing, renewing, and modernizing the system. This dataset includes data on all revenue bus fleets and does not include data on Access-A-Ride paratransit vehicles. For each fleet type (standard, articulated, express), and power source (Zero Emissions, or Any Type, which includes both Zero Emissions and non-Zero Emission buses), there is data for the number of buses, their useful lives, and the percent of buses beyond their useful lives. The dataset also includes the number of buses planned to be ordered in the 2025–2029, 2030–2034, 2035–2039, and 2040–2044 capital programs by type and power source.

This data set shows the Principal statistics of bus services, 1971 - 2015. Footnote Data for 2005 and 2010 refer to a census while for the other years the coverage refers to cut-off where establishments with revenue RM500,000 and above were covered. Coverage for 2010 included city bus, express bus, school bus and employee bus services. No survey were conducted in 1980, 1982, 1993, 1995, 1997, 1999, 2001, 2007, 2011-2014. Number of establishments, value of gross output, value of intermediate input, and value added are only available from the year 2000 onwards. Source: Department of Statistics, Malaysia No. of Views : 58

Canadian passenger bus and urban transit industries, total revenue and total passenger trips by the North American Industry Classification System (NAICS), selected provinces and regions, monthly.

Annual financial performance of the passenger bus and urban transit industries (number of companies; total revenues; total expenses; net income), by North American Industry Classification System (NAICS) (urban transit; interurban and rural bus; school and employee bus; charter bus and sightseeing; other transit-shuttle).

These data sets contain benchmark instances for airport shuttle scheduling problem which is introduced in the paper titled "The Airport Shuttle Bus Scheduling Problem".

In the Airport Shuttle Bus Scheduling Problem, ASBSP, the incoming passengers are transferred from the airport to the city center and the outgoing passengers are transferred from the city center to the airport using a number of identical, capacitated vehicles. The flight schedule and the number of passengers that use the transfer service from each flight are known. Passengers have their associated ready times and due dates, which are associated with their flight times and allowable waiting times. Revenue is earned from each transferred customer. On the other hand, there is a transfer cost associated with empty and loaded transfers of the vehicle. Since this is a private company that aims to maximize profit, it is possible to reject some customer requests (if it will result in low utilization of the vehicle). In order to maximize the utilization of the vehicles, it is also allowed to split or group the transfer requests of passengers from different flights without overriding allowable waiting time limits. The problem is to determine the schedule of the vehicles and the assignment of the passengers to these vehicles in order to maximize the total profit.

You can find the details about the parameters used in the instances in "explanation.txt" file.

If you find any errors, want to comment, have any suggestions or have any additional benchmark instances, you are welcome to share and write an e-mail to nihatoner10@gmail.com or hakan.gultekin@gmail.com or cagrikoc.ck@gmail.com.

Canadian passenger bus and urban transit industries, total revenue and total passenger trips by Urban transit agency, selected Canadian Urban transit agencies, monthly.

Annual financial performance of the passenger bus and urban transit industries (number of companies; total revenues; total expenses; net income), by North American Industry Classification System (NAICS) (urban transit; interurban and rural bus; school and employee bus; charter bus and sightseeing; other transit-shuttle).

DVRPC tracks transit ridership in the region through unlinked passenger trips, with data provided by each of the region's four transit operators—Southeastern Pennsylvania Transportation Authority (SEPTA), New Jersey Transit (NJ Transit), Port Authority Transit Corporation (PATCO), and Pottstown Area Rapid Transit (PART)—to the National Transit Database (NTD). Unlinked passenger trips count each passenger boarding, regardless of fare paid. Thus, a trip with a transfer would count as two boardings. Transit ridership does not include Amtrak, shuttles, or private bus passengers. It also does not include services that receive partial funding from SEPTA, like SCCOOT operated by the Transportation Management Association of Chester County.

One table shows the number of unlinked trips by mode—bus, trolley bus, light rail, heavy rail, commuter or regional rail, and non-scheduled services, which includes paratransit, demand response, and vanpools. All NJ Transit services are classified as being in the NJ Counties Subregion, because they mostly do not cross over to the Pennsylvania side of the region. The other table shows the number of unlinked trips by transit agency. The region's commuter rail services are mostly in the PA Suburban Counties Subregion. SEPTA is the only agency in the region with trolleybus services. SEPTA classified the Norristown High Speed Line (NHSL) as a subway, which travels through the PA Suburban Counties Subregion. SEPTA's subway services travel within the city of Philadelphia. Because of how these subway services cover multiple subregions, subways were simply classified as being in the DVRPC Region. Also, both tables contain: Total unlinked trips, unlinked trips per capita, unlinked trips per vehicle revenue hour (VRH), and unlinked trips per vehicle revenue mile (VRM). Vehicle revenue hours and miles count only when transit vehicles are operating along their scheduled routes. These figures do not account for ‘deadhead’ miles when not in passenger service, such as going to and from the depot on their way to or coming back from their scheduled route. Heavy rail and commuter rail services that operate with multiple passenger vehicles in one train count miles and hours for each vehicle individually. For example, a train with six passenger cars traveling one mile will count as six vehicle revenue miles.

NJ Transit's ridership figures are for the DVRPC region only. These figures are based on service in the Trenton and Philadelphia urbanized areas collected by the NTD. Prior to 2013, NJ Transit ridership, vehicle revenue hours, and vehicle revenue miles data was only provided at the statewide level. To calculate the region’s share of these NJ Transit figures, statewide totals from 1997 to 2012 were multiplied by the region’s percentage based on the 2013 to 2022 averages in each category. These averages were 12% of the state's total ridership, 14% of the vehicle revenue miles, and 12% of the vehicle revenue hours. PART ridership data is only available from 2005 onwards. PART vehicle revenue hour and mile data is only available from 2011 onwards. SEPTA trolley bus ridership data is not available from 2004 to 2007.

This data set shows Revenue, Expenditure, Total Number of Persons Engaged, Salaries & Wages Paid of Bus Services

The Monthly module includes a limited set of key indicators reported by transit properties. Data is reported on a monthly basis, by mode and type of service, for a fiscal year.

The four data items included are:

1. Unlinked Passenger Trips
2. Vehicle Revenue Miles
3. Vehicle Revenue Hours
4. Vehicles Operated in Maximum Service (Peak Vehicles)

This dataset presents these values in their own column in a long format (each row of the file is an individual Agency/Mode/TOS/Date). The data source is shared with the static Excel file hosted on the FTA website here: https://www.transit.dot.gov/ntd/data-product/monthly-module-adjusted-data-release. This dataset differs from the static Excel file in its formatting as well as being updated weekly, to capture data as it is reported and validated for a given publication month.

Mode Codes: Alaska Railroad (AR) Cable Car (CC) Commuter Rail (CR) Heavy Rail (HR) Hybrid Rail (YR) Inclined Plane (IP) Light Rail (LR) Monorail/Automated Guideway (MG) Streetcar Rail (SR) Aerial Tramway (TR) Commuter Bus (CB) Bus (MB) Bus Rapid Transit (RB) Demand Response (DR) Ferryboat (FB) Jitney (JT) Público (PB) Trolleybus (TB) Vanpool (VP)

Mode and Type of Service Changes and Impacts on this Time Series:

"Monthly data are reported by mode and type of service. From 2002 through 2011, there were 16 modes in the NTD. NTD monthly ridership data is now reported according to refined modal classifications. Service previously reported as bus (MB) now may be reported as either MB, Commuter Bus (CB), or Bus Rapid Transit (RB). Additionally, service previously categorized as Light Rail (LR) now may be reported as LR or Streetcar (SR).

Similarly, Types of Service were refined in Report Year 2019. From 2002 - 2018, there were two types of service: Directly Operated (DO) and Purchased Transportation (PT). As of 2019, Purchased Transportation is now classified such that agencies report the purchased transportation based on the type of contractor: general third party (PT), taxicab operator (TX), or transportation network company (TN). FTA concurrently removed the ""Demand Response Taxi"" (DT/PT) mode in 2019. FTA now considers all such service as Demand Response (DR) with Taxi (TX) type of service and this time series has been updated to reflect this change. "

For more information on this dataset, please consult the full Read Me in the attached file.

This data set provides a rate of trips taken by individuals sampled in obtaining data for the ACT Household Travel Survey. The data is categorised by age, gender and income. A trip is defined as the travel between two main activities, where a stop may constitute a change in transport mode. As an example: driving from home to a park and ride facility, then catching a bus to an interchange, then walking to a shop to purchase an item and finally walking to work is comprised of 4 ‘stops’ and two …Show full descriptionThis data set provides a rate of trips taken by individuals sampled in obtaining data for the ACT Household Travel Survey. The data is categorised by age, gender and income. A trip is defined as the travel between two main activities, where a stop may constitute a change in transport mode. As an example: driving from home to a park and ride facility, then catching a bus to an interchange, then walking to a shop to purchase an item and finally walking to work is comprised of 4 ‘stops’ and two ‘trips’. Note: This data represents travel and activity on an average weekday. Total trip rate includes ‘other/not stated’ gender respondents.

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.

Canadian passenger bus and urban transit industries, total revenue and total passenger trips by the North American Industry Classification System (NAICS), selected provinces and regions, monthly.

Vital Signs: Transit Cost-Effectiveness by Mode (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.

This is an access to information request for: "1) The total number of permanent billboards located on city owned property and the monthly revenue generated by those leases. I would like that information to be disclosed in two different categories. The number of permanent billboards on city property. In addition and in a separate category the number of permanent billboards located on City of Regina setback and the monthly revenue they generate for the city. 2) I am also requesting a copy of the winning tender for advertising in relation to the bus benches and bus shelters located on city property."

Canadian passenger bus and urban transit industries, total revenue and total passenger trips by Urban transit agency, selected Canadian Urban transit agencies, monthly.

In 2005, the Canadian passenger bus and urban transit industries generated total revenues of about $8.6 billion, fueled by strong growth in government operating and capital funding. This represented a 12.2% increase over the $7.7 billion recorded for 2004.

On behalf of the Press and Information Office of the Federal Government, the opinion research institute forsa conducted a short survey in September 2023 on the attitudes of the German population towards the increase in citizen´s benefit, their consumer behavior and their financial situation (ability to save, debts, additional payments for electricity and heating costs, salary increases, inflation compensation premium). In the survey period 11.09.2023 to 13.09.2023, a total of 1506 German-speaking people aged 14 and over were surveyed in telephone interviews (CATI). Respondents were selected using a multi-stage random sample as part of the forsa multi-topic survey (Politik-BUS), including landline and mobile phone numbers (dual-frame sample). Assessment of the increase in citizen´s benefit; consumer behavior and shopping behavior restricted; areas in everyday life with financial restrictions (energy consumption (heating and electricity), clothing and shoes, eating out, food, vacations, fuel (petrol, diesel), none of the above); financial situation: ability to save: household income set aside in the last month (grouped); if no income could be set aside: debts incurred or savings drawn on to cover household costs; already received an electricity or heating bill this year; reimbursements or additional payments for electricity and heating costs; dependent employees were also asked: job security; inflation adjustment bonus from employer in the last 12 months; salary increase from employer in the last 12 months. Demography: sex; age (grouped), education; income level (net equivalent income) low, medium, high; location size; party preference in the next federal election; voting behavior in the last federal election; household size. Additionally coded were: region West/East; weighting factor. Im Auftrag des Presse- und Informationsamts der Bundesregierung hat das Meinungsforschungsinstitut forsa im September 2023 eine Kurzumfrage durchgeführt, die sich mit den Einstellungen der deutschen Bevölkerung zur Bürgergelderhöhung, ihrem Konsumverhalten sowie ihrer finanziellen Lage (Möglichkeit zum Sparen, Schulden, Nachzahlungen bei Strom- und Heizkosten, Gehaltserhöhungen, Inflationsausgleichsprämie) beschäftigt. Im Erhebungszeitraum 11.09.2023 bis 13.09.2023 wurden insgesamt 1506 deutschsprachige Personen ab 14 Jahren in telefonischen Interviews (CATI) befragt. Die Auswahl der Befragten erfolgte durch eine mehrstufige Zufallsstichprobe im Rahmen der forsa-Mehrthemenumfrage (Politik-BUS) unter Einschluss von Festnetz- und Mobilfunknummern (Dual-Frame-Stichprobe). Beurteilung der Erhöhung des Bürgergeldes; Konsumverhalten und Einkaufsverhalten eingeschränkt; Bereiche im Alltag mit finanzieller Einschränkung (Energieverbrauch (Heizung und Strom), Kleidung und Schuhe, Essengehen, Lebensmittel, Urlaub, Kraftstoff (Benzin, Diesel), nichts davon); finanzielle Lage: Möglichkeit zum Sparen: zurückgelegte Haushaltseinkünfte im letzten Monat (gruppiert); falls keine Einkünfte zurückgelegt werden konnten: Schulden gemacht oder auf Ersparnisse zurückgegriffen um Haushaltskosten zu decken; in diesem Jahr bereits eine Strom- bzw. Heizkostenabrechnung erhalten; Kostenerstattungen oder Nachzahlungen bei Strom- und Heizkosten; abhängig Beschäftigte wurden zusätzlich gefragt: Sicherheit des Arbeitsplatzes; Inflationsausgleichsprämie vom Arbeitgeber in den letzten 12 Monaten; Gehaltserhöhung vom Arbeitgeber in den letzten 12 Monaten. Demographie: Geschlecht; Alter (gruppiert), Bildung; Einkommenslage (Nettoäquivalenzeinkommen) niedrig, mittel, hoch; Ortsgröße; Parteipräferenz bei der nächsten Bundestagswahl; Wahlverhalten bei der letzten Bundestagswahl; Haushaltsgröße. Zusätzlich verkodet wurde: Region West/Ost; Gewichtungsfaktor.