Daily vehicle miles traveled (VMT) is a distance- and volume-based measure of driving on roadways for all motorized vehicle types—car, bus, motorcycle, and truck—on an average day. Per capita VMT is the same measure divided by the same area's population for the same year. Per vehicle VMT divides VMT by the number of household vehicles available by residents of that geography in the same year. These three value types can be selected in the dropdown in the first chart below. Use the legend items to explore various geographies. The second chart below shows per capita and total personal vehicles available to the region’s households from the American Community Survey.

Normalizing VMT by a county or region's population, or household vehicles, is helpful for context, but does not have complete parity with what is measured in VMT estimates. People and vehicles come into the region from other places, just as people and vehicles leave the region to visit other places. VMT per capita compares all miles traveled on the region's roads to the region's population (for all ages) from the U.S. Census Bureau's latest population estimates. Vehicle counts for VMT are classified by vehicle types, but not by vehicle ownership. In 2017, statewide estimates for VMT by motorcycles, passenger cars, and two-axle single-unit trucks with four wheels made up 88% of Pennsylvania's VMT, and 95% of New Jersey's. These vehicle types are highly likely to be personal vehicles, owned by households, but a small percent could be fleet vehicles of companies or governments. The remaining VMT is made up of vehicle types like school and commercial buses and trucks with more than two axles so they are highly likely to be commercial vehicles.

The volume of vehicle miles traveled by local buses has been on a downward trend across Great Britain in the past two decades, dropping from around 1.6 billion miles in 2005 to 1.2 billion miles in 2024. While all regions of Great Britain have experienced this downward trend, it was most pronounced in Wales, where bus miles declined by 38 percent during this period. London, meanwhile, experienced the smallest contraction in bus services, with total bus miles only decreasing by 3.4 percent.

This driver represents the total miles driven by all motor vehicles over the calendar year. This includes cars, trucks, motorcycles and buses, but not trains or planes. Data is sourced from the Bureau of Transportation Statistics (BTS).

This statistic depicts the annual local commercial bus mileage in Great Britain (excluding London) from the financial year 2004/05 to 2017/18. Commercial buses travelled a total of 992 million miles in Great Britain (excluding London) in 2017/18.

Kilometers travelled by buses registered in Germany dropped substantially at the beginning of the COVID-19 pandemic from 4.6 billion kilometers in 2019 to 2.9 billion kilometers in 2020 and 2021. This was a drop of about 37 percent.

This table contains figures on traffic performance (vehicle-kilometres) of passenger cars, delivery vans, lorries, semi-trailers, special purpose vehicles and buses.
Vehicle-kilometres of Dutch vehicles have been broken down by Dutch vehicles on Dutch territory and Dutch vehicles on foreign territory.
In addition, there are figures on the total distance covered on Dutch territory. A distinction is made between kilometres covered by Dutch vehicles and kilometres by foreign vehicles.
From 2016 up the kilometres driven by busses are no longer devided in kilometres driven on Dutch or on foreign roads. The vehicle population used to estimate the kilometres is based on the vehicle fleet statistics. The population of the figures in this table is based on the old selection method of the vehicle fleet. The difference between the old and the new selection method is described in a methodological report, see paragraph 4. The data series of vehicle kilometres estimated for the old population ends with 2020. The data series based on the new population is available starting from 2018. The way in which the vehicle kilometres are estimated has not changed, only the population.

For the 2020 data a correction factor was implemented to correct for the ‘smoothing effect’ caused by the method. The smoothing effect smoothes out yearly variation in the data and this results in a distorted picture of periods of time when mobility patterns suddenly change drastically, like happened in 2020 due to COVID-19.

Data available from: 1990 to 2020

Status of the figures: The figures in this table up to and including 2018 are definitive. Figures over 2019 and 2020 have a provisional status.

Changes as of 10 November 2022: None, this table has been discontinued. This table is followed by the table Traffic performance motor vehicles; kilometres, type of vehicle, territory, see paragraph 3.

When will new figures be published? No longer applicable.

This file contains data on consumption of fuel and energy used by the MBTA to operate revenue vehicles. Organized by mode, type of service (TOS), and fuel type. Data is reported by the MBTA to the National Transit Database (NTD) annually. The published data from NTD for the most recent year can be found here. For more information on NTD definitions, visit the glossary. Data is not guaranteed to be complete for any mode or fuel type.Data Dictionary:

Name
Description
Data Type
Example


Year
Year in which data was reported for.
Integer
2016


Mode
NTD-defined public transport mode. Either commuter rail (CR), demand response (DR), ferry boat (FB), heavy rail (HR), light rail (LR), motor bus (MB), bus rapid transit (RB), or trolley bus (TB).
String
HR


Type_of_Service
NTD-defined source of public transport. Either purchased transportation (PT) or directly operated (DO).
String
DO


Fuel_Source
Type of fuel used to operate revenue vehicles.
String
Diesel


Units
Measuring units for fuel source. Electricity is measured in kilowatt hours (kwh), all other sources are measured in gallons or gallon equivalents (gal).
String
kwh


Volume
Total annual units of fuel used.
Integer
2047027


Miles_Traveled
Total annual vehicle miles traveled.
Integer
12928651


Efficiency
Annual fuel efficiency, measured in either miles per gallon or miles per kilowatt hour. Note: While in most cases, efficiency is equal to the miles traveled divided by the total volume, NTD performs aggregations on certain fuel types that creates exceptions. Efficiency should only be used in cases where all rows have data.
Double
6.3158

MassDOT/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.

This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

This statistic depicts the annual mileage of buses supported by local authorities in Wales from the financial year 2004/05 to 2017/18. Local authority buses travelled a total of 21 million miles in Wales in 2012/13 after which that number slowly declined over the next few years.

Safety issues in school zone areas have been one of the most important topics in the traffic safety field. This research project assesses the safety effects of different roadway countermeasures in school zone areas. Although many studies have evaluated the effectiveness of various traffic control devices (e.g., sign, flashing beacon, speed monitoring display), there is a lack of studies exploring different roadway countermeasures that might have significant impacts on the school zone safety. In this research project, the most crash-prone school zone was identified in Orange and Seminole Counties, Florida, based on crash rate, which is defined as crash per thousand daily vehicle miles traveled. The results showed that Westridge Middle and Sadler Elementary schools were the top two crash-prone school zones. Afterward, a microsimulation network was built in VISSIM to test different roadway countermeasures in the school zones. Before applying different countermeasures, the network was calibrated and validated by traffic volume and travel time in order to replicate the real field. Three different countermeasures—two-step speed reduction, decreasing the number of driveways, and converting the two-way left-turn lane (TWLTL) to a raised median—were implemented in microsimulation and compared with the field condition. For each countermeasure, we also ran different sub-scenarios. In two-step speed reduction, we analyzed three sub-scenarios that were defined by the maximum speed limit on the main roadway. The number of driveways was reduced by 25%, 50%, 75%, and 100%, so four sub-scenarios were used to analyze in this countermeasure. We replaced TWLTL with a raised median, so all the left-turning vehicles made left turns either at the intersection or median. Therefore, two sub-scenarios, intersection U-turn and median U-turn, were analyzed. Surrogate safety measures are widely used as indicators to evaluate crash risk in the microsimulation software as it cannot directly measure the traffic crashes. In this research project, three surrogate safety measures were used; two of them were developed from time-to-collision (TTC) notations. Three surrogate safety measures—time-exposed time to collision (TET), time-integrated time to collision (TIT), and (3) time-exposed rear-end crash risk index (TERCRI) —were utilized in this research project as indicators for safety evaluation. The higher value of surrogate safety measures indicates higher crash risk. The results showed that all the sub-scenarios in two-step speed reduction and decreasing driveway access reduced TET, TIT, and TERCRI values significantly compared to the base condition. Moreover, the combination of two-step speed reduction and decreasing driveway access countermeasures outperformed their individual effects as well as the base condition. The one-way ANOVA analysis showed that all the sub-scenarios were significantly different from each other. Sensitivity analysis was also conducted to capture the impact of different sub-scenarios for different values of TTC threshold. The results show that all the sub-scenarios in two-step speed reduction and decreasing the number of driveway access reduced TET, TIT, and TERCRI values significantly for different values of TTC threshold, which ranged from 1 to 3 s. Conversely, for converting the TWLTL to the raised median, the crash risk was higher than the base condition because the value of TET, TIT, and TERCRI was much higher than the base condition. Therefore, the results of this research project provide useful insights for transportation and safety planners.

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.

These PennDOT-maintained Park and Ride facilities offer a safe, convenient location for commuters to leave their automobiles and travel to their destinations in carpools, vanpools or buses.Ride sharing reduces the total number of vehicle miles of travel and improves air quality. It reduces road deterioration, saves fuel, reduces congestion and limits our carbon footprint. It’s also a great way to meet new friends and enjoy your commute.A park and ride facility can offer a transit provider convenient access to many patrons. These locations may also expand the area public transit service covers as the result of a larger customer base and they reduce transit agency operating expense by eliminating the need for the buses to circulate through residential neighborhoods.For more information on this layer, you can use the Data Dictionary available in both web and spreadsheet format. Updates to this layer are made as needed. Any errors or omissions should be reported to the PennDOT Geographic Information Division (RA-PDPENNDOT_GIS@pa.gov).

Snapshot img

Autonomous Bus Market Size 2025-2029

The autonomous bus market size is forecast to increase by USD 2.88 billion at a CAGR of 22.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing launch of new autonomous buses and the development of autonomous vehicle corridors. The number of autonomous buses in operation is on the rise, indicating a strong market demand. This trend is further fueled by the establishment of dedicated hybrid and autonomous vehicle corridors, which provide a safe and regulated environment for the deployment of these vehicles. However, the market also faces challenges, primarily in the form of cybersecurity threats. The input data highlights that autonomous buses are vulnerable to cyberattacks, which could compromise the safety and reliability of these vehicles.
This issue poses a significant challenge for market players, requiring them to invest in robust cybersecurity measures to protect their systems and maintain consumer trust. Companies seeking to capitalize on the opportunities presented by the market must address these challenges effectively to ensure the safe and efficient deployment of their vehicles.

What will be the Size of the Autonomous Bus Market during the forecast period?

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The market continues to evolve, driven by advancements in technology and shifting consumer preferences. Autonomous fleets are revolutionizing mass transit, with hybrid buses and electric vehicles leading the charge towards emission reduction and fuel efficiency. Route optimization and adaptive cruise control enable seamless passenger experience, while machine learning and sensor fusion enhance path planning and vehicle health monitoring. Autonomous driving is a key focus, with virtual testing and cloud computing facilitating the development of software-defined vehicles. Autonomous shuttle services and shared mobility solutions are gaining traction in urban mobility, addressing the last-mile delivery challenge. Regulatory frameworks are evolving to accommodate these innovations, with human-machine interface and passenger safety at the forefront.
On-demand transportation and predictive maintenance are essential components of fleet management, ensuring optimal performance and passenger experience. Autonomous navigation and obstacle detection are critical safety standards, with emergency braking and vehicle-to-vehicle communication further enhancing safety. Public acceptance is a crucial factor, with passenger information systems and vehicle-to-infrastructure communication playing a role in building trust. Continuous testing and validation, deep learning, and artificial intelligence are driving improvements in performance and reliability. The market's dynamics are ever-changing, with ongoing deployment strategies and evolving patterns shaping the future of autonomous buses.

How is this Autonomous Bus Industry segmented?

The autonomous bus industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Semi-autonomous
  Fully-autonomous


Propulsion

  Diesel
  Electric
  Hybrid


End-user

  Public transportation
  Private shuttle services
  Logistics and goods transport


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Type Insights

The semi-autonomous segment is estimated to witness significant growth during the forecast period.

The market encompasses the development and implementation of buses with advanced technologies, including lane keeping, user experience, hybrid buses, last-mile delivery, fuel efficiency, route optimization, passenger safety, mass transit, path planning, software-defined vehicles, adaptive cruise control, camera systems, shared mobility, emission reduction, urban mobility, edge computing, over-the-air updates, vehicle health monitoring, electric buses, corporate transportation, autonomous shuttle, sensor fusion, fleet management, school transportation, vehicle-to-vehicle communication, machine learning, deployment strategies, passenger capacity, autonomous driving, virtual testing, cloud computing, automated guided vehicle, public transportation, regulatory framework, human-machine interface, passenger experience, on-demand transportation, safety standards, testing and validation, deep learning, public acceptance, passenger information system, predictive maintenance, artificial intelligence, emergency braking, obstacle detection, shuttle services, vehicle-to-infrastructure communication, and data analytics.

The semi-autonomous segment, which includes buses with driving automation at Level 3 or below, as defined by t

This table contains figures on traffic performance (vehicle-kilometres) of buses divided by age of the vehicle and type of bus. The table contains figures on the total distance travelled on Dutch territory, the total number of kilometres travelled by Dutch buses and the average annual mileage of Dutch buses.

As of reporting year 2016, the bus-kilometres will no longer be broken down by home and abroad. The vehicle population for which kilometres are estimated is based on statistics on the motor vehicle fleet. The population of the figures in this table is based on the old method of selection of the motor vehicle fleet. The difference between the old and the new selection method is described in a method report, see paragraph 4. The range of kilometres estimated on the basis of the old vehicle population runs until the reporting year 2020. The series based on the new population is available as of reporting year 2018. The way in which the mileage is estimated has not changed, only the population.

The figures for the reporting year 2020 have been corrected for the smoothing effect of the method by means of a correction factor. This smoothing effect flattens the annual variation in the figures. This gives a distorted picture of periods in which mobility suddenly changes drastically, such as in 2020 as a result of the coronavirus crisis.

Data available from: 1990 to 2020.

Status of the figures: The figures up to 2018 are final and the 2019 and 2020 figures have provisional status.

Changes as of 10 November 2022: None, this table has been discontinued. This table is followed up by the bus traffic performance table; miles, age class, territory. See paragraph 3.

When are new figures coming? No longer applicable.

The USA electric school bus market is experiencing robust growth, driven by stringent emission regulations, increasing environmental concerns, and government incentives promoting the adoption of zero-emission vehicles. The market, valued at approximately $2.1 billion in 2025 (assuming a proportional share of the global market size based on the USA's significant school bus fleet), is projected to expand significantly over the forecast period (2025-2033). A Compound Annual Growth Rate (CAGR) of 4.74% (as provided) suggests a substantial market expansion, driven by factors such as decreasing battery costs, technological advancements leading to improved battery performance and range, and the increasing availability of charging infrastructure. Furthermore, school districts are actively seeking to reduce their carbon footprint and improve air quality around schools, creating a strong demand for electric school buses. Key players like Green Power Motor Company, Navistar Inc (IC Bus), and Lion Electric Company are actively shaping the market with their innovative models and expanding production capacities. The market segmentation by powertrain type (IC Engine, Hybrid, Electric) and design type (Type A, B, C, D) reflects the diversity of vehicle types catering to various school transportation needs. The market's growth is, however, subject to factors such as the high initial cost of electric school buses compared to diesel counterparts, and the need for continued investment in charging infrastructure to support widespread adoption. The continued growth hinges on overcoming challenges related to infrastructure development. Sufficient charging infrastructure is crucial for widespread adoption, requiring significant investment from both the public and private sectors. Government policies and funding play a pivotal role in accelerating the transition. While the initial investment costs remain a hurdle, ongoing technological advancements are reducing battery prices and increasing the lifespan of electric school bus batteries, eventually making the total cost of ownership competitive with traditional diesel buses. The long-term outlook for the USA electric school bus market remains positive, indicating a significant shift towards sustainable transportation solutions in the education sector. Ongoing technological improvements and supportive policies are expected to propel market growth and contribute to a cleaner and more environmentally friendly future for school transportation. This comprehensive report provides a detailed analysis of the burgeoning USA electric school bus market, offering invaluable insights for stakeholders across the industry value chain. Utilizing data from the historical period (2019-2024), base year (2025), and estimated year (2025), this report projects market trends through 2033. The study covers key market segments, competitive dynamics, and growth drivers, equipping readers with the knowledge needed to navigate this rapidly evolving landscape. Recent developments include: September 2023: Audi of America and IC Bus Navistar school buses illustrated the role of direct connection via Cellular Vehicle to Everything (C-V2X). Technology may play a role in providing potentially life-saving safety technologies for the 26 million students who ride school buses in the United States. The driver receives a direct message alert in the cockpit of the Audi vehicle using C-V2X direct communications technology. It will provide early notification of an approaching school bus stop situation even when the school bus is not visible to the driver., March 2023: First Student, North America's biggest supplier of student transportation services, chose Bechtel to assist its industry-leading electrification efforts. Bechtel will begin designing and installing charge stations for one of First Student's electrification projects in the United States., March 2022: The school bus manufacturers Highland Electric Fleets and Thomas Built Buses (TBB) announced an expansion of their mutual relationship to reduce upfront costs and accelerate the adoption of electric school buses across the country. In addition, both companies signed a letter of intent that will allow Highland to provide electric school bus subscriptions at cost parity with diesel until 2025., March 2022: California's Modesto City Schools ordered 30 Blue Bird All-American Type D electric school buses. The agreement, Blue Bird's largest single order of electric school buses from a school district to date, allows the district to convert roughly half of its diesel-powered bus fleet to electric. Zero-emission vehicles are expected to be available in the fourth quarter of 2022, according to the district. Blue Bird electric buses come with a capacity of 84 passengers and can travel up to 120 miles on a single charge.. Key drivers for this market are: Potential Shift Toward Adoption of Electric Buses to Drive the Market. Potential restraints include: Lack of Electric Charging Infrastructure May Hamper the Growth of the Market. Notable trends are: Potential Shift Toward Adoption of Electric Buses to Drive the Market.

The USA electric school bus market is experiencing robust growth, driven by stringent emission regulations, increasing environmental concerns, and government incentives aimed at promoting sustainable transportation. The market, currently valued at an estimated $500 million in 2025 (based on the global market size and the significant proportion of school bus adoption in the US), is projected to expand at a CAGR of approximately 5% from 2025 to 2033. This growth is fueled by a significant shift towards electrification in the public transportation sector, coupled with the increasing awareness of the health benefits associated with reduced tailpipe emissions in school environments. Key players such as Blue Bird Corporation, Navistar Inc (IC Bus), and Lion Electric are leading this transition, investing heavily in R&D and production capacity to meet the growing demand. The market segmentation shows a strong preference towards Type A and Type B school buses, particularly in urban areas, due to their maneuverability and suitability for shorter routes. While the initial investment cost of electric school buses remains higher than their diesel counterparts, decreasing battery costs and increasing operational efficiencies (lower fuel and maintenance expenses) are making them increasingly cost-competitive over their lifespan. The market's growth, however, isn't without challenges. Infrastructure limitations, particularly concerning charging infrastructure, remain a significant hurdle, especially in rural areas with limited grid capacity. Range anxiety among school districts and concerns about battery lifespan and replacement costs also need addressing. Despite these restraints, the long-term outlook for the USA electric school bus market remains highly positive. Continued technological advancements in battery technology, government support in the form of grants and subsidies, and the increasing availability of charging infrastructure are expected to accelerate adoption in the coming years. The evolving regulatory landscape, pushing for cleaner transportation, will further propel the market's growth trajectory, making it a highly attractive sector for investment and innovation. Recent developments include: September 2023: Audi of America and IC Bus Navistar school buses illustrated the role of direct connection via Cellular Vehicle to Everything (C-V2X). Technology may play a role in providing potentially life-saving safety technologies for the 26 million students who ride school buses in the United States. The driver receives a direct message alert in the cockpit of the Audi vehicle using C-V2X direct communications technology. It will provide early notification of an approaching school bus stop situation even when the school bus is not visible to the driver., March 2023: First Student, North America's biggest supplier of student transportation services, chose Bechtel to assist its industry-leading electrification efforts. Bechtel will begin designing and installing charge stations for one of First Student's electrification projects in the United States., March 2022: The school bus manufacturers Highland Electric Fleets and Thomas Built Buses (TBB) announced an expansion of their mutual relationship to reduce upfront costs and accelerate the adoption of electric school buses across the country. In addition, both companies signed a letter of intent that will allow Highland to provide electric school bus subscriptions at cost parity with diesel until 2025., March 2022: California's Modesto City Schools ordered 30 Blue Bird All-American Type D electric school buses. The agreement, Blue Bird's largest single order of electric school buses from a school district to date, allows the district to convert roughly half of its diesel-powered bus fleet to electric. Zero-emission vehicles are expected to be available in the fourth quarter of 2022, according to the district. Blue Bird electric buses come with a capacity of 84 passengers and can travel up to 120 miles on a single charge.. Key drivers for this market are: Potential Shift Toward Adoption of Electric Buses to Drive the Market. Potential restraints include: Potential Shift Toward Adoption of Electric Buses to Drive the Market. Notable trends are: Potential Shift Toward Adoption of Electric Buses to Drive the Market.

description: Baltimore City is home to many green spaces, parks, and waterways. Some of the more widely recognized locations include the Inner Harbor, Middle Branch, Druid Hill , Gwynns Falls and Herring Run Parks. City residents in particular value access to green spaces as a place to recreate, exercise and congregate, but the City _ s green spaces serve a vital role in ensuring clean air and water for long term urban sustainability. Baltimore neighborhoods actively participate in increasing access to green spaces through tree planting and other watershed protection activities such as stream clean-ups. Urban living also enables residents the option to choose alternative means of transportation to reduce vehicle miles traveled by car. The City is already served by numerous modes of mass transit including MARC, metro, light rail, the Charm City Circulator, and bus lines. BNIA-JFI tracks eight indicators to measure the City _ s urban environment and transit. These indicators are categorized into the following categories: air quality and hazardous waste; tree canopy, alternative transportation mode use; and travel time to work.; abstract: Baltimore City is home to many green spaces, parks, and waterways. Some of the more widely recognized locations include the Inner Harbor, Middle Branch, Druid Hill , Gwynns Falls and Herring Run Parks. City residents in particular value access to green spaces as a place to recreate, exercise and congregate, but the City _ s green spaces serve a vital role in ensuring clean air and water for long term urban sustainability. Baltimore neighborhoods actively participate in increasing access to green spaces through tree planting and other watershed protection activities such as stream clean-ups. Urban living also enables residents the option to choose alternative means of transportation to reduce vehicle miles traveled by car. The City is already served by numerous modes of mass transit including MARC, metro, light rail, the Charm City Circulator, and bus lines. BNIA-JFI tracks eight indicators to measure the City _ s urban environment and transit. These indicators are categorized into the following categories: air quality and hazardous waste; tree canopy, alternative transportation mode use; and travel time to work.

Vehicle Information Vehicle Type: This column represents the type of vehicle. Possible values include:

Car: A standard passenger vehicle. Truck: A larger vehicle used for transporting goods. Bus: A vehicle designed to carry multiple passengers. Motorcycle: A two-wheeled motor vehicle. Fuel Type: This column indicates the type of fuel the vehicle uses. Possible values are:

Petrol: Also known as gasoline, a common fuel for internal combustion engines. Diesel: A type of fuel used in diesel engines, often found in larger vehicles like trucks and buses. Electric: Vehicles powered by electric batteries. Hybrid: Vehicles that use a combination of an internal combustion engine and electric propulsion. Engine Size: The size of the vehicle's engine, measured in liters. Larger engines typically produce more power and can lead to higher emissions.

Age of Vehicle: The age of the vehicle in years. Older vehicles may have higher emissions due to wear and tear or outdated technology.

Mileage: The total distance the vehicle has traveled, measured in kilometers or miles. Higher mileage can indicate more wear and potentially higher emissions.

Driving Conditions Speed: The average speed of the vehicle during the measurement period, measured in kilometers per hour (km/h) or miles per hour (mph). Vehicle emissions can vary with speed.

Acceleration: The rate at which the vehicle's speed increases, measured in meters per second squared (m/s²). Rapid acceleration can lead to higher emissions.

Road Type: The type of road the vehicle is driving on. Possible values include:

Highway: Major roads designed for fast travel. City: Urban roads with frequent stops and lower speeds. Rural: Country roads that may have varying conditions. Traffic Conditions: The level of traffic during the measurement period. Possible values include:

Free flow: Minimal traffic, allowing for smooth travel. Moderate: Some traffic, but generally steady movement. Heavy: High traffic, often leading to stop-and-go conditions. Environmental Conditions Temperature: The ambient temperature during the measurement period, measured in degrees Celsius (°C) or Fahrenheit (°F). Temperature can affect engine performance and emissions.

Humidity: The relative humidity of the air during the measurement period, measured as a percentage. Humidity can affect the combustion process and emissions.

Wind Speed: The speed of the wind during the measurement period, measured in meters per second (m/s) or kilometers per hour (km/h). Wind can influence the dispersion of emissions.

Air Pressure: The atmospheric pressure during the measurement period, measured in hectopascals (hPa). Air pressure can affect engine efficiency and emissions.

Emission Data CO2 Emissions: The amount of carbon dioxide emitted by the vehicle, measured in grams per kilometer (g/km). CO2 is a major greenhouse gas contributing to climate change.

NOx Emissions: The amount of nitrogen oxides emitted by the vehicle, measured in grams per kilometer (g/km). NOx contributes to air pollution and can cause respiratory problems.

PM2.5 Emissions: The amount of particulate matter with a diameter of 2.5 micrometers or smaller emitted by the vehicle, measured in grams per kilometer (g/km). PM2.5 can penetrate deep into the lungs and cause health issues.

VOC Emissions: The amount of volatile organic compounds emitted by the vehicle, measured in grams per kilometer (g/km). VOCs contribute to the formation of ground-level ozone and smog.

SO2 Emissions: The amount of sulfur dioxide emitted by the vehicle, measured in grams per kilometer (g/km). SO2 can contribute to acid rain and respiratory problems.

Target Variable Emission Level: This column categorizes the overall emission level of the vehicle into three classes: Low: Vehicles with low emissions. Medium: Vehicles with moderate emissions. High: Vehicles with high emissions. Summary Categorical Features: Vehicle Type, Fuel Type, Road Type, Traffic Conditions, Emission Level. Continuous Numerical Features: Engine Size, Age of Vehicle, Mileage, Speed, Acceleration, Temperature, Humidity, Wind Speed, Air Pressure, CO2 Emissions, NOx Emissions, PM2.5 Emissions, VOC Emissions, SO2 Emissions.