The data in this dataset comes from the Common Questionnaire for Transport Statistics, developed and surveyed by Eurostat in cooperation between the United Nations Economic Commission for Europe (UNECE) and the International Transport Forum (ITF) at OECD.

The Common Questionnaire is not supported by a legal act, but is based on a gentlemen's agreement with the participating countries; its completeness varies from country to country.

Eurostat’s datasets based on the Common Questionnaire cover annual data for the EU Member States, EFTA states and Candidate countries to the EU. Data for other participating countries are available through the ITF and the UNECE. In total, comparable transport data collected through the Common Questionnaire is available for close to 60 countries worldwide.

The Common Questionnaire collects aggregated annual data on:

• Railway transport
• Road transport
• Inland waterways transport
• Oil pipelines transport
• Road traffic - vehicle-kilometres
• Buses (and coaches)

For each mode of transport, the Common Questionnaire covers some or all of the following sub-modules (the number of questions/variables within each sub-module varies between the different modes of transport):

• Infrastructure (All modes)
• Transport equipment (Rail, Road and Inland Waterways)
• Enterprises, economic performance and employment (All modes)
• Traffic (Rail, Road and Inland Waterways)
• Transport measurement (All modes).

As its name suggests, the theme "Road traffic" focuses on "traffic" only, on road:

• of national vehicle stocks, on national and foreign territories, by type of road; and by vehicle category, fuel type and age. These data should rely on odometer readings;
• of national- and foreign-registered vehicles on national territories. These data should be issued from road censuses.

The theme “Buses and coaches” covers detailed information on road “traffic” (vkm) and “transport measurement” (passengers, passenger-km) performed by buses and coaches.

The data collection on Common Questionnaire was streamlined twice in the recent years:

• In 2013, among other changes, the collection of employment data was terminated;
• In 2019, among other changes, the collection of gas pipeline data was terminated, and the number of “Buses” indicators was reduced.

The Common Questionnaire is completed by the competent national authorities. The responsibility for completing specific modules (e.g. Transport by Rail) or part of modules (e.g. Road Infrastructure) may be delegated to other national authorities in charge of specific fields.

Global Ownership of Passenger Cars by Country, 2023 Discover more data with ReportLinker!

Global Special Purpose Motor Vehicles Market Size Value Per Capita by Country, 2023 Discover more data with ReportLinker!

Context

Automobile data holds immense importance as it offers insights into the functioning and efficiency of the automotive industry. It provides valuable information about car models, specifications, sales trends, consumer demographics, and preferences, which car manufacturers and dealerships can leverage to optimize their operations and enhance customer experiences. By analyzing data on vehicle reliability, fuel efficiency, safety ratings, and resale values, the automotive industry can identify trends and implement strategies to produce more reliable and environmentally friendly vehicles, improve safety standards, and enhance the overall value of cars for consumers. Moreover, regulatory bodies and policymakers rely on this data to enforce regulations, set emissions standards, and make informed decisions regarding automotive policies and environmental impacts. Researchers and analysts use car purchase data to study market trends, assess the environmental impact of various vehicle types, and develop strategies for sustainable growth within the industry. In essence, car purchase data serves as a foundation for informed decision-making, operational efficiency, and the overall advancement of the automotive sector.

Content

This dataset comprises diverse parameters relating to car purchases and ownership on a global scale. The dataset prominently incorporates fields such as 'First Name', 'Last Name', 'Country', 'Car Brand', 'Car Model', 'Car Color', 'Year of Manufacture', and 'Credit Card Type'. These columns collectively provide comprehensive insights into customer demographics, vehicle details, and payment information. Researchers and industry experts can leverage this dataset to analyze trends in car purchasing behavior, optimize the customer car-buying experience, evaluate the popularity of car brands and models, and understand payment preferences within the automotive industry.

Dataset Glossary (Column-wise)

• First Name - The first name of the car purchaser.
• Last Name - The last name of the car purchaser.
• Country - The country of residence of the car purchaser.
• Car Brand - The brand or manufacturer of the purchased car.
• Car Model - The specific model of the purchased car.
• Car Color - The color of the purchased car.
• Year of Manufacture - The year the car was manufactured.
• Credit Card Type - The type of credit card used for the car purchase.

Structure of the Dataset

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Acknowledgement

The dataset provided here is a simulated example and was generated using the online platform found at Mockaroo. This web-based tool offers a service that enables the creation of customizable mock datasets that closely resemble real data. It is primarily intended for use by developers, testers, and data experts who require sample data for a range of uses, including testing databases, filling applications with demonstration data, and crafting lifelike illustrations for presentations and tutorials. To explore further details, you can visit their website.

Cover Photo by: Freepik

Thumbnail by: Car icons created by Freepik - Flaticon

AU: Passenger Cars: Per One Million Units of Current USD GDP data was reported at 10.747 Ratio in 2020. This records an increase from the previous number of 10.686 Ratio for 2019. AU: Passenger Cars: Per One Million Units of Current USD GDP data is updated yearly, averaging 12.420 Ratio from Dec 1994 (Median) to 2020, with 26 observations. The data reached an all-time high of 26.972 Ratio in 2001 and a record low of 8.189 Ratio in 2012. AU: Passenger Cars: Per One Million Units of Current USD GDP data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Australia – Table AU.OECD.ITF: Motor Vehicles Statistics: OECD Member: Annual. PASSENGER CARS The stock of road motor vehicles is the number of road motor vehicles registered at a given date in a country and licenced to use roads open to public traffic. This includes road vehicles exempted from annual taxes or licence fee; it also includes imported second-hand vehicles and other road vehicles according to national practices. It should not include military vehicles.; PASSENGER CARS A passenger car is a road motor vehicle, other than a moped or a motorcycle, intended for the carriage of passengers and designed to seat no more than nine people (including the driver). It refers to category M1 of the UN Consolidated Resolution on the Construction of Vehicles. Passenger cars, vans designed and used primarily for transport of passengers, taxis, hire cars, ambulances and motor homes are not included. Light goods road vehicles, motor-coaches and buses and mini-buses/mini-coaches are not included. Microcars (needing no permit to be driven), taxis and passenger hire cars, provided that they have fewer than ten seats, are included.

European Household Expenditure on Purchase of Vehicles by Country, 2023 Discover more data with ReportLinker!

European Motor Vehicles Number of Persons Employed Share by Country (Units (Employees)), 2023 Discover more data with ReportLinker!

This dataset contains number of vehicles in GCC countries from 2008 - 2018 . Data from GCC Statistical center.Follow datasource.kapsarc.org for timely data to advance energy economics research.

Number of Cars: Privately Owned: Per 1000 Person: CF: City of Moscow data was reported at 282.272 Unit in 2022. This records a decrease from the previous number of 297.353 Unit for 2021. Number of Cars: Privately Owned: Per 1000 Person: CF: City of Moscow data is updated yearly, averaging 232.100 Unit from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 306.351 Unit in 2017 and a record low of 69.800 Unit in 1990. Number of Cars: Privately Owned: Per 1000 Person: CF: City of Moscow data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.RAD005: Number of Cars Privately Owned per 1000 Persons.

Global Motor Vehicles Shock Absorbers Market Size Value Per Capita by Country, 2023 Discover more data with ReportLinker!

European Motor Vehicles Number of Persons Employed by Country, 2023 Discover more data with ReportLinker!

Global Motor Vehicles Steering Wheels, Columns and Boxes Market Size Value Per Capita by Country, 2023 Discover more data with ReportLinker!

Introduction

Welcome to the Gujarati Language In-car Speech Dataset, a comprehensive collection of audio recordings designed to facilitate the development of speech recognition models specifically tailored for in-car environments. This dataset aims to support research and innovation in automotive speech technology, enabling seamless and robust voice interactions within vehicles for drivers and co-passengers.

Speech Data

This dataset comprises over 5,000 high-quality audio recordings collected from various in-car environments. These recordings include scripted wake words and command-type prompts.

Participant Diversity:

- Speakers: 50+ native Gujarati speakers from the FutureBeeAI Community.

- Regions: Ensures a balanced representation of Gujarat1 accents, dialects, and demographics.

- Participant Profile: Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.

Recording Nature: Scripted wake word and command type of audio recordings.

- Duration: Average duration of 5 to 20 seconds per audio recording.

- Formats: WAV format with mono channels, a bit depth of 16 bits. The dataset contains different data at 16kHz and 48kHz.

Dataset Diversity

Apart from participant diversity, the dataset is diverse in terms of different wake words, voice commands, and recording environments.

Different Automobile Related Wake Words: Hey Mercedes, Hey BMW, Hey Porsche, Hey Volvo, Hey Audi, Hi Genesis, Hey Mini, Hey Toyota, Ok Ford, Hey Hyundai, Ok Honda, Hello Kia, Hey Dodge.

Different Cars: Data collection was carried out in different types and models of cars.

Different Types of Voice Commands:

- Navigational Voice Commands

- Mobile Control Voice Commands

- Car Control Voice Commands

- Multimedia & Entertainment Commands

- General, Question Answer, Search Commands

Recording Time: Participants recorded the given prompts at various times to make the dataset more diverse.

- Morning

- Afternoon

- Evening

Recording Environment: Various recording environments were captured to acquire more realistic data and to make the dataset inclusive of various types of noises. Some of the environment variables are as follows:

- Noise Level: Silent, Low Noise, Moderate Noise, High Noise

- Parking Location: Indoor, Outdoor

- Car Windows: Open, Closed

- Car AC: On, Off

- Car Engine: On, Off

- Car Movement: Stationary, Moving

Metadata

The dataset provides comprehensive metadata for each audio recording and participant:

Participant Metadata: Unique identifier, age, gender, country, state, district, accent, and dialect.

Other Metadata: Recording transcript, recording environment, device details, sample rate, bit depth, file format, recording time.

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Gujarati voice assistant speech recognition models.

License

This Gujarati In-car audio dataset is created by FutureBeeAI and is available for commercial use.

Introduction

Welcome to the US English Language In-car Speech Dataset, a comprehensive collection of audio recordings designed to facilitate the development of speech recognition models specifically tailored for in-car environments. This dataset aims to support research and innovation in automotive speech technology, enabling seamless and robust voice interactions within vehicles for drivers and co-passengers.

Speech Data

This dataset comprises over 5,000 high-quality audio recordings collected from various in-car environments. These recordings include scripted wake words and command-type prompts.

Participant Diversity:

- Speakers: 50+ native English speakers from the FutureBeeAI Community.

- Regions: Ensures a balanced representation of United States of America1 accents, dialects, and demographics.

- Participant Profile: Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.

Recording Nature: Scripted wake word and command type of audio recordings.

- Duration: Average duration of 5 to 20 seconds per audio recording.

- Formats: WAV format with mono channels, a bit depth of 16 bits. The dataset contains different data at 16kHz and 48kHz.

Dataset Diversity

Apart from participant diversity, the dataset is diverse in terms of different wake words, voice commands, and recording environments.

Different Automobile Related Wake Words: Hey Mercedes, Hey BMW, Hey Porsche, Hey Volvo, Hey Audi, Hi Genesis, Hey Mini, Hey Toyota, Ok Ford, Hey Hyundai, Ok Honda, Hello Kia, Hey Dodge.

Different Cars: Data collection was carried out in different types and models of cars.

Different Types of Voice Commands:

- Navigational Voice Commands

- Mobile Control Voice Commands

- Car Control Voice Commands

- Multimedia & Entertainment Commands

- General, Question Answer, Search Commands

Recording Time: Participants recorded the given prompts at various times to make the dataset more diverse.

- Morning

- Afternoon

- Evening

Recording Environment: Various recording environments were captured to acquire more realistic data and to make the dataset inclusive of various types of noises. Some of the environment variables are as follows:

- Noise Level: Silent, Low Noise, Moderate Noise, High Noise

- Parking Location: Indoor, Outdoor

- Car Windows: Open, Closed

- Car AC: On, Off

- Car Engine: On, Off

- Car Movement: Stationary, Moving

Metadata

The dataset provides comprehensive metadata for each audio recording and participant:

Participant Metadata: Unique identifier, age, gender, country, state, district, accent, and dialect.

Other Metadata: Recording transcript, recording environment, device details, sample rate, bit depth, file format, recording time.

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of English voice assistant speech recognition models.

License

This US English In-car audio dataset is created by FutureBeeAI and is available for commercial use.

This is a cars data set which is used to perform a data analysis process about this data. This data set also used to predict the type of car based on the features input like MPG, Cylinders, Displacement, Horsepower, Weight, Acceleration, Year of publishing and Country of origin. Use this data set to practice and implement the new techniques.

Cars and how much fuels they use

• Cars 1 Data is collected from link ### Dataset of electric passenger cars with their specifications This dataset lists all fully electric passenger cars with their attributes (properties). The collection does not contain data on plug-in hybrid cars and electric cars from the so-called “range extenders”. Hydrogen cars were also not included in the dataset due to the insufficient number of mass-produced models and different (compared to EV) specificity of the vehicle, including the different charging methods. The dataset includes cars that, as of 2 December, 2020, could be purchased in Poland as new at an authorized dealer and those available in public and general presale, but only if a publicly available price list with equipment versions and full technical parameters was available. The list does not include discontinued cars that cannot be purchased as new from an authorized dealer (also when they are not available in stock). The subject of the study is only passenger cars, the main purpose of which is to transport people (inter alia without including vans intended for professional deliveries). The dataset of electric cars includes all fully electric cars on the primary market that were obtained from official materials (technical specifications and catalogs) provided by automotive manufacturers with a license to sell cars in Poland. These materials were downloaded from their official websites. In the event that the data provided by the manufacturer were incomplete, the information was supplemented with data from the SAMAR AutoCatalog (link can be found in References section). The database consisting of 53 electric cars (each variant of a model – which differs in terms of battery capacity, engine power, etc. – is treated as separate) and 22 variables (25 variables, including make, model and “car name” merging these two previous).
• FEV data are collected from link ### Electric vehicle population This dataset shows the Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) that are currently registered through Washington State Department of Licensing (DOL).
• Data collected from link

Global Motor Vehicles Safety Seat Belts Market Size Value Per Capita by Country, 2023 Discover more data with ReportLinker!

Statewise Analysis of Electric Vehicles and Charging Stations in India

This dataset presents a combined analysis of electric vehicle (EV) adoption and charging infrastructure deployment in India by merging two distinct datasets. Utilizing data from the Kaggle repository on Electric Vehicle Charging Stations in India and the official government resource on Statewise Current Sales of Electric Vehicles, this dataset offers a unified view of EV counts and charging station distributions across different states.

The datasets have been merged based on state names, ensuring consistency and coherence in the analysis. The provided preprocessing file, available via my GitHub, details the steps undertaken to merge and preprocess the datasets, guaranteeing data integrity and reliability.

Explore the statewise distribution of electric vehicles and charging stations to uncover regional trends and patterns in EV adoption and infrastructure development and understand the geographical dynamics shaping the transition towards electric mobility in India.

According to IBEF “Domestic automobiles, production increased at 2.36% CAGR between FY16-20 with 26.36 million vehicles being manufactured in the country in FY20. Overall, domestic automobiles sales increased at 1.29% CAGR between FY16-FY20 with 21.55 million vehicles being sold in FY20”.The rise in vehicles on the road will also lead to multiple challenges and the road will be more vulnerable to accidents. Increased accident rates also lead to more insurance claims and payouts rise for insurance companies.

In order to pre-emptively plan for the losses, the insurance firms leverage accident data to understand the risk across the geographical units e.g. Postal code/district etc.

In this challenge, we are providing you with the dataset to predict the “Accident_Risk_Index” against the postcodes. Accident_Risk_Index (mean casualties at a postcode) = sum(Number_of_casualities)/count(Accident_ID)

Pro-tip: The participants are required to perform feature engineering to the first roll up the train data at postcode level and create a column as “accident_risk_index” and optimize the model against postcode level.

Few Hypothesis to help you think: "More accidents happen in the later part of the day as those are office hours causing congestion"

"Postal codes with more single carriage roads have more accidents"

(***In the above hypothesis features such as office_hours_flag and #single _carriage roads can be formed)

Additionally, we are providing you with road network data (contains info on the nearest road to a postcode and its characteristics) and population data (contains info about the population at the area level). This info is for augmentation of features, but not mandatory to use.

The provided dataset contains the following files:

Train: 4,84,042 rows x 27 columns

Test: 1,15,958 rows x 27 columns

train.csv & test.csv:

'Accident_ID', 'Police_Force', 'Number_of_Vehicles', 'Number_of_Casualties', 'Date', 'Day_of_Week', 'Time', ‘Local_Authority_(District)', 'Local_Authority_(Highway)', '1st_Road_Class', '1st_Road_Number', 'Road_Type', 'Speed_limit', '2nd_Road_Class', '2nd_Road_Number', 'Pedestrian_Crossing-Human_Control', 'Pedestrian_Crossing-Physical_Facilities', 'Light_Conditions', ‘'Weather_Conditions', 'Road_Surface_Conditions', 'Special_Conditions_at_Site', 'Carriageway_Hazards', 'Urban_or_Rural_Area', 'Did_Police_Officer_Attend_Scene_of_Accident', 'state', 'postcode', 'country'

Population: 8,035 rows x 10 columns

population.csv:

​​'postcode', 'Rural Urban', 'Variable: All usual residents; measures: Value', 'Variable: Males; measures: Value', 'Variable: Females; measures: Value', ‘Variable: Lives in a household; measures: Value', ‘Variable: Lives in a communal establishment; measures: Value', 'Variable: Schoolchild or full-time student aged 4 and over at their non term-time address; measures: Value', 'Variable: Area (Hectares); measures: Value', 'Variable: Density (number of persons per hectare); measures: Value'

Road Network: 91,566 rows x 8 columns

roads_network.csv:

'WKT', 'roadClassi', ‘roadFuncti', 'formOfWay', 'length', 'primaryRou', 'distance to the nearest point on rd', 'postcode’

Acknowledgements

The license for this dataset is the Open Government Licence used by all data on data.gov.uk here data downloaded from machine hack

European Number of Persons Employed of Renting and Leasing of Cars and Light Motor Vehicles by Country, 2023 Discover more data with ReportLinker!

European Sold Production of Motor Vehicles for the Transport of More than 10 Persons Share by Country (Units (Items)), 2023 Discover more data with ReportLinker!