The data set contains registered vehicle population count by various criteria such as vehicle class, vehicle status, vechicle make, vehicle model, vehicle year, plate class, plate declaration, county, weight related class and other vehicle decriptors.

This Mikrozensus special survey consists of two parts of the traffic statistics: motor vehicles and driving licenses The first part is a repetition of the Mikrozensus special survey from September 1971 (Mikrozensus MZ7103) on motor vehicles and their road performance. The results of this survey were the basis for studies and measure in the fields of traffic policy, road safety and the general transport. By repeating this special survey, new data for these fields is collected. Moreover, changes due to the strong increase in the number of vehicles are are evaluated. More attention, than in the study from 1971, is also given to the energy consumption resulting from the performance of the vehicle. The questions are only on certain types of vehicles which are of special interest due to their road performance (passenger cars, estate cars, motorcycles, mopeds). Preliminary, important vehicle data and personal data of its owner are are collected. Then the questions are on the mileage at the time the vehicle was bought and at the time of the survey, as well as on the last working day’s and last weekend’s mileage. Owner’s of passenger- or estate cars are also asked how many people usually drive the car (as driver or passenger) from Monday to Friday as well as on the weekends and for what what purpose the car is mainly used. Up until now, statistics on driving licenses have only been conducted in some states on varying form (and therefore not really comparable). The results of this survey should provide information for the whole federal territory on the number of people with driving licenses, the data of the acquiring of the licence and the groups these licenses refer to. Probability: Stratified: Disproportional Face-to-face interview

This dataset is historical. For recent data, we recommend using https://chicagotraffictracker.com. -- Average Daily Traffic (ADT) counts are analogous to a census count of vehicles on city streets. These counts provide a close approximation to the actual number of vehicles passing through a given location on an average weekday. Since it is not possible to count every vehicle on every city street, sample counts are taken along larger streets to get an estimate of traffic on half-mile or one-mile street segments. ADT counts are used by city planners, transportation engineers, real-estate developers, marketers and many others for myriad planning and operational purposes. Data Owner: Transportation. Time Period: 2006. Frequency: A citywide count is taken approximately every 10 years. A limited number of traffic counts will be taken and added to the list periodically. Related Applications: Traffic Information Interactive Map (http://webapps.cityofchicago.org/traffic/).

Context

My Uber Drives (2016)

Here are the details of my Uber Drives of 2016. I am sharing this dataset for data science community to learn from the behavior of an ordinary Uber customer.

Content

Geography: USA, Sri Lanka and Pakistan

Time period: January - December 2016

Unit of analysis: Drives

Total Drives: 1,155

Total Miles: 12,204

Dataset: The dataset contains Start Date, End Date, Start Location, End Location, Miles Driven and Purpose of drive (Business, Personal, Meals, Errands, Meetings, Customer Support etc.)

Acknowledgements & References

Users are allowed to use, download, copy, distribute and cite the dataset for their pet projects and training. Please cite it as follows: “Zeeshan-ul-hassan Usmani, My Uber Drives Dataset, Kaggle Dataset Repository, March 23, 2017.”

Past Research

Uber TLC FOIL Response - The dataset contains over 4.5 million Uber pickups in New York City from April to September 2014, and 14.3 million more Uber pickups from January to June 2015 https://github.com/fivethirtyeight/uber-tlc-foil-response

1.1 Billion Taxi Pickups from New York - http://toddwschneider.com/posts/analyzing-1-1-billion-nyc-taxi-and-uber-trips-with-a-vengeance/

What you can do with this data - a good example by Yao-Jen Kuo - https://yaojenkuo.github.io/uber.html

Inspiration

Some ideas worth exploring:

• What is the average length of the trip?

• Average number of rides per week or per month?

• Total tax savings based on traveled business miles?

• Percentage of business miles vs personal vs. Meals

• How much money can be saved by a typical customer using Uber, Careem, or Lyft versus regular cab service?

This Mikrozensus special survey consists of two parts of the traffic statistics: motor vehicles and driving licenses The first part is a repetition of the Mikrozensus special survey from September 1971 (Mikrozensus MZ7103) on motor vehicles and their road performance. The results of this survey were the basis for studies and measure in the fields of traffic policy, road safety and the general transport. By repeating this special survey, new data for these fields is collected. Moreover, changes due to the strong increase in the number of vehicles are are evaluated. More attention, than in the study from 1971, is also given to the energy consumption resulting from the performance of the vehicle. The questions are only on certain types of vehicles which are of special interest due to their road performance (passenger cars, estate cars, motorcycles, mopeds). Preliminary, important vehicle data and personal data of its owner are are collected. Then the questions are on the mileage at the time the vehicle was bought and at the time of the survey, as well as on the last working day’s and last weekend’s mileage. Owner’s of passenger- or estate cars are also asked how many people usually drive the car (as driver or passenger) from Monday to Friday as well as on the weekends and for what what purpose the car is mainly used. Up until now, statistics on driving licenses have only been conducted in some states on varying form (and therefore not really comparable). The results of this survey should provide information for the whole federal territory on the number of people with driving licenses, the data of the acquiring of the licence and the groups these licenses refer to.

**This data set was last updated 3:30 PM ET Monday, January 4, 2021. The last date of data in this dataset is December 31, 2020. **

Overview

Data shows that mobility declined nationally since states and localities began shelter-in-place strategies to stem the spread of COVID-19. The numbers began climbing as more people ventured out and traveled further from their homes, but in parallel with the rise of COVID-19 cases in July, travel declined again.

This distribution contains county level data for vehicle miles traveled (VMT) from StreetLight Data, Inc, updated three times a week. This data offers a detailed look at estimates of how much people are moving around in each county.

Data available has a two day lag - the most recent data is from two days prior to the update date. Going forward, this dataset will be updated by AP at 3:30pm ET on Monday, Wednesday and Friday each week.

This data has been made available to members of AP’s Data Distribution Program. To inquire about access for your organization - publishers, researchers, corporations, etc. - please click Request Access in the upper right corner of the page or email kromano@ap.org. Be sure to include your contact information and use case.

Findings

• Nationally, data shows that vehicle travel in the US has doubled compared to the seven-day period ending April 13, which was the lowest VMT since the COVID-19 crisis began. In early December, travel reached a low not seen since May, with a small rise leading up to the Christmas holiday.
• Average vehicle miles traveled continues to be below what would be expected without a pandemic - down 38% compared to January 2020. September 4 reported the largest single day estimate of vehicle miles traveled since March 14.
• New Jersey, Michigan and New York are among the states with the largest relative uptick in travel at this point of the pandemic - they report almost two times the miles traveled compared to their lowest seven-day period. However, travel in New Jersey and New York is still much lower than expected without a pandemic. Other states such as New Mexico, Vermont and West Virginia have rebounded the least. ## About This Data The county level data is provided by StreetLight Data, Inc, a transportation analysis firm that measures travel patterns across the U.S.. The data is from their Vehicle Miles Traveled (VMT) Monitor which uses anonymized and aggregated data from smartphones and other GPS-enabled devices to provide county-by-county VMT metrics for more than 3,100 counties. The VMT Monitor provides an estimate of total vehicle miles travelled by residents of each county, each day since the COVID-19 crisis began (March 1, 2020), as well as a change from the baseline average daily VMT calculated for January 2020. Additional columns are calculations by AP.

Included Data

01_vmt_nation.csv - Data summarized to provide a nationwide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

02_vmt_state.csv - Data summarized to provide a statewide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

03_vmt_county.csv - Data providing a county level look at vehicle miles traveled. Includes VMT estimate, percent change compared to January and seven day rolling averages to smooth out the trend lines over time.

Additional Data Queries

* Filter for specific state - filters 02_vmt_state.csv daily data for specific state.

* Filter counties by state - filters 03_vmt_county.csv daily data for counties in specific state.

* Filter for specific county - filters 03_vmt_county.csv daily data for specific county.

Interactive

The AP has designed an interactive map to show percent change in vehicle miles traveled by county since each counties lowest point during the pandemic:

@(https://interactives.ap.org/vmt-map/)

Interactive Embed Code

Using the Data

This data can help put your county's mobility in context with your state and over time. The data set contains different measures of change - daily comparisons and seven day rolling averages. The rolling average allows for a smoother trend line for comparison across counties and states. To get the full picture, there are also two available baselines - vehicle miles traveled in January 2020 (pre-pandemic) and vehicle miles traveled at each geography's low point during the pandemic.

Caveats

• The data from StreetLight Data, Inc does not include data for some low-population counties with low VMT (<5,000 miles/day in their baseline month of January 2020). In our analyses, we only include the 2,779 counties that have daily data for the entire period (March 1, 2020 to current).
• In some cases, a lack of decline in mobility from March to April can indicate that movement in the county is essential to keeping the larger economy going or that residents need to drive further to reach essentials businesses like grocery stores compared to other counties.
• The VMT includes both passenger and commercial miles, so truck traffic is included. However, the proxy is based on the "total number of trip starts and ends for all devices whose most frequent location is in this county". It does not count the VMT of trucks cutting through a county.
• For those instances where travel begins in one county and ends in another, the county where the miles are recorded is always the vehicle’s home county. ###### Contact reporter Angeliki Kastanis at akastanis@ap.org.

The FDOT Annual Average Daily Traffic feature class provides spatial information on Annual Average Daily Traffic section breaks for the state of Florida. In addition, it provides affiliated traffic information like KFCTR, DFCTR and TFCTR among others. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 07/12/2025.Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/aadt.zip

The data is based upon traffic volume trends data collected by the United States Department of Transportation data from January 1971 to February 2013.Since June 2005, vehicle miles driven have fallen 8.75 percent. This decline has remained steady for the past 92 months. There are several reasons that may be causing this steady downward trend. It has been suggested that due to rising gas prices, the Great Recession, an aging population led by the Baby Boom generation which is comprised of Americans over the age of 55 who tend to drive less, and quite possibly younger Americans choosing to drive less. Between 2001 and 2009, the average yearly number of miles driven by 16- to 34-year-olds has dropped 23 percent.Researchers indicate that this trend may be linked to five principal factors:The cost of Driving has increasedThe recent recessionIt is harder to get a license in many statesMore younger people are choosing to live in transit-oriented areas andTechnology is making it easier to go car-freeData Source Information: Traffic Volume Trends is a monthly report based on hourly traffic count data reported by the States. These data are collected at approximately 4,000 continuous traffic counting locations nationwide and are used to estimate the percent change in traffic for the current month compared with the same month in the previous year. Estimates are re-adjusted annually to match the vehicle miles of travel from the Highway Performance Monitoring System and are continually updated with additional data.

Purpose: Although many people with an acquired brain injury (ABI) encounter difficulties with executive functioning and memory which could negatively affect driving, few people are assessed for fitness to drive after injury. The purpose of this systematic review was to synthesize the literature on factors affecting driving and public transportation among youth and young adults with ABI, post injury. Method: Seven databases were systematically searched for articles from 1980 to 2016. Studies were screened independently by two researchers who performed the data extraction. Study quality was appraised using the Standard Quality Assessment Criteria (Kmet) for evaluating primary research from a variety of fields. Results: Of the 6577 studies identified in the search, 25 met the inclusion criteria, which involved 1527 participants with ABI (mean age = 25.1) across eight countries. Six studies focused on driving assessment and fitness to drive, ten on driving performance or risk of accidents and nine studies explored issues related to accessing or navigating public transportation. Quality assessment of the included studies ranged from 0.60 to 0.95. Conclusions: Our findings highlight several gaps in clinical practice and research along with a critical need for enhanced fitness to drive assessments and transportation-related training for young people with ABI.

The provided code processes a Tajweed dataset, which appears to be a collection of audio recordings categorized by different Tajweed rules (Ikhfa, Izhar, Idgham, Iqlab). Let's break down the dataset's structure and the code's functionality:

Dataset Structure:

• Organized by Tajweed Rule and Sheikh: The dataset is structured into directories for each Tajweed rule (e.g., 'Ikhfa', 'Izhar'). Within each rule's directory, there are subdirectories representing different reciters (sheikhs). This hierarchical organization is crucial for creating a structured metadata file and for training machine learning models.
• Audio Files: The audio files (presumably WAV or other supported formats) are stored within the sheikh's subdirectories. The original filenames are not standardized.
• Multiple Sheikhs per Rule: The dataset includes multiple recitations for each rule from different sheikhs, offering diversity in pronunciation.
• Google Drive Storage: The dataset is located on Google Drive, which requires mounting the drive to access the data within a Colab environment.

Code Functionality:

1. Initialization and Imports: The code begins with necessary imports (pandas, pydub) and mounts Google Drive. Pydub is used for audio file format conversion.

2. Directory Listing: It initially checks if a specified directory exists (for example, Alaa_alhsri/Ikhfa) and lists its files, demonstrating basic file system access.

3. Metadata Creation: The core of the script is the generation of metadata, which provides essential information about each audio file. The tajweed_paths dictionary maps each Tajweed rule to a list of paths, associating each path with the reciter's name.

   • Iterating through Paths: The code iterates through each Tajweed rule and its corresponding paths.
   • File Listing: Inside each directory, it iterates through the audio files.
   • Metadata Dictionary: For each audio file, it creates a metadata dictionary that includes:
     • global_id: A unique identifier for each audio file.
     • original_filename: The original filename of the audio file.
     • new_filename: A standardized filename that incorporates the Tajweed rule (label), sheikh's ID, audio number, and a global ID.
     • label: The Tajweed rule.
     • sheikh_id: A numerical identifier for each sheikh.
     • sheikh_name: The name of the reciter.
     • audio_number: A sequential number for the audio files within a specific sheikh and Tajweed rule combination.
     • original_path: Full path to the original audio file.
     • new_path: Full path to the intended location for the renamed and potentially converted audio file.
   • Pandas DataFrame: The metadata is collected in a list of dictionaries and then converted into a Pandas DataFrame for easier viewing and processing. This DataFrame is highly informative.

4. File Renaming and Conversion:

   • File Renaming: (commented out) The code is able to rename the audio files to the standardized format defined in new_filename and store it in the designated directory.
   • Audio Conversion to WAV: The script then converts any files in the specified directories to .wav format, creating standardized files in a new output_dataset directory. The new filenames are based on rules, sheikh and a counter.

5. Metadata Export: Finally, the compiled metadata is saved as a CSV file (metadata.csv) in the output directory. This CSV file is crucial for training any machine learning model using 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.

Accident-Detection-Model

Accident Detection Model is made using YOLOv8, Google Collab, Python, Roboflow, Deep Learning, OpenCV, Machine Learning, Artificial Intelligence. It can detect an accident on any accident by live camera, image or video provided. This model is trained on a dataset of 3200+ images, These images were annotated on roboflow.

Problem Statement

• Road accidents are a major problem in India, with thousands of people losing their lives and many more suffering serious injuries every year.
• According to the Ministry of Road Transport and Highways, India witnessed around 4.5 lakh road accidents in 2019, which resulted in the deaths of more than 1.5 lakh people.
• The age range that is most severely hit by road accidents is 18 to 45 years old, which accounts for almost 67 percent of all accidental deaths.

Accidents survey

https://user-images.githubusercontent.com/78155393/233774342-287492bb-26c1-4acf-bc2c-9462e97a03ca.png" alt="Survey">

Literature Survey

• Sreyan Ghosh in Mar-2019, The goal is to develop a system using deep learning convolutional neural network that has been trained to identify video frames as accident or non-accident.
• Deeksha Gour Sep-2019, uses computer vision technology, neural networks, deep learning, and various approaches and algorithms to detect objects.

Research Gap

• Lack of real-world data - We trained model for more then 3200 images.
• Large interpretability time and space needed - Using google collab to reduce interpretability time and space required.
• Outdated Versions of previous works - We aer using Latest version of Yolo v8.

Proposed methodology

• We are using Yolov8 to train our custom dataset which has been 3200+ images, collected from different platforms.
• This model after training with 25 iterations and is ready to detect an accident with a significant probability.

Model Set-up

Preparing Custom dataset

• We have collected 1200+ images from different sources like YouTube, Google images, Kaggle.com etc.
• Then we annotated all of them individually on a tool called roboflow.
• During Annotation we marked the images with no accident as NULL and we drew a box on the site of accident on the images having an accident
• Then we divided the data set into train, val, test in the ratio of 8:1:1
• At the final step we downloaded the dataset in yolov8 format.
  #### Using Google Collab
• We are using google colaboratory to code this model because google collab uses gpu which is faster than local environments.
• You can use Jupyter notebooks, which let you blend code, text, and visualisations in a single document, to write and run Python code using Google Colab.
• Users can run individual code cells in Jupyter Notebooks and quickly view the results, which is helpful for experimenting and debugging. Additionally, they enable the development of visualisations that make use of well-known frameworks like Matplotlib, Seaborn, and Plotly.
• In Google collab, First of all we Changed runtime from TPU to GPU.
• We cross checked it by running command ‘!nvidia-smi’
  #### Coding
• First of all, We installed Yolov8 by the command ‘!pip install ultralytics==8.0.20’
• Further we checked about Yolov8 by the command ‘from ultralytics import YOLO from IPython.display import display, Image’
• Then we connected and mounted our google drive account by the code ‘from google.colab import drive drive.mount('/content/drive')’
• Then we ran our main command to run the training process ‘%cd /content/drive/MyDrive/Accident Detection model !yolo task=detect mode=train model=yolov8s.pt data= data.yaml epochs=1 imgsz=640 plots=True’
• After the training we ran command to test and validate our model ‘!yolo task=detect mode=val model=runs/detect/train/weights/best.pt data=data.yaml’ ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt conf=0.25 source=data/test/images’
• Further to get result from any video or image we ran this command ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt source="/content/drive/MyDrive/Accident-Detection-model/data/testing1.jpg/mp4"’
• The results are stored in the runs/detect/predict folder.
  Hence our model is trained, validated and tested to be able to detect accidents on any video or image.

Challenges I ran into

I majorly ran into 3 problems while making this model

• I got difficulty while saving the results in a folder, as yolov8 is latest version so it is still underdevelopment. so i then read some blogs, referred to stackoverflow then i got to know that we need to writ an extra command in new v8 that ''save=true'' This made me save my results in a folder.
• I was facing problem on cvat website because i was not sure what

Over the course of the 20th century, the number of operational motor vehicles in the United States grew significantly, from just 8,000 automobiles in the year 1900 to more than 183 million private and commercial vehicles in the late 1980s. Generally, the number of vehicles increased in each year, with the most notable exceptions during the Great Depression and Second World War.

Graph and download economic data for Moving 12-Month Total Vehicle Miles Traveled (M12MTVUSM227NFWA) from Dec 1970 to Apr 2025 about miles, travel, vehicles, and USA.

A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity. The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions. This update features the most recent geographic boundaries (2019 Census Block Groups) and new and expanded sources of data used to calculate variables. Entirely new variables have been added and the methods used to calculate some of the SLD variables have changed. More information on the National Walkability index: https://www.epa.gov/smartgrowth/smart-location-mapping More information on the Smart Location Calculator: https://www.slc.gsa.gov/slc/

Number of vehicles travelling between Canada and the United States, by trip characteristics, length of stay and type of transportation. Data available monthly.