This dataset contains traffic violation information from all electronic traffic violations issued in the County. Any information that can be used to uniquely identify the vehicle, the vehicle owner or the officer issuing the violation will not be published. Update Frequency: Daily

Context

This dataset contains traffic violation information from all electronic traffic violations issued in the County. Any information that can be used to uniquely identify the vehicle, the vehicle owner or the officer issuing the violation will not be published.

Acknowledgements

source of original dataset: https://catalog.data.gov/dataset/traffic-violations-56dda

Inspiration

The original dataset was just too time-consuming to perform several basic tasks. Thus, I shuffled it and took the first 100k rows.

This dataset contains 4,000 records of traffic violations reported across different states in India. It includes detailed information about the violation type, fine amount, vehicle details, driver characteristics, enforcement agency, weather conditions, and additional penalty-related data.

The dataset can be used for traffic pattern analysis, violation trend predictions, fine collection efficiency studies, and road safety improvements.

Key Features: Violation Details: Type, fine amount, date, time, location.

Vehicle Information: Type, color, model year, registration state.

Driver Profile: Age, gender, license type, previous violations.

Enforcement Details: Officer ID, issuing agency, towed status, fine payment.

Environmental Factors: Weather conditions, road status, speed limits.

This dataset is ideal for machine learning models, traffic enforcement strategies, and public safety research. 🚗⚖️

Data extracted from records of tickets on file with NYS DMV. The tickets were issued to motorists for violations of: NYS Vehicle & Traffic Law (VTL), Thruway Rules and Regulations, Tax Law, Transportation Law, Parks and Recreation Regulations, Local New York City Traffic Ordinances, and NYS Penal Law pertaining to the involvement of a motor vehicle in acts of assault, homicide, manslaughter and criminal negligence resulting in injury or death.

About Dataset: This dataset contains around 65k+ traffic-related violation records.

Attribute Information: 1. stop_date - Date of violation 2. stop_time - Time of violation 3. driver_gender - Gender of violators (Male-M, Female-F) 4. driver_age - Age of violators 5. driver_race - Race of violators 6. violation - Category of violation : - Speeding - Moving Violation (Reckless Driving, Hit and run, Assaulting another driver, pedestrian, improper turns and lane changes, etc) - Equipment (Window tint violations, Headlight/taillights out, Loud exhaust, Cracked windshield, etc.) - Registration/Plates - Seat Belt - other (Call for Service, Violation of City/Town Ordinance, Suspicious Person, Motorist Assist/Courtesy, etc.) 7. search_conducted - Whether search is conducted in True and False form 8. stop_outcome - Result of violation 9. is_arrested - Whether a person was arrested in True and False form 10. stop_duration - Detained time for violators approx (in minutes) 11. drugs_related_stop - Whether a person was involved in drugs crime (True, False)

The accompanying data cover all MPD stops including vehicle, pedestrian, bicycle, and harbor stops for the period from January 1, 2023 – December 31, 2024. A stop may involve a ticket (actual or warning), investigatory stop, protective pat down, search, or arrest. If the final outcome of a stop results in an actual or warning ticket, the ticket serves as the official documentation for the stop. The information provided in the ticket include the subject’s name, race, gender, reason for the stop, and duration. All stops resulting in additional law enforcement actions (e.g., pat down, search, or arrest) are documented in MPD’s Record Management System (RMS). This dataset includes records pulled from both the ticket (District of Columbia Department of Motor Vehicles [DMV]) and RMS sources. Data variables not applicable to a particular stop are indicated as “NULL.” For example, if the stop type (“stop_type” field) is a “ticket stop,” then the fields: “stop_reason_nonticket” and “stop_reason_harbor” will be “NULL.” Each row in the data represents an individual stop of a single person, and that row reveals any and all recorded outcomes of that stop (including information about any actual or warning tickets issued, searches conducted, arrests made, etc.). A single traffic stop may generate multiple tickets, including actual, warning, and/or voided tickets. Additionally, an individual who is stopped and receives a traffic ticket may also be stopped for investigatory purposes, patted down, searched, and/or arrested. If any of these situations occur, the “stop_type” field would be labeled “Ticket and Non-Ticket Stop.” If an individual is searched, MPD differentiates between person and property searches. Please note that the term property in this context refers to a person’s belongings and not a physical building. The “stop_location_block” field represents the block-level location of the stop and/or a street name. The age of the person being stopped is calculated based on the time between the person’s date of birth and the date of the stop. There are certain locations that have a high prevalence of non-ticket stops. These can be attributed to some centralized processing locations. Additionally, there is a time lag for data on some ticket stops as roughly 20 percent of tickets are handwritten. In these instances, the handwritten traffic tickets are delivered by MPD to the DMV, and then entered into data systems by DMV contractors. On August 1, 2021, MPD transitioned to a new version of its current records management system, Mark43 RMS. Beginning January 1, 2023, fields pertaining to the bureau, division, unit, and PSA (if applicable) of the officers involved in events where a stop was conducted were added to the dataset. MPD’s Records Management System (RMS) captures all members associated with the event but cannot isolate which officer (if multiple) conducted the stop itself. Assignments are captured by cross-referencing officers’ CAD ID with MPD’s Timesheet Manager Application. These fields reflect the assignment of the officer issuing the Notice of Infraction (NOIs) and/or the responding officer(s), assisting officer(s), and/or arresting officer(s) (if an investigative stop) as of the end of the two-week pay period for January 1 – June 30, 2023 and as of the date of the stop for July 1, 2023 and forward. The values are comma-separated if multiple officers were listed in the report. For Stop Type = Harbor and Stop Type = Ticket Only, the officer assignment information will be in the NOI_Officer fields. For Stop Type = Ticket and Non-Ticket the officer assignments will be in both NOI Officer (for the officer that issued the NOI) and RMS_Officer fields (for any other officer involved in the event, which may also be the officer who issued the NOI). For Stop Type = Non-Ticket, the officer assignment information will be in the RMS_Officer fields. Null values in officer assignment fields reflect either Reserve Corps members, who’s assignments are not captured in the Timesheet Manager Application, or members who separated from MPD between the time of the stop and the time of the data extraction. Finally, MPD is conducting on-going data audits on all data for thorough and complete information. Figures are subject to change due to delayed reporting, on-going data quality audits, and data improvement processes.

Data extracted from records of convictions on file with NYS DMV. The convictions were from traffic tickets issued to motorists for violations of:— NYS Vehicle & Traffic Law (VTL),— Thruway Rules and Regulations— Tax Law— Transportation Law— Parks and Recreation Regulations— Local New York City Traffic Ordinances and— NYS Penal Law pertaining to the involvement of a motor vehicle in acts of assault, homicide, manslaughter and criminal negligence resulting in injury or death.

Dataset

This dataset was created by Sanjana chaudhari☑️

Contents

This dataset contains traffic citation information (criminal and civil) from January 2018 through September 8, 2025*, including demographic information for officers as well as individuals.

*Note: We want to inform our users that updates to this dataset is currently unavailable from September 9, 2025, forward. The city is actively working with our partners to restore regular data publishing and is committed to resuming monthly updates as soon as possible. We appreciate your patience and understanding during this time. Our goal is to ensure the accuracy, consistency, and timeliness of the data we provide. Please check back for updates and thank you for your continued interest in open data.

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This complete version of the dataset contains traffic citations issued in Somerville by Somerville police officers since 2017. Citations include both written warnings and those with a monetary fine. Every citation is composed of one or more violations. Each row in the dataset represents a violation. This data set should be refreshed daily with data appearing with a one-month delay (e.g. citations issued on 1/1 will appear on 2/1). If a daily update does not refresh, please email data@somervillema.gov.

Released under formal Government Information Public Access (GIPA) Application to Department of Finance, Services and Innovation (DFSI) - FA#2915-16.

"1. Please state the total number of camera-detected speeding infringements issued in NSW in 2014 that were replaced by a caution based on good driving record, broken down to a.) fixed cameras, b.) mobile cameras, c.) safety/intersection cameras for speed only.

1. Please state the total number of camera-detected speeding infringements issued in NSW in 2014 were subject to reviews, broken down to a.) fixed cameras, b.) mobile cameras, c.) safety/intersection cameras for speed only.

2. Please state the total number of camera-detected speeding infringements issued in NSW in 2014 were subject to reviews that were later withdrawn or converted to cautions, broken down to a.) fixed cameras, b.) mobile cameras, c.) safety/intersection cameras for speed only.

3. For those offences subject to referred to in 4.), please give category of reason why the infringement was withdrawn. Please state reasons, such as medical emergency or speedometer accuracy.

4. Please state the total number of camera-detected speeding infringements issued in NSW in 2014 that were taken to court and then withdrawn following a court order, broken down to a.) fixed cameras, b.) mobile cameras, c.) safety/intersection cameras for speed only.

"

Traffic Violations For Royal

## Overview

Traffic Violations For Royal is a dataset for object detection tasks - it contains Helmet Seatbelt annotations for 1,118 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

The dataset contains year- and month-wise data (as available on the government website) on electronic challans (e-Challans) issued, disposed, and pending with transport and traffic departments in traffic violation cases. It also includes details on e-Challans taken to court, along with their disposal and pending status. Additionally, the dataset covers the amount collected and revenue generated from penalties

Dataset

This dataset was created by Madhura Pande

Contents

Here are a few use cases for this project:

1. Traffic Management Applications: Traffic authorities can use the "Speed Limitation" model to monitor highways and city roads for compliance with speed limits. If a vehicle is detected to be exceeding the speed limit, automatic notifications can be sent either to the vehicle's dashboard (in smart cars) or directly to traffic police.

2. Navigation And Map Services: Integrate it into map services like Google Maps, Waze, or in-car navigation systems to alert drivers about the speed limits in real-time, improving road safety and preventing speeding tickets.

3. Autonomous Vehicle Systems: Utilize this model as part of the guidance and regulation systems in self-driving cars. The cars will constantly scan for these signs to adapt their driving speed in real-time, ensuring they adhere to road regulations.

4. Traffic-Related Intelligent Tutoring Systems: Educators can leverage it in driving schools or online driver education programs, teaching new drivers to identify and understand the diverse speed-related traffic signs and abide by them.

5. Research and Studies: Researchers in traffic engineering and road safety can use this model to investigate the impact of different speed limits on traffic flow and accident rates, strengthening their understanding of real-world traffic conditions and proposing efficient road management strategies.

This dataset contains all tickets and citations that have been issued and all of the charged offenses for each citation. One citation may include multiple offenses. Demographic information for the ticketed person is also included.

Here are a few use cases for this project:

1. Traffic Flow Analysis: The dataset can be used in machine learning models to analyze traffic flow in cities. It can identify the type of vehicles on the city roads at different times of the day, helping in planning and traffic management.

2. Vehicle Class Based Toll Collection: Toll booths can use this model to automatically classify and charge vehicles based on their type, enabling a more efficient and automated system.

3. Parking Management System: Parking lot owners can use this model to easily classify vehicles as they enter for better space management. Knowing the vehicle type can help assign it to the most suitable parking spot.

4. Traffic Rule Enforcement: The dataset can be used to create a computer vision model to automatically detect any traffic violations like wrong lane driving by different vehicle types, and notify law enforcement agencies.

5. Smart Ambulance Tracking: The system can help in identifying and tracking ambulances and other emergency vehicles, enabling traffic management systems to provide priority routing during emergencies.

This dataset provides traffic tickets issued by law enforcement, capturing a range of information related to traffic enforcement activities. This data is valuable for analyzing traffic safety trends, evaluating the impact of enforcement on driving behaviors, identifying high-risk areas, and informing policies to enhance road safety. Data is from January 1st, 2023 to December 31st, 2023.For inquiries about the data, please call 515-283-4887.

🇺🇸 United States English The accompanying data cover all MPD stops including vehicle, pedestrian, bicycle, and harbor stops for the period from January 1, 2023 – June 30, 2024. A stop may involve a ticket (actual or warning), investigatory stop, protective pat down, search, or arrest.If the final outcome of a stop results in an actual or warning ticket, the ticket serves as the official documentation for the stop. The information provided in the ticket include the subject’s name, race, gender, reason for the stop, and duration. All stops resulting in additional law enforcement actions (e.g., pat down, search, or arrest) are documented in MPD’s Record Management System (RMS). This dataset includes records pulled from both the ticket (District of Columbia Department of Motor Vehicles [DMV]) and RMS sources. Data variables not applicable to a particular stop are indicated as “NULL.” For example, if the stop type (“stop_type” field) is a “ticket stop,” then the fields: “stop_reason_nonticket” and “stop_reason_harbor” will be “NULL.”Each row in the data represents an individual stop of a single person, and that row reveals any and all recorded outcomes of that stop (including information about any actual or warning tickets issued, searches conducted, arrests made, etc.). A single traffic stop may generate multiple tickets, including actual, warning, and/or voided tickets. Additionally, an individual who is stopped and receives a traffic ticket may also be stopped for investigatory purposes, patted down, searched, and/or arrested. If any of these situations occur, the “stop_type” field would be labeled “Ticket and Non-Ticket Stop.” If an individual is searched, MPD differentiates between person and property searches. Please note that the term property in this context refers to a person’s belongings and not a physical building. The “stop_location_block” field represents the block-level location of the stop and/or a street name. The age of the person being stopped is calculated based on the time between the person’s date of birth and the date of the stop.There are certain locations that have a high prevalence of non-ticket stops. These can be attributed to some centralized processing locations. Additionally, there is a time lag for data on some ticket stops as roughly 20 percent of tickets are handwritten. In these instances, the handwritten traffic tickets are delivered by MPD to the DMV, and then entered into data systems by DMV contractors.On August 1, 2021, MPD transitioned to a new version of its current records management system, Mark43 RMS.Beginning January 1, 2023, fields pertaining to the bureau, division, unit, and PSA (if applicable) of the officers involved in events where a stop was conducted were added to the dataset. MPD’s Records Management System (RMS) captures all members associated with the event but cannot isolate which officer (if multiple) conducted the stop itself. Assignments are captured by cross-referencing officers’ CAD ID with MPD’s Timesheet Manager Application. These fields reflect the assignment of the officer issuing the Notice of Infraction (NOIs) and/or the responding officer(s), assisting officer(s), and/or arresting officer(s) (if an investigative stop) as of the end of the two-week pay period for January 1 – June 30, 2023 and as of the date of the stop for July 1, 2023 and forward. The values are comma-separated if multiple officers were listed in the report.For Stop Type = Harbor and Stop Type = Ticket Only, the officer assignment information will be in the NOI_Officer fields. For Stop Type = Ticket and Non-Ticket the officer assignments will be in both NOI Officer (for the officer that issued the NOI) and RMS_Officer fields (for any other officer involved in the event, which may also be the officer who issued the NOI). For Stop Type = Non-Ticket, the officer assignment information will be in the RMS_Officer fields.Null values in officer assignment fields reflect either Reserve Corps members, who’s assignments are not captured in the Timesheet Manager Application, or members who separated from MPD between the time of the stop and the time of the data extraction.Finally, MPD is conducting on-going data audits on all data for thorough and complete information. Figures are subject to change due to delayed reporting, on-going data quality audits, and data improvement processes.

Content temporarily unavailable due to system update. Anticipated live date: 11/7/2025

Dataset contains information about moving traffic citations issued by the Memphis Police Department (MPD). Each row is a traffic citation issued by MPD. One ticket number can have multiple rows depending on how many violations were on the ticket. This dataset is includes both moving and non-moving citations.

Data is exported daily from MPD’s server by automated script. Data is subject to change. MPD makes every reasonable effort to ensure that the information is current and accurate at the time it is provided. This information is not to be used or construed as an official MPD report.

Traffic stop data is available in a different dataset on the Data Hub. Search the data catalog for “traffic stops”. Not all traffic stops result in a traffic citation.