1. Title: 1985 Auto Imports Database

2. Source Information: -- Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) -- Date: 19 May 1987 -- Sources: 1) 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook. 2) Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038 3) Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037

3. Past Usage: -- Kibler,~D., Aha,~D.~W., & Albert,~M. (1989). Instance-based prediction of real-valued attributes. {\it Computational Intelligence}, {\it 5}, 51--57. -- Predicted price of car using all numeric and Boolean attributes -- Method: an instance-based learning (IBL) algorithm derived from a localized k-nearest neighbor algorithm. Compared with a linear regression prediction...so all instances with missing attribute values were discarded. This resulted with a training set of 159 instances, which was also used as a test set (minus the actual instance during testing). -- Results: Percent Average Deviation Error of Prediction from Actual -- 11.84% for the IBL algorithm -- 14.12% for the resulting linear regression equation

4. Relevant Information: -- Description This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c) its normalized losses in use as compared to other cars. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process "symboling". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.

   The third factor is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/speciality, etc...), and represents the average loss per car per year.

   -- Note: Several of the attributes in the database could be used as a "class" attribute.

5. Number of Instances: 205

6. Number of Attributes: 26 total -- 15 continuous -- 1 integer -- 10 nominal

7. Attribute Information:
   Attribute: Attribute Range:

   1. symboling: -3, -2, -1, 0, 1, 2, 3.
   2. normalized-losses: continuous from 65 to 256.
   3. make: alfa-romero, audi, bmw, chevrolet, dodge, honda, isuzu, jaguar, mazda, mercedes-benz, mercury, mitsubishi, nissan, peugot, plymouth, porsche, renault, saab, subaru, toyota, volkswagen, volvo
   4. fuel-type: diesel, gas.
   5. aspiration: std, turbo.
   6. num-of-doors: four, two.
   7. body-style: hardtop, wagon, sedan, hatchback, convertible.
   8. drive-wheels: 4wd, fwd, rwd.
   9. engine-location: front, rear.
   10. wheel-base: continuous from 86.6 120.9.
   11. length: continuous from 141.1 to 208.1.
   12. width: continuous from 60.3 to 72.3.
   13. height: continuous from 47.8 to 59.8.
   14. curb-weight: continuous from 1488 to 4066.
   15. engine-type: dohc, dohcv, l, ohc, ohcf, ohcv, rotor.
   16. num-of-cylinders: eight, five, four, six, three, twelve, two.
   17. engine-size: continuous from 61 to 326.
   18. fuel-system: 1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi.
   19. bore: continuous from 2.54 to 3.94.
   20. stroke: continuous from 2.07 to 4.17.
   21. compression-ratio: continuous from 7 to 23.
   22. horsepower: continuous from 48 to 288.
   23. peak-rpm: continuous from 4150 to 6600.
   24. city-mpg: continuous from 13 to 49.
   25. highway-mpg: continuous from 16 to 54.
   26. price: continuous from 5118 to 45400.

8. Missing Attribute Values: (denoted by "?") Attribute #: Number of instances missing a value:

   1. 41
   2. 2
   3. 4
   4. 4
   5. 2
   6. 2
   7. 4

As described in https://data.cityofchicago.org/stories/s/311-Dataset-Changes-12-11-2018/d7nq-5g7t, the function of this dataset was replaced by https://data.cityofchicago.org/d/v6vf-nfxy. This dataset is historical-only. All open abandoned vehicle complaints made to 311 and all requests completed since January 1, 2011. A vehicle can be classified as abandoned if it meets one or more of the following criteria:All open abandoned vehicle complaints made to 311 and all requests completed since January 1, 2011. A vehicle can be classified as abandoned if it meets one or more of the following criteria: 1) On a public way in a state of disrepair as to be incapable of being driven in its present condition. 2) Has not been moved or used for more than seven consecutive days and is apparently deserted. 3) Has been left on the public way without state registration or a temporary state registration placard for two or more days. 4) Is a hazardous dilapidated vehicle left in full view of the general public, whether on public or private property. For some Open service requests, the vehicle has been towed but further action is required before the request may be closed. 311 sometimes receives duplicate abandoned vehicle complaints. If a vehicle is towed it remains as open, work in progress until it is redeemed, transferred or disposed of. The service request is not closed until there is a final disposition for the vehicle. Requests that have been labeled as Duplicates are in the same geographic area and have been entered into 311 Customer Service Requests (CSR) system at around the same time as a previous request. Duplicate reports/requests are labeled as such in the Status field, as either "Open - Dup" or "Completed - Dup." Data is updated daily.

This deprecated dataset provides data showing the number of vehicles (including cars, buses, trucks and motorcycles) that pass through each of the nine bridges and tunnels operated by the MTA each day. The data is now shared by the hour at https://data.ny.gov/d/qzve-kjga/

The dataset includes the number of vehicles on the road since 2004. The vehicles are divided into the following categories: * BUSES * TRUCKS FOR GOODS TRANSPORT * SPECIAL MOTOR VEHICLES - SPECIFIC * CARS * MOTOR TRUCKS AND QUAD CYCLES FOR GOODS TRANSPORT * MOTORCYCLES * MOTOR VEHICLES AND SPECIAL QUAD CYCLES - SPECIFIC * TRAILERS AND SEMI-TRAILERS SPECIAL - SPECIFIC * TRAILERS AND SEMI-TRAILERS FOR GOODS TRANSPORT * ROAD TRACTORS OR TRUCKS * OTHER VEHICLES The content of the dataset (owned by ACI and fully present on https://www.aci.it/laci/studi-e- searches/dati-e-statistiche/open-data.html) was taken from http://www.asr-lombardia.it/asrlomb/it/14022comuniparco-veicolare-circolante-categoria-comunale, in which the data for the year 2009. This elaboration of the dataset was released by the municipality of Milan.

This repository contains input data and results used in "Norway's electric vehicle revolution: Unveiling greenhouse gas emissions reductions and material use of passenger cars across space and time" by Lola S. A. Rousseau, Jan Sandstad Næss, Marine Lhuillier, Romain G. Billy, Peter Schön, and Edgar G. Hertwich.

The research article will soon be published.

The following is included in this repository:

• Data behind the figures of the article (source data) (data_behind_figures.zip)
• The code used to extract data from microdata.no (code_microdata.txt)
• The three datasets generated using extracted data from microdata.no and some manual adjustments (generated_microdata_datasets.zip):
  • (dataset 1) number and average curb weight of vehicles grouped by municipality and powertrain;
  • (dataset 2) number and average curb weight of vehicles grouped by size (for material composition in Section 2.2 in the article), powertrain and first registration year;
  • (dataset 3) number of vehicles grouped by size (for life cycle GHG emissions calculations in Section 2.3 in the article), powertrain, first registration year and size of municipality

The following data sources were processed and used to generate the figures (more details about the processing are provided in the SI of the article):

• Figure_1, Figure_3: microdata.no (dataset 2, dataset 3), archetypes from CIRCOMOD, carculator
• Figure_2: microdata.no (dataset 2, dataset 3), carculator
• Figure_4: left column (a, c, e) - microdata.no (dataset 1), carculator; right column (b, d, f) - Geodata Online, carculator
• Figure 5: Geodata Online, carculator
• Figure 6: Geodata Online, carculator, SSB

If there are questions about the data or the article, please contact Lola S. A. Rousseau (lola.s.a.rousseau(a)ntnu.no).

A. SUMMARY This dataset contains daily counts of warnings and citations issued by the SFMTA’s Automated Speed Enforcement program, broken down by camera. Some enforcement locations have two cameras to monitor traffic in both directions. To show which direction a camera is facing, a directional abbreviation is used—like NB for northbound, meaning traffic heading north. The dataset also includes the average speed of vehicles that received warnings or citations, as well as citation counts categorized by how much the vehicle exceeded the speed limit: 11–15 mph over 16–20 mph over 21+ mph over For more information about the program, visit SFMTA.com/SpeedCameras. B. HOW THE DATASET IS CREATED Data is collected through SFMTA's Automated Speed Enforcement Program. C. UPDATE PROCESS We will update this data set once a month. D. HOW TO USE THIS DATASET You can filter warnings issued by day, site/location, number of warnings issued, posted speed limit, average speed, and speed distribution bucket.

This dataset contains the results of traffic engineering studies conducted on streets in Norfolk. Residents can apply for a speed hump or speed table to be installed via MyNorfolk. Upon receipt of the request, the Department of Transportation schedules a traffic engineering study to determine the level of speeding and traffic volume. If the prevailing 85th percentile speed is at least 8 miles per hour above the posted speed limit, then a street segment qualifies for a speed hump if the traffic volume is between 500-3,000 vehicles per day, or a speed table if the volume is between 1,000-4,000 vehicles per day. These qualifiers do not guarantee a speed hump/table but are the initial qualifiers to move forward in the process. There are several more steps required to complete the process of installing a speed hump or speed table in Norfolk after the traffic engineering study is completed.

This dataset the includes the location of traffic engineering studies, study start and end dates, traffic volume, vehicle class and axel volume, average and percentile speeds observed, and the number of vehicles traveling above the speed limit. The dataset will be updated ad-hoc as new speed studies are conducted and the results are provided.

The Motor Vehicle Collisions crash table contains details on the crash event. Each row represents a crash event. The Motor Vehicle Collisions data tables contain information from all police reported motor vehicle collisions in NYC. The police report (MV104-AN) is required to be filled out for collisions where someone is injured or killed, or where there is at least $1000 worth of damage (https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ny_overlay_mv-104an_rev05_2004.pdf). It should be noted that the data is preliminary and subject to change when the MV-104AN forms are amended based on revised crash details.For the most accurate, up to date statistics on traffic fatalities, please refer to the NYPD Motor Vehicle Collisions page (updated weekly) or Vision Zero View (updated monthly).

Due to success of the CompStat program, NYPD began to ask how to apply the CompStat principles to other problems. Other than homicides, the fatal incidents with which police have the most contact with the public are fatal traffic collisions. Therefore in April 1998, the Department implemented TrafficStat, which uses the CompStat model to work towards improving traffic safety. Police officers complete form MV-104AN for all vehicle collisions. The MV-104AN is a New York State form that has all of the details of a traffic collision. Before implementing Trafficstat, there was no uniform traffic safety data collection procedure for all of the NYPD precincts. Therefore, the Police Department implemented the Traffic Accident Management System (TAMS) in July 1999 in order to collect traffic data in a uniform method across the City. TAMS required the precincts manually enter a few selected MV-104AN fields to collect very basic intersection traffic crash statistics which included the number of accidents, injuries and fatalities. As the years progressed, there grew a need for additional traffic data so that more detailed analyses could be conducted. The Citywide traffic safety initiative, Vision Zero started in the year 2014. Vision Zero further emphasized the need for the collection of more traffic data in order to work towards the Vision Zero goal, which is to eliminate traffic fatalities. Therefore, the Department in March 2016 replaced the TAMS with the new Finest Online Records Management System (FORMS). FORMS enables the police officers to electronically, using a Department cellphone or computer, enter all of the MV-104AN data fields and stores all of the MV-104AN data fields in the Department’s crime data warehouse. Since all of the MV-104AN data fields are now stored for each traffic collision, detailed traffic safety analyses can be conducted as applicable

Please be advised that there are issues with the Small Area boundary dataset generalised to 20m which affect Small Area 268014010 in Ballygall D, Dublin City. The Small Area boundary dataset generalised to 20m is in the process of being revised and the updated datasets will be available as soon as the boundaries are amended.This feature layer was created using Census 2016 data produced by the Central Statistics Office (CSO) and Small Areas national boundary data (generalised to 20m) produced by Tailte Éireann. The layer represents Census 2016 theme 15.1, number of households with cars. Attributes include a breakdown of households by number of cars owned (e.g. 1 motor car, 2 motor cars). Census 2016 theme 15 represents PC and Internet Access. The Census is carried out every five years by the CSO to determine an account of every person in Ireland. The results provide information on a range of themes, such as, population, housing and education. The data were sourced from the CSO. The Small Area Boundaries were created with the following credentials. National boundary dataset. Consistent sub-divisions of an ED. Created not to cross some natural features. Defined area with a minimum number of GeoDirectory building address points. Defined area initially created with minimum of 65 – approx. average of around 90 residential address points. Generated using two bespoke algorithms which incorporated the ED and Townland boundaries, ortho-photography, large scale vector data and GeoDirectory data. Before the 2011 census they were split in relation to motorways and dual carriageways. After the census some boundaries were merged and other divided to maintain privacy of the residential area occupants. They are available as generalised and non generalised boundary sets.

This deprecated dataset provides data showing the number of vehicles (including cars, buses, trucks and motorcycles) that pass through each of the bridges and tunnels operated by the MTA each hour of the day. For a more detailed look traffic, refer to dataset https://data.ny.gov/d/ebfx-2m7v/.

Certain types of Public Passenger Vehicles licensed by the City of Chicago must be inspected as part of the license renewal or change of vehicle associated with the license. This dataset shows the schedule of upcoming appointments, as well as some past appointments.

For additional information about Public Passenger Vehicle licensing, please see https://www.chicago.gov/city/en/depts/bacp/provdrs/vehic.html.

For a list of Public Passenger Vehicle Licenses, please see https://data.cityofchicago.org/d/tfm3-3j95.

For any questions about appointments, including requests to reschedule, please e-mail BACPPV@cityofchicago.org.

This deprecated dataset provides data showing the number of vehicles (including cars, buses, trucks and motorcycles) using E-ZPass that pass through each of the nine bridges and tunnels operated by the MTA each day. The data is now shared by the hour at https://data.ny.gov/d/qzve-kjga/

A dataset of car tax calculations for company cars by operating cycle, manufacturer, model, and derivative.

PPS-D is a comprehensive synthetic image and video dataset designed explicitly for the protection of public
spaces. This dataset is compiled from a large number of video surveillance cameras that monitor different
environments such as streets, squares, parks, railway stations, airports, car parks, shopping centres and
industrial areas, as well as different weather and time conditions such as sun, clouds, night, rain, snow, etc.
Each camera is strategically positioned to provide a realistic view of the most important areas in each
scenario. All the environments and actors, including people, vehicles and objects, are entirely synthetic and
created from scratch. The generated individuals are not based on any real or specific person.
PPS-D emphasises object and event detection, with various simulations representing different events, all
linked to human behaviour. The dataset includes scenarios ranging from everyday normality to more
complex situations, such as abandoned objects, increasing crowd density and sudden panic caused by
pedestrian or vehicle incidents. The diverse settings and realistic camera perspectives are designed to
enhance algorithms for public safety and surveillance applications