Data
Traffic-counting-using-cameras

Traffic-counting-using-cameras

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Elif Ceren Gok
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Context These data come from a camera that is part of the Telraam device which makes counting cameras available to interested citizens. https://www.telraam.net/fr/what-is-telraam This camera is located, place gnral de gaulle, Paris (mouans sartoux, France) https://www.telraam.net/fr/location/9000000411 What exactly does the Telraam measure ? Telraam counts pedestrians, cyclists, cars and freight/heavy vehicles. This is done using images taken by the device camera and the analysis performed by the Raspberry Pi (a small computer on which the device is based). The analysis simply uses the size and speed of the passing object. Each hour the camera records the following data: Percentage of camera activity Counting of pedestrians, cars, bicycles, trucks (total, left and right of the street) Histogram of car speeds for the intervals [0-10 [[10-20 [[20- 30 [.. [70 and more [ Why a percentage of camera activity ? Telraam does not count when it is dark. This camera has constraints, because to perform the counts, it does image recognition: The camera is not active at night The camera is not active 100 of the time in daylight, therefore the percentage of activity is indicated. When the camera is partially active, the counts are prorated using this percentage to estimate the activity in the observed hour. The camera may be out of service for a period Content The data has been flatten in a csv file. The original data (json) can be retrieved in real time here with a POST : https://telraam-api.net/v0/reports/9000000411 The API Documentation: https://telraam.zendesk.com/hc/en-us/articles/360027325572-Want-more-data-Telraam-API Columns ['time','id','timezone','pctup','pedestrian','bike','car','lorry','pedestrianlft','bikelft','carlft','lorrylft','pedestrianrgt','bikergt','carrgt','lorryrgt','carspeed00','carspeed10','carspeed20','carspeed30','carspeed40','carspeed50','carspeed60','carspeed_70'] Inspiration Time series forecasting : predict traffic flow

24 features

2020-06-11_16:00:00+00string970 unique values
0 missing
9000000411numeric1 unique values
0 missing
Europe/Parisstring1 unique values
0 missing
0.09numeric443 unique values
0 missing
0numeric560 unique values
0 missing
0.1numeric607 unique values
0 missing
24numeric679 unique values
0 missing
0.2numeric461 unique values
0 missing
0.3numeric515 unique values
0 missing
24.1numeric651 unique values
0 missing
24.2numeric651 unique values
0 missing
0.4numeric392 unique values
0 missing
0.5numeric515 unique values
0 missing
0.6numeric560 unique values
0 missing
0.7numeric661 unique values
0 missing
0.8numeric445 unique values
0 missing
12numeric636 unique values
297 missing
12.1numeric637 unique values
299 missing
Unnamed:_18numeric637 unique values
292 missing
Unnamed:_19numeric582 unique values
303 missing
Unnamed:_20numeric483 unique values
358 missing
Unnamed:_21numeric401 unique values
445 missing
Unnamed:_22numeric318 unique values
530 missing
Unnamed:_23numeric425 unique values
404 missing

19 properties

970
Number of instances (rows) of the dataset.
24
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
2928
Number of missing values in the dataset.
625
Number of instances with at least one value missing.
22
Number of numeric attributes.
0
Number of nominal attributes.
0.02
Number of attributes divided by the number of instances.
91.67
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
64.43
Percentage of instances having missing values.
Average class difference between consecutive instances.
12.58
Percentage of missing values.

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