This is a collection of publicly available API call methods that can be used to access Telraam (https://telraam.net/) data, connected to our latest AWS server. Please read this section before using the API, we provide some basic information on its usage and on the interpretation of the data.
\nAPI endpoints with [Advanced API] in the title are only available for users with the Data subscription.
\nSome basic definitions
\nTelraam data: Telraam devices count road users passing in front of them. Telraam does not record images or movies of the road users, only count data (along with basic object properties such as size and duration of visibility in the case of Telraam V1 devices) is uploaded to the servers. Road users are classified into classes (either by the server, or directly by the edge devices, in the case of Telraam V1 and Telraam S2 data, respectively), and are aggregated into quarterly and hourly periods. Aggregate counts are available through the API.
\nCamera instance: A camera instance - or, as often referred to, simply an instance - is the concept of a physical Telraam device (with its mac/device id, that identifies the hardware), installed on one side of a distinct road segment (which identifies the location of the hardware), by a specific user (that identifies the person who owns or houses the hardware). One specific combination of a mac/device ID, a segment ID, and a user ID defines an instance, which is then identified by a specific instance ID. If any of these three properties changes (a new hardware is installed, the Telraam is moved to another segment, or simply it is transferred to a different user), that will mean the end of the original instance (ID) and the creation of a new instance (ID).
\nRoad segment: All Telraam units are installed on (a specific side of) a road segment - most often simply referred to as segments. These segments are defined typically between two corners of a street, so a longer street will most likely consist of multiple road segments. In practice, road segments are described by a set of geographical coordinate pairs, a chain of (or at least two) longitude-latitude pairs. The first coordinate pair defines the beginning of the road segment (point A on our mini maps on the Telraam website), and the last pair defines the end of the segment (point B on the Telraam website). Data on the Telraam website is always displayed on a segment level, but for example it is possible to have multiple instances on the same segment (two people on opposite sides of the road, or two neighbours installing a Telraam), then the average of these two instances will be taken as segment-level data.
\nTraffic modes - Telraam V1: Road users are classified into four categories based on their observed properties (see FAQ for a more detailed technical explanation): initially cars, two-wheelers (including mainly cyclists and motorbikes), and pedestrians, then in a second step, cars are divided into passenger cars and heavy vehicles (anything that is larger than a passenger car, including lorries, trucks, buses, etc.). The cut-off (in size) between cars and heavy vehicles is derived (automatically) only when enough data is available from a given instance, therefore new Telraam V1 units will not have heavy vehicles counts in the first week (or two) of their operation.
\nTraffic modes - Telraam S2: Telraam S2 devices classify the detected objects on the fly, so we do not need to do this as a post-processing step on the Telraam servers anymore. Actually object detection and classification happens at the same time now and it is done by an artificial intelligence (AI) chip. Our Telraam S2 sensor can differentiate between ten different road user categories: bicycle, bus, car, light truck, motorcycle, pedestrian, stroller, tractor, trailer, and truck. These are also aggregated into the four classical Telraam classes: pedestrians (pedestrians + strollers), two wheelers (bicycles and motorcycles), cars, and heavy vehicles (all the remaining classes) to make sure that data across different types of devices is still easy to compare and visualise using the legacy APIs and tools.
\nUptime: Due to the technical setup of the Telraam V1 units, they do not count objects 100% of the time, but spend ~20% of their time calculating a typical background this happens every few minutes and it defines how the street would look without cars moving on it), and depending on the amount of object that they observe, a few % of time doing processing on the data before it is uploaded to the servers. When we subtract the time that is not spent actively counting from the 100%, then we get the uptime. So if we assume 20% is spent on background calculation, and 5% on processing in a given hour, then the uptime for that hour will be 100% - (20% + 5%) = 75%, which is given as 0.75 in the API. The data provided through the API (an displayed on the website) is already corrected for this uptime, meaning that we provide the numbers that would have been counted if the camera had been actively counting 100% of the time. There are two important things to note here. First, when uptime is 0, then the camera was not actively counting, this happens, e.g., during night hours. Second, when for example sunrise is a 6:55, Telraam has only 5 minutes left in the hour to count, so even without time lost for background calculations and processing, the uptime would be below 0.1, therefore quite some uncertainty is introduced into the corrected data of that hour (for easy to understand statistical reasons). For this reason always keep an eye on the uptime values, and use them, e.g., as weights when calculating averages over multiple days, or as quality markers. A high 0.7-0.8 uptime will always mean very good data. The first and last daylight hour of the day will always have lower uptimes for the aforementioned reason, but if uptimes during the day are below 0.5, that is usually a clear sign that something is probably wrong with the instance. Uptime for Telraam S2 units is almost always 1.
\nUnderstanding traffic direction
\nOn the Telraam website we differentiate between traffic flowing in directions A->B and B->A on a given segment. In the API directions are given as _lft and _rgt. Below we will explain why the difference, and how users can convert between these two notations.
\nThe origin of _lft and _rgt - left and right direction on instance level: When someone buys a Telraam, they are interested in the traffic viewed from their window, so for them it makes sense to think in terms of traffic going left and right. This means that in instance level traffic data (provided by an instance level POST traffic API call) _lft actually means traffic moving to the left from the point of the view of the camera, and _rgt means traffic moving to the right. Instance level data also has a direction value (1 or 0, depending on which side of the street segment the Telraam is installed - see mode explanation below) in the data, but it is not relevant in terms of traffic flow from the point of the camera.
\nProblems with talking about left and right direction on street segment level: When thinking about traffic flowing along a street segment, left and right directions do not make sense anymore, because while on an West-East oriented street we could still talk about left and right flow directions, that is impossible to do on a North-south oriented street. Moreover what if there is a Telraam that is installed on the North side of a West-East oriented street (looking South), then what is traffic moving to the left from the point of view of the Telraam would be moving right from the point of the view of somebody who is looking at the map.
\nDirections on street segment level: To solve this we have chosen to represent street segments in our database with a virtual Telraam (instance) that is placed consistently on the direction = 1 side (that we also refer to as the True side) of a street segment. The data that is displayed for the street segments on the website, or given by the segment level POST traffic API calls, is data aggregated to these virtual cameras (hence all segment level API calls will return a direction = 1 flag). So for example if a given street segment has only one instance, and that is actually placed on the direction = 0 side of the street segment, then the _lft and _rgt traffic counts will be switched up between the instance level report and the segment level report, since for the segment level report we had to change these as the virtual camera that represent the segments is looking at the street from the opposite side of the road as the actual instance. Similarly if on a street segment there is only one instance, and it is installed on the direction = 1 side of the road, then the segment and instance level data will be the same for this instance and this segment, since the virtual camera representing the segment is placed on the same side of the road as the actual instance. This also makes it easy to deal with street segments that contain multiple instances, e.g., one instance on the direction = 1 and another on the direction = 0 side of the road, because when we aggregate (average) the instance level data to the virtual camera that represents the street segment, then we simply flip the _lft and _rgt values of the direction = 0 segment before we average the counts.
\nConversion from _lft/_rgt to A->B and B->A: To convert the segment level data from the traffic API onto directions that can be matched with a street on a map, use the following key:
\nTrue side (direction = 1)
A ----------------------- B
False side (direction = 0)**
A -> B traffic flow seen from the True side (direction = 1) is the _lft direction (traffic flows to the left from the camera’s point of view). Since segments are always represented by a direction = 1 virtual camera, this key is always valid for segment level data.** For instance level data, _lft and _rgt keep representing the traffic flow direction from the point of view of the actual instance, so if you want to convert that to the A->B notation, then if the instance has direction = 1 then the same conversion hold, if the direction = 0 then the conversion is the opposite (and _rgt becomes A->B on the corresponding segment).
\nAPI Token Required The Telraam v1 api (documentation version 1.2 and above) uses an API key (otherwise called an API token) that the client (for example Postman) needs to provide when making API calls. We use the \"X-Api-Key\", a custom header convention for passing your API key and its light form of authentication.
\nYou can create your personal token through your online Telraam dashboard. First register as a normal user on https://telraam.net/ - even if you do not intend to register a Telraam camera itself. Then click on the button next to your name in the bottom left, and select \"API Tokens\". Now in the upper right corner you can find \"Request new token\".
\nIt's possible to use multiple api tokens, eg for each project you're running you can create a token. A maximum of three tokens is allowed.
\nThe following limits apply to the usage of the API:
\n* Rate: 1 request per second
* Burst: 1 request
* Quota: 1000 requests per day (10000 for Advanced API users)
Coding is for nerds and you just need some data? The following Python snippet should teach you how to get a pandas data frame, or even just a plain text .csv file from our popular traffic API endpoint:
\nimport pandas as pd\nimport requests\nimport json\nurl = \"https://telraam-api.net/v1/reports/traffic\"\nbody = {\n \"id\":\"9000001463\",\n \"time_start\":\"2022-10-01 00:00:00Z\",\n \"time_end\":\"2022-10-02 00:00:00Z\",\n \"level\":\"segments\",\n \"format\":\"per-hour\"\n}\nheaders = {\n 'X-Api-Key': 'Your personal API key comes here'\n}\npayload = str(body)\nresponse = requests.request(\"POST\", url, headers=headers, data=payload)\njson = response.json()\ndataframe = pd.DataFrame(json['report'])\ndataframe.to_csv('test.csv')\n\nNote on legacy API Some of the v0 API (Up to documentation version 1.1) still works, but it is connected to an older (in not all aspects live) version of our database, therefore we strongly suggest using the v1. We have also made better versions for some of the original v1 APIs in the meantime, they are available parallel with their legacy versions, and we strongly recommend not using any API that has the legacy version tag in their name, as their support is not guaranteed.
\nFor any further questions please visit the Telraam FAQ page, especially the article about the API usage.
\n","schema":"https://schema.getpostman.com/json/collection/v2.0.0/collection.json","toc":[],"owner":"8210376","collectionId":"c7fa0b13-2fcd-4f56-abff-4d3775ac3ee7","publishedId":"TWDRqyaV","public":true,"customColor":{"top-bar":"FFFFFF","right-sidebar":"303030","highlight":"EF5B25"},"publishDate":"2021-02-11T13:50:54.000Z"},"item":[{"name":"welcome","id":"6c537788-60fa-4b5b-a349-c0384396220c","protocolProfileBehavior":{"disableBodyPruning":true},"request":{"method":"GET","header":[{"key":"X-Api-Key","value":"Your personal API Token comes here.","type":"text"}],"url":"https://telraam-api.net/v1","description":"This is a simple GET call that can be used to check if the Telraam API is alive and well.
This HTTP POST request method can be used to retrieve the observed traffic statistics for a given segment for a given time interval (maximum 3 months at a time). Parameters for the API call can be provided in the body portion of the call with the following keywords:
level: the main use case is \"segments\" (\"instance\" is another option), denoting that the statistics are calculated on segment (and not individual camera, a.k.a. \"instance\") levelformat: can only be \"hourly\", resulting in hourly aggregated trafficid: the segment (or instance) identifier in question (can be found in the address of the segment from the Telraam website, e.g.: https://telraam.net/nl/location/348917)time_start: The beginning of the requested time interval (UTC)time_end: The end of the requested time interval (UTC, note: the time interval is closed-open, so the end time is not included anymore in the request)The call returns a JSON data table, with the following columns:
\ninstance_id: the instance identifier for \"instance\" level calls (\"-1\" for \"segment\" level calls)
segment_id: the segment identifier for \"segment\" level calls (in the future when \"instance\" calls are implemented too, this will read \"-1\")
date: ISO timeflag (date and UTC time) of the reporting interval (beginning of the interval)
interval: can be \"hourly\" or \"daily\" for hourly or daily aggregate data, respectively
uptime: between 0 and 1, represents the portion of the reporting interval (hour or day) that was actively spent counting the traffic (background calculation intervals in hourly periods, and the night time in daily periods contribute to values being less than 1)
heavy: the number of heavy vehicles (called lorry in older APIs, but all stand for the same: anything larger than car) on this day (and in this hour)
car: the number of cars
bike: the number of two-wheelers (mainly cyclists and motorbikes)
pedestrian: the number of pedestrians
Then the counts for left and right directions are also reported separately, followed by
direction: \"1\" - disregard, this is an internal consistency value making sure that when multiple cameras on different sides of the street are aggregated then the left and right directions are handled properly
timezone: The name of the Time zone where the segment can be found, which can be used to convert reported UTC timestamps to local times
car_speed_hist_0to70plus: the estimated car speed distribution in 10 km/h bins from 0 to 70+ km/h (in percentage of the total 100%)
car_speed_hist_0to120plus: the estimated car speed distribution in 5 km/h bins from 0 to 120+ km/h (in percentage of the total 100%)
v85: the estimated car speed limit in km/h that 85% of all cars respect (15% of drivers drive faster than this limit). Just like all other speed related measurements, the accuracy of this value is likely not better than +/-10%.
The advanced version of our traffic API is exactly what the name suggests: a more feature-full version of the standard traffic API available under the Data subscription. *Currently on segment level the 10 modes are not yet available. This feature will be added in a future update.
This HTTP POST request method can be used to retrieve the observed traffic statistics for a given segment for a given time interval (maximum interval for a single call depends on the number of requested columns - see below). Parameters for the API call can be provided in the body portion of the call with the following keywords:
level: can be \"instances\" or \"segments\", depending on if the requested data is on the street segment, or instance level (a Telraam sensor is installed on a segment as a Telraam camera instance, but there can be multiple instances associated with a segment, in which case segment-level data is a smart average of the data coming from instances associated with the given segment, while instance level data is always just data coming from a single sensor).
format: can only be \"per-quarter\" or \"per-hour\", resulting in quarterly of hourly aggregated traffic, respectively
id: the segment or instance identifier in question.
time_start: the beginning of the requested time interval (UTC).
time_end: the end of the requested time interval (UTC, note: the time interval is closed-open, so the end time is not included anymore in the request).
columns: Optional. A list of requested columns. When not given, the Advanced API will return the same data as the standard API. If columns are specified, then instance_id or segment_id is compulsory (depending on your level specification of instances or segments), and car_speed_hist_0to120plus is required too if v85 is requested. Since using a lot of columns combined with a long time interval will push the request beyond the 6MB payload limit (and result in an error), we recommend using the \"columns\": \"instance_id,segment_id,date,interval,uptime,heavy,car,bike,pedestrian,heavy_lft,heavy_rgt,car_lft,car_rgt,bike_lft,bike_rgt,pedestrian_lft,pedestrian_rgt,direction,car_speed_hist_0to120plus,mode_bicycle_lft,mode_bicycle_rgt,mode_bus_lft,mode_bus_rgt,mode_car_lft,mode_car_rgt,mode_lighttruck_lft,mode_lighttruck_rgt,mode_motorcycle_lft,mode_motorcycle_rgt,mode_pedestrian_lft,mode_pedestrian_rgt,mode_stroller_lft,mode_stroller_rgt,mode_tractor_lft,mode_tractor_rgt,mode_trailer_lft,mode_trailer_rgt,mode_truck_lft,mode_truck_rgt,speed_hist_car_lft,speed_hist_car_rgt,v85\" set when interested in the broadest set of S2 data, and an even more limited set when interested in only, e.g, a subset of modes.
The call returns a JSON data table, with the specified columns. Beyond the columns already described under the standard traffic API, the following extra columns are available (fields with \"Telraam S2 specific\" in the description are only available for instances that are connected to Telraam S2 sensors):
\nxxx_lft: the number xxx in the left direction. xxx can be heavy, car, bike, and pedestrian.
xxx_rgt: the same as xxx_lft but in the opposite - right - direction.
mode_xxx_lft: (Telraam S2 specific) the number xxx in the left direction. xxx are the 10 modes provided by Telraam S2 sensors, they can be bicycle, bus, car, lighttruck, motorcycle, pedestrian, stroller, tractor, trailer, and truck.
mode_xxx_rgt: (Telraam S2 specific) the same as mode_xxx_lft but in the opposite - right - direction.
speed_hist_car_lft: (Telraam S2 specific) the same as car_speed_hist_0to120plus but only for cars in the left direction.
speed_hist_car_rgt: (Telraam S2 specific) the same as speed_hist_car_lft but in the opposite - right - direction.
brightness: (Telraam S2 specific) the average brightness in the field of view (FOV) of the Telraam S2, where 0 is black, and 1 is white. Typical values during the day are around 0.5, as that is the expected value for a well exposed image. Note: this value is calulcted for the whole FOV, and not for the region of interest (ROI) which can be much narrower inside the FOV, meaning that the actual brightness in the ROI can be different.
sharpness: (Telraam S2 specific) the average standard deviation of the pixel-by-pixel image brightness in the FOV. Homogeneous FOVs (grey road surface, empty fields, dark image at night) have low sharpness, while FOVs with a lot of detail and/or different contrast areas have a high sharpness. Low brightness + low sharpness FOVs at normal daytime are a clear sign that something was not right with the sensor, and the data is likely inaccurate.
period_start: (Telraam S2 specific) the start of the counting period (UTC)
period_duration: (Telraam S2 specific) the duration of the counting period (seconds)
device_id: (Telraam S2 specific) the device_id of the Telraam S2 device connected to the requested instance. It is given as -1 for segments and non Telraam S2 instances.
WARNING! The {\"time\":\"live\"} option will be removed from this API. Please use the new live traffic snapshot API call instead (which has the same functionality but it is much faster).
\nThis HTTP POST call returns a GeoJSON entity with the geometry of the segments as a MultiLineString (actually a single line string containing the coordinate pars that define the segment) and a limited set of properties (main category counts, v85 when contents are \"full\", and segment geometry information) for all active segments for a given (UTC) hour. Using this method you can make a (live) Telraam map.
When the live option is used extra information is provided and not only the active segments are returned, as it is detailed in the description of the option. Parameters for the API call can be provided in the body portion of the call with the following keywords, each with different options:
\ntime: There are two options, asking data from the past with a given epoch, or asking the most recent - live - data. 1) Specified in UT, e.g. \"2021-06-25 10:00:00Z\" will return data for that hour only. 2) Most recent data (\"live\") will result in the most recent aggregate info data if it is not older than 2 hours, plus extra information about segments that have been registered within the last week but are not live yet, plus about segments that have already been online at one point. De-registered segments are not included. So if there is no last_data_package entry (and object counts of course) in the response for a given segment, then it is a recently registered not yet active segment, and if there is no date (and object counts of course) then it is a segment that is currently offline (and of course the last_data_package will show since when). NOTE: if you want live data, it is better - and much faster - to use the live traffic snapshot API call instead, as this option will be phased out from this API.contents: This specifies if the v85 values are includeed or not, and it is important because including v85 requires and extra calculation step on the server side which makes the response slower, and when requesting the whole Terlaam network, it could also time out (see options below to make sure this does not happen). 1) Use \"minimal\" to not include v85 calculation. 2) Use \"full\" if you want v85 values too (note that this makes the API much slower, definitely only use if the query is limited in area - see below).area: Here you can define the geographical extent of your query using three options. 1) Use \"full\" to query all segments in the database. 2) Or if you want segments that have their centroid within a circle with a radious of R kilometers around a geographical coordinate, then use \"lon[degrees],lat[degrees],radius[km]\", e.g. \"4.44,51.19,1\" is segments within a 1 km radious circle around 4.44 East and 51.19 North. 3) Or if you want segments that fall within (any part of the segment, here we don't look at the centroid) an area specified by the diagonal coordinates of a bounding box (upper left and bottom right, or bottom left and upper right), then use \"lon_ul,lat_ul,lon_br_lat_br\", all given in degrees just like in the circle option.All combinations of the options within these three can be used.
\nThe call returns a GeoJSON, with the same columns as the traffic API, except it does not contain separately the traffic counts by direction, and it contains the geographical coordinates of each reported segment.
This HTTP GET call is the live version of the traffic snapshot API (see documentation there). The returned GeoJSON is compiled and cached on our servers every 5 minutes, meaning that this API performs much faster than the original live option under the traffic snapshot API.
This API is only available for Telraam users with a Data subscription. This HTTP POST call is the advanced version of the standard traffic snapshot API: it is linked to the 15 minute resolution quarterly traffic data instead of the hourly aggregates. Other than this difference, endpoint usage and options are identical.
*Since snapshot data is most meaningful on the segment level, this endpoint does not benefit from the 10 modes at this point. This feature will be added in a future update.
\n","urlObject":{"protocol":"https","path":["advanced","reports","traffic_snapshot"],"host":["telraam-api","net"],"query":[],"variable":[]}},"response":[{"id":"633cbf6a-6a1e-4bc0-8184-2f5618d907e5","name":"traffic snapshot","originalRequest":{"method":"POST","header":[{"key":"X-Api-Key","value":"Your personal API Token comes here.","type":"text"}],"body":{"mode":"raw","raw":"{\r\n \"time\":\"live\",\r\n \"contents\":\"minimal\",\r\n \"area\":\"4.01,50.92,4.07,50.96\"\r\n}\r\n","options":{"raw":{"language":"json"}}},"url":"https://telraam-api.net/v1/reports/traffic_snapshot"},"_postman_previewlanguage":"json","header":null,"cookie":[],"responseTime":null,"body":"{\n \"status_code\": 200,\n \"message\": \"ok\",\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.04451041920408,\n 50.9346197016993\n ],\n [\n 4.04468516398887,\n 50.9346499094883\n ],\n [\n 4.04476128017613,\n 50.9346736897766\n ],\n [\n 4.04486091154096,\n 50.9347048165154\n ],\n [\n 4.0450104190652,\n 50.9347897018035\n ],\n [\n 4.04508041942127,\n 50.9348297017355\n ],\n [\n 4.04536795581668,\n 50.9349798325647\n ],\n [\n 4.04561050090522,\n 50.9350979049717\n ],\n [\n 4.04563353497237,\n 50.9351073291995\n ],\n [\n 4.04581042002322,\n 50.9351797011571\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 426009,\n \"last_data_package\": \"2021-09-30 08:14:03.339300+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.750277777777778,\n \"heavy\": 26.6567937800814,\n \"car\": 277.230655312846,\n \"bike\": 33.3209922251018,\n \"pedestrian\": 3.99851906701222\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03531444355503,\n 50.9288772355736\n ],\n [\n 4.03545589688854,\n 50.9288770958734\n ],\n [\n 4.03548822908613,\n 50.9288780060799\n ],\n [\n 4.0354885139736,\n 50.9288780137901\n ],\n [\n 4.03551383872196,\n 50.928878726955\n ],\n [\n 4.03555341213731,\n 50.9288839731206\n ],\n [\n 4.03558193422525,\n 50.9288895464249\n ],\n [\n 4.03558256514116,\n 50.928889669603\n ],\n [\n 4.03560660929996,\n 50.9288947124406\n ],\n [\n 4.03563041985294,\n 50.92889970571\n ],\n [\n 4.03586148750961,\n 50.9289487610109\n ],\n [\n 4.03592191751754,\n 50.9289668461978\n ],\n [\n 4.03599283578826,\n 50.9289958719562\n ],\n [\n 4.03606910391476,\n 50.9290337071421\n ],\n [\n 4.03620257270986,\n 50.9291126356297\n ],\n [\n 4.03623210178816,\n 50.92913199573\n ],\n [\n 4.03628940925861,\n 50.9291820649194\n ],\n [\n 4.03632680101235,\n 50.9292122730082\n ],\n [\n 4.03636236240796,\n 50.9292354788664\n ],\n [\n 4.03650259331365,\n 50.9293189515686\n ],\n [\n 4.03671872304301,\n 50.9294394224488\n ],\n [\n 4.037607955555,\n 50.929890105631\n ],\n [\n 4.03764520085595,\n 50.9299073100572\n ],\n [\n 4.03774444343072,\n 50.9299398346743\n ],\n [\n 4.03782869261126,\n 50.9299791564778\n ],\n [\n 4.03796893158081,\n 50.9300453442153\n ],\n [\n 4.0382136208549,\n 50.9301621725093\n ],\n [\n 4.03844334569677,\n 50.930267182646\n ],\n [\n 4.03846799226049,\n 50.9302804433105\n ],\n [\n 4.03854000417825,\n 50.9303378074132\n ],\n [\n 4.0385669615121,\n 50.930352214247\n ],\n [\n 4.03899674922585,\n 50.9305535997551\n ],\n [\n 4.03915846719836,\n 50.9306075188147\n ],\n [\n 4.03973531666222,\n 50.9308823103882\n ],\n [\n 4.03988729358174,\n 50.930954706392\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 426887,\n \"last_data_package\": \"2021-09-30 08:13:53.031600+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.807777777777778,\n \"heavy\": 7.42778541953232,\n \"car\": 87.8954607977993,\n \"bike\": 22.283356258597,\n \"pedestrian\": 1.23796423658872\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03549290370565,\n 50.9268075442186\n ],\n [\n 4.03555968068931,\n 50.926649304721\n ],\n [\n 4.03556016768663,\n 50.9266481514296\n ],\n [\n 4.03565561381621,\n 50.926415212173\n ],\n [\n 4.03569249875684,\n 50.9262937436624\n ],\n [\n 4.03572171953873,\n 50.9261794881216\n ],\n [\n 4.03573604155765,\n 50.926104007932\n ],\n [\n 4.03576007443655,\n 50.9259700651626\n ],\n [\n 4.03580373915013,\n 50.9257040162304\n ],\n [\n 4.03583114097952,\n 50.9253532379842\n ],\n [\n 4.03586164554008,\n 50.9251207660756\n ],\n [\n 4.03589827231512,\n 50.9248917708619\n ],\n [\n 4.03593501629719,\n 50.924673431484\n ],\n [\n 4.03602329073822,\n 50.9240957192501\n ],\n [\n 4.036060257419,\n 50.9238901209125\n ],\n [\n 4.03609374315468,\n 50.9237040535175\n ],\n [\n 4.03612119172653,\n 50.9235446386602\n ],\n [\n 4.03615960328404,\n 50.9233118460048\n ],\n [\n 4.03617273569794,\n 50.9232271197955\n ],\n [\n 4.03619784267452,\n 50.9231033463747\n ],\n [\n 4.03622577794139,\n 50.9229930480672\n ],\n [\n 4.03624280187887,\n 50.9228898488942\n ],\n [\n 4.03628073700036,\n 50.9226060910706\n ],\n [\n 4.03629274338314,\n 50.9225119165908\n ],\n [\n 4.03631198397249,\n 50.9223609949454\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 426890,\n \"last_data_package\": \"2021-09-30 08:13:42.159200+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.725277777777778,\n \"heavy\": 52.3937188816546,\n \"car\": 209.574875526618,\n \"bike\": 68.9391037916508,\n \"pedestrian\": 8.27269245499808\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03629514269256,\n 50.9425506496903\n ],\n [\n 4.03626837873716,\n 50.9425592510871\n ],\n [\n 4.03624069579263,\n 50.9425655578145\n ],\n [\n 4.03620750162818,\n 50.9425687917316\n ],\n [\n 4.03619352186077,\n 50.9425689024146\n ],\n [\n 4.03594258456817,\n 50.942570882591\n ],\n [\n 4.03542539332699,\n 50.9425709744201\n ],\n [\n 4.03523080188705,\n 50.9425710086009\n ],\n [\n 4.03503267248585,\n 50.9425897964458\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 428588,\n \"last_data_package\": \"2021-09-30 08:13:20.359200+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.611666666666667,\n \"heavy\": 8.17438692098093,\n \"car\": 80.1089918256131,\n \"bike\": 71.9346049046322,\n \"pedestrian\": 91.5531335149864\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.02516583687611,\n 50.9425513404751\n ],\n [\n 4.02530828681596,\n 50.9424762954622\n ],\n [\n 4.0253940222125,\n 50.9424311280771\n ],\n [\n 4.02594299843017,\n 50.9421321106909\n ],\n [\n 4.02646152029551,\n 50.9418371197748\n ],\n [\n 4.02697396320853,\n 50.9415455798195\n ],\n [\n 4.02698803470365,\n 50.9415375742479\n ],\n [\n 4.02703138504327,\n 50.941514510095\n ],\n [\n 4.027134276801,\n 50.94145976671\n ],\n [\n 4.02719160323199,\n 50.9414292656207\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 428621,\n \"last_data_package\": \"2021-09-30 08:14:07.165500+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.725207596699882,\n \"heavy\": 27.0781099324976,\n \"car\": 159.691417550626,\n \"bike\": 138.167791706847,\n \"pedestrian\": 38.8813886210222\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03224919254457,\n 50.9416658001897\n ],\n [\n 4.03239338841046,\n 50.9417116812912\n ],\n [\n 4.03260469938496,\n 50.9417789174786\n ],\n [\n 4.03314957511101,\n 50.94194519328\n ],\n [\n 4.03377403998461,\n 50.9421226740524\n ],\n [\n 4.03488862458985,\n 50.942446821954\n ],\n [\n 4.03494342280564,\n 50.9424627582747\n ],\n [\n 4.03503267248585,\n 50.9425897964458\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 428630,\n \"last_data_package\": \"2021-09-30 08:13:53.843200+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.802222222222222,\n \"heavy\": 2.49307479224377,\n \"car\": 250.554016620498,\n \"bike\": 178.254847645429,\n \"pedestrian\": 14.9584487534626\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.02015455488541,\n 50.9367796710597\n ],\n [\n 4.02033593958703,\n 50.9368071018422\n ],\n [\n 4.02035076069,\n 50.9368093434207\n ],\n [\n 4.02051944931661,\n 50.9368386320305\n ],\n [\n 4.02064673638947,\n 50.9368605138772\n ],\n [\n 4.02086368447323,\n 50.9369005052309\n ],\n [\n 4.02107340776363,\n 50.9369381873772\n ],\n [\n 4.02134023733303,\n 50.9369897642138\n ],\n [\n 4.02156730718775,\n 50.9370320715995\n ],\n [\n 4.02165189901722,\n 50.9370501662691\n ],\n [\n 4.02171407299007,\n 50.9370640760914\n ],\n [\n 4.02204949762588,\n 50.9371428497745\n ],\n [\n 4.02231403130564,\n 50.9372113475674\n ],\n [\n 4.02272158488829,\n 50.9373292237154\n ],\n [\n 4.02295644188021,\n 50.9373962601232\n ],\n [\n 4.02311251919548,\n 50.9374424741382\n ],\n [\n 4.02314719700145,\n 50.937453557631\n ],\n [\n 4.02317823783809,\n 50.9374669179111\n ],\n [\n 4.02321358235769,\n 50.9374857816643\n ],\n [\n 4.02346672723586,\n 50.9376265381026\n ],\n [\n 4.02364646903886,\n 50.9377239241424\n ],\n [\n 4.02365506848306,\n 50.9377285835462\n ],\n [\n 4.02374083002328,\n 50.9377733030499\n ],\n [\n 4.02383818776174,\n 50.9378282672324\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 428760,\n \"last_data_package\": \"2021-09-30 08:13:37.411600+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.801944444444444,\n \"heavy\": 2.49393834430204,\n \"car\": 233.183235192241,\n \"bike\": 4.98787668860409,\n \"pedestrian\": 0\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.00970304089371,\n 50.9413696545002\n ],\n [\n 4.01011544413305,\n 50.9413566692705\n ],\n [\n 4.01038100291132,\n 50.9413483066308\n ],\n [\n 4.01150842455172,\n 50.9413064243979\n ],\n [\n 4.01158872577047,\n 50.9413034410303\n ],\n [\n 4.01170896148288,\n 50.9412991231459\n ],\n [\n 4.01288197537034,\n 50.9412569897204\n ],\n [\n 4.01305889256034,\n 50.9412456197609\n ],\n [\n 4.01324461651085,\n 50.9412196233127\n ],\n [\n 4.01369043111494,\n 50.9411497065003\n ],\n [\n 4.01437509517888,\n 50.9410380479159\n ],\n [\n 4.01460008240885,\n 50.940994768675\n ],\n [\n 4.01478595023681,\n 50.9409495395985\n ],\n [\n 4.01499511062201,\n 50.9408906438802\n ],\n [\n 4.01523334802913,\n 50.9408199305796\n ],\n [\n 4.01539681040396,\n 50.9407659781632\n ],\n [\n 4.01548305779413,\n 50.9407375112463\n ],\n [\n 4.0155972971735,\n 50.9407015719759\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 428773,\n \"last_data_package\": \"2021-09-30 08:13:43.785800+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.731944444444444,\n \"heavy\": 2.73244781783681,\n \"car\": 81.9734345351043,\n \"bike\": 36.888045540797,\n \"pedestrian\": 12.2960151802657\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.04949895508081,\n 50.9450732029646\n ],\n [\n 4.04955553155271,\n 50.9451430663707\n ],\n [\n 4.04956675890608,\n 50.9451569303052\n ],\n [\n 4.04958452472537,\n 50.9451788681271\n ],\n [\n 4.04986815697765,\n 50.9455454772653\n ],\n [\n 4.05005555665521,\n 50.945786631096\n ],\n [\n 4.05015235054362,\n 50.9459153785745\n ],\n [\n 4.05027512236911,\n 50.9460786784273\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 456513,\n \"last_data_package\": \"2021-09-30 08:14:01.321300+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.79,\n \"heavy\": 25.3164556962025,\n \"car\": 341.772151898736,\n \"bike\": 154.430379746835,\n \"pedestrian\": 34.1772151898734\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03527852778512,\n 50.9371017045637\n ],\n [\n 4.03540933187645,\n 50.9370121927632\n ],\n [\n 4.03555020766824,\n 50.9369294767982\n ],\n [\n 4.03557890275761,\n 50.9369127414034\n ],\n [\n 4.03581454365265,\n 50.9367753128046\n ],\n [\n 4.03594525310093,\n 50.9366991544521\n ],\n [\n 4.03613466695363,\n 50.9365887899011\n ],\n [\n 4.03618548515019,\n 50.9365591802119\n ],\n [\n 4.036577282298,\n 50.9363308820392\n ],\n [\n 4.0369234795683,\n 50.9361299508864\n ],\n [\n 4.03716457296212,\n 50.935989661789\n ],\n [\n 4.03744179148078,\n 50.9358284384323\n ],\n [\n 4.03751893854323,\n 50.9357850013682\n ],\n [\n 4.0375513312137,\n 50.9357667633756\n ],\n [\n 4.03759340866594,\n 50.9357499598427\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 508068,\n \"last_data_package\": \"2020-06-30 12:54:08.278900+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"\",\n \"period\": \"\",\n \"uptime\": \"\",\n \"heavy\": \"\",\n \"car\": \"\",\n \"bike\": \"\",\n \"pedestrian\": \"\"\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.04176266606168,\n 50.951679640223\n ],\n [\n 4.04184411220818,\n 50.9516313699631\n ],\n [\n 4.04187687493372,\n 50.951570813303\n ],\n [\n 4.04190482219798,\n 50.9515191618303\n ],\n [\n 4.04209083696084,\n 50.9511697978295\n ],\n [\n 4.04211712382042,\n 50.9511255535018\n ],\n [\n 4.0421625596447,\n 50.9510805683225\n ],\n [\n 4.04222873398112,\n 50.9510317865662\n ],\n [\n 4.04241512776431,\n 50.9509095028852\n ],\n [\n 4.04257902155479,\n 50.9508029611336\n ],\n [\n 4.04272233782001,\n 50.9507048820664\n ],\n [\n 4.0427543537139,\n 50.9506797850322\n ],\n [\n 4.04284362915457,\n 50.9506098042994\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 543175,\n \"last_data_package\": \"2021-09-30 08:13:58.257900+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.813333333333333,\n \"heavy\": 66.3934426229508,\n \"car\": 281.557377049179,\n \"bike\": 3.68852459016393,\n \"pedestrian\": 6.14754098360656\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03084909599802,\n 50.9324418024535\n ],\n [\n 4.030908895998,\n 50.9323290024535\n ],\n [\n 4.03091589599799,\n 50.9323158024535\n ],\n [\n 4.0311144959979,\n 50.9319472024536\n ],\n [\n 4.03162039599769,\n 50.931017902454\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000000077,\n \"last_data_package\": \"2020-04-15 11:05:38.022400+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"\",\n \"period\": \"\",\n \"uptime\": \"\",\n \"heavy\": \"\",\n \"car\": \"\",\n \"bike\": \"\",\n \"pedestrian\": \"\"\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.04366169599503,\n 50.9337551024545\n ],\n [\n 4.04188849599539,\n 50.9331148024545\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000000236,\n \"last_data_package\": \"2021-09-30 08:13:52.607600+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.790833333333333,\n \"heavy\": 5.05795574288725,\n \"car\": 39.1991570073762,\n \"bike\": 45.5216016859852,\n \"pedestrian\": 27.8187565858799\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.0246081959999,\n 50.9357433024519\n ],\n [\n 4.0258512959998,\n 50.9377593024514\n ],\n [\n 4.0258734959998,\n 50.9377895024514\n ],\n [\n 4.0259108959998,\n 50.9378404024514\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000000784,\n \"last_data_package\": \"2021-09-30 08:14:05.477800+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.790555555555556,\n \"heavy\": 21.5038650737878,\n \"car\": 136.612789880534,\n \"bike\": 40.4778636683064,\n \"pedestrian\": 10.1194659170766\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03045909599931,\n 50.9437727024502\n ],\n [\n 4.03107929599908,\n 50.9430926024504\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000001952,\n \"last_data_package\": \"2021-09-30 08:13:48.449200+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.803611111111111,\n \"heavy\": 2.48876598686485,\n \"car\": 46.0421707569997,\n \"bike\": 39.8202557898375,\n \"pedestrian\": 17.4213619080539\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.02733609599937,\n 50.9371538024517\n ],\n [\n 4.02714709599942,\n 50.9370933024517\n ],\n [\n 4.02684179599948,\n 50.9369844024517\n ],\n [\n 4.02657089599953,\n 50.9368590024517\n ],\n [\n 4.02639709599956,\n 50.9367283024517\n ],\n [\n 4.02632529599957,\n 50.9366599024518\n ],\n [\n 4.02625799599958,\n 50.9366142024518\n ],\n [\n 4.0261018959996,\n 50.9364557024518\n ],\n [\n 4.02605369599961,\n 50.9363803024518\n ],\n [\n 4.02577949599962,\n 50.9358945024519\n ],\n [\n 4.02576639599962,\n 50.9358368024519\n ],\n [\n 4.02560179599963,\n 50.935536302452\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000001979,\n \"last_data_package\": \"2021-09-30 08:13:48.747300+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.670555555555556,\n \"heavy\": 0,\n \"car\": 0,\n \"bike\": 0,\n \"pedestrian\": 0\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03502679599807,\n 50.942598902451\n ],\n [\n 4.03517369599804,\n 50.942692402451\n ],\n [\n 4.03588539599793,\n 50.9432843024509\n ],\n [\n 4.03658499599781,\n 50.9438348024508\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000001989,\n \"last_data_package\": \"2021-09-30 08:13:17.097700+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.798888888888889,\n \"heavy\": 10.0139082058414,\n \"car\": 207.78859527121,\n \"bike\": 88.8734353268429,\n \"pedestrian\": 41.307371349096\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03187949599823,\n 50.9368788024523\n ],\n [\n 4.03228639599815,\n 50.9369929024523\n ],\n [\n 4.03241579599812,\n 50.9370453024523\n ],\n [\n 4.032982195998,\n 50.9372328024523\n ],\n [\n 4.03314889599797,\n 50.9373132024523\n ],\n [\n 4.03331229599794,\n 50.9374018024524\n ],\n [\n 4.03344619599792,\n 50.9375176024523\n ],\n [\n 4.0335702959979,\n 50.9376416024523\n ],\n [\n 4.0336366959979,\n 50.9377391024523\n ],\n [\n 4.03369339599789,\n 50.9378510024522\n ],\n [\n 4.0337570959979,\n 50.9380062024522\n ],\n [\n 4.0337628959979,\n 50.9380863024522\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000001990,\n \"last_data_package\": \"2021-09-30 08:13:55.130000+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.786388888888889,\n \"heavy\": 6.35817732250088,\n \"car\": 20.3461674320028,\n \"bike\": 143.69480748852,\n \"pedestrian\": 38.1490639350053\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03803189599758,\n 50.9450137024507\n ],\n [\n 4.0379319959976,\n 50.9449391024507\n ],\n [\n 4.03789719599761,\n 50.9449120024507\n ],\n [\n 4.03765689599764,\n 50.9447044024507\n ],\n [\n 4.03753949599766,\n 50.9445659024507\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000001991,\n \"last_data_package\": \"2021-09-29 07:05:16.467600+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"\",\n \"period\": \"\",\n \"uptime\": \"\",\n \"heavy\": \"\",\n \"car\": \"\",\n \"bike\": \"\",\n \"pedestrian\": \"\"\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.05139449599364,\n 50.938505802454\n ],\n [\n 4.05119669599373,\n 50.9389578024538\n ],\n [\n 4.05111579599383,\n 50.9396842024536\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000001992,\n \"last_data_package\": \"2021-09-30 08:14:03.116000+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.803333333333333,\n \"heavy\": 19.9170124481328,\n \"car\": 129.460580912863,\n \"bike\": 28.6307053941909,\n \"pedestrian\": 11.2033195020747\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.02865429599893,\n 50.9359419024522\n ],\n [\n 4.02870619599892,\n 50.9359722024522\n ],\n [\n 4.02975699599869,\n 50.9362834024523\n ],\n [\n 4.02984879599867,\n 50.9362987024523\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000001993,\n \"last_data_package\": \"2021-09-30 08:13:52.940400+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.803611111111111,\n \"heavy\": 3.73314898029727,\n \"car\": 79.6405115796751,\n \"bike\": 93.3287245074318,\n \"pedestrian\": 12.4438299343242\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03107929599908,\n 50.9430926024504\n ],\n [\n 4.03173189599884,\n 50.9422952024508\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000001994,\n \"last_data_package\": \"2021-09-30 08:13:32.606600+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.804444444444444,\n \"heavy\": 4.97237569060773,\n \"car\": 45.9944751381216,\n \"bike\": 58.4254143646409,\n \"pedestrian\": 24.8618784530387\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03793869599719,\n 50.9410521024518\n ],\n [\n 4.03786269599721,\n 50.9410453024518\n ],\n [\n 4.03775909599723,\n 50.9410274024518\n ],\n [\n 4.03682509599745,\n 50.9409415024517\n ],\n [\n 4.03673999599748,\n 50.9409440024517\n ],\n [\n 4.03666519599749,\n 50.9409737024517\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000001996,\n \"last_data_package\": \"2021-09-30 08:14:05.353900+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.781944444444444,\n \"heavy\": 47.3179396092363,\n \"car\": 415.630550621669,\n \"bike\": 200.781527531083,\n \"pedestrian\": 121.492007104796\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03030439599913,\n 50.9417103024508\n ],\n [\n 4.03069489599898,\n 50.9412605024509\n ],\n [\n 4.03082919599889,\n 50.9406418024511\n ],\n [\n 4.03087749599887,\n 50.9405630024512\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000001998,\n \"last_data_package\": \"2021-09-30 08:13:56.847600+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.793611111111111,\n \"heavy\": 11.3405670283514,\n \"car\": 91.9845992299614,\n \"bike\": 80.6440322016101,\n \"pedestrian\": 40.322016100805\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.02174049600104,\n 50.9400339024503\n ],\n [\n 4.01967429600162,\n 50.9407400024499\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000001999,\n \"last_data_package\": \"2021-09-30 08:14:02.640800+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.707777777777778,\n \"heavy\": 8.47723704866562,\n \"car\": 67.8178963893249,\n \"bike\": 118.681318681319,\n \"pedestrian\": 50.8634222919937\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.02406009600081,\n 50.9431674024496\n ],\n [\n 4.02291439600116,\n 50.9438315024493\n ],\n [\n 4.02253759600128,\n 50.9441513024492\n ],\n [\n 4.02241559600133,\n 50.9442981024491\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002001,\n \"last_data_package\": \"2021-09-30 08:13:13.229900+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.722222222222222,\n \"heavy\": 45.6923076923077,\n \"car\": 184.153846153846,\n \"bike\": 31.8461538461538,\n \"pedestrian\": 6.92307692307692\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.02900759599928,\n 50.9400913024511\n ],\n [\n 4.03087749599887,\n 50.9405630024512\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002002,\n \"last_data_package\": \"2021-09-30 08:14:02.591100+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.769722222222222,\n \"heavy\": 19.4875496210754,\n \"car\": 135.11367737279,\n \"bike\": 187.080476362324,\n \"pedestrian\": 101.335258029592\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03933209599734,\n 50.9457441024506\n ],\n [\n 4.03935379599733,\n 50.9457090024506\n ],\n [\n 4.03936779599733,\n 50.9456863024506\n ],\n [\n 4.03939329599731,\n 50.9455672024506\n ],\n [\n 4.03941679599729,\n 50.9454932024507\n ],\n [\n 4.03985779599714,\n 50.9450086024509\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002003,\n \"last_data_package\": \"2021-08-24 13:03:53.986500+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"\",\n \"period\": \"\",\n \"uptime\": \"\",\n \"heavy\": \"\",\n \"car\": \"\",\n \"bike\": \"\",\n \"pedestrian\": \"\"\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.04082259599712,\n 50.9470504024504\n ],\n [\n 4.04081759599712,\n 50.9470793024504\n ],\n [\n 4.04054799599729,\n 50.9480778024501\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002005,\n \"last_data_package\": \"2021-09-30 08:13:50.945400+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.803611111111111,\n \"heavy\": 37.3314898029727,\n \"car\": 211.545108883512,\n \"bike\": 92.0843415139994,\n \"pedestrian\": 7.46629796059454\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.04054799599729,\n 50.9480778024501\n ],\n [\n 4.0405451959973,\n 50.94820340245\n ],\n [\n 4.04054759599731,\n 50.94825560245\n ],\n [\n 4.04055709599731,\n 50.94831930245\n ],\n [\n 4.04081729599734,\n 50.9491783024498\n ],\n [\n 4.04085009599734,\n 50.9492742024498\n ],\n [\n 4.04085269599735,\n 50.9493558024497\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002007,\n \"last_data_package\": \"2021-09-30 08:13:57.972800+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.801111111111111,\n \"heavy\": 24.9653259361997,\n \"car\": 188.488210818308,\n \"bike\": 94.868238557559,\n \"pedestrian\": 32.4549237170596\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03444469599847,\n 50.9450578024503\n ],\n [\n 4.03472829599836,\n 50.9447005024504\n ],\n [\n 4.03481309599833,\n 50.9445772024504\n ],\n [\n 4.03487539599829,\n 50.9444420024505\n ],\n [\n 4.03489629599828,\n 50.9443392024505\n ],\n [\n 4.03502679599807,\n 50.942598902451\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002009,\n \"last_data_package\": \"2021-09-30 08:13:58.072500+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.781111111111111,\n \"heavy\": 62.7311522048364,\n \"car\": 405.83214793741,\n \"bike\": 44.8079658605974,\n \"pedestrian\": 14.0825035561878\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.0343710959967,\n 50.9280397024551\n ],\n [\n 4.03432159599674,\n 50.9282836024551\n ],\n [\n 4.03406409599689,\n 50.9291492024548\n ],\n [\n 4.03358099599711,\n 50.9300910024544\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002011,\n \"last_data_package\": \"2021-09-30 08:13:43.455600+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.805,\n \"heavy\": 0,\n \"car\": 6.2111801242236,\n \"bike\": 58.3850931677019,\n \"pedestrian\": 16.1490683229814\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.04096269599559,\n 50.9328106024545\n ],\n [\n 4.0409136959956,\n 50.9327789024545\n ],\n [\n 4.04018139599575,\n 50.9325160024545\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002014,\n \"last_data_package\": \"2021-09-30 08:13:53.796200+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.7875,\n \"heavy\": 6.34920634920635,\n \"car\": 33.015873015873,\n \"bike\": 100.31746031746,\n \"pedestrian\": 72.3809523809524\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.0365301959967,\n 50.9330742024539\n ],\n [\n 4.03687729599669,\n 50.9337881024538\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002015,\n \"last_data_package\": \"2021-09-30 08:13:51.464500+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.783333333333333,\n \"heavy\": 29.3617021276596,\n \"car\": 301.276595744681,\n \"bike\": 257.872340425532,\n \"pedestrian\": 70.2127659574468\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.02221229600116,\n 50.9422367024497\n ],\n [\n 4.0225222960011,\n 50.9423971024497\n ],\n [\n 4.02329139600095,\n 50.9427813024497\n ],\n [\n 4.02406009600081,\n 50.9431674024496\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002023,\n \"last_data_package\": \"2021-09-30 08:14:11.813900+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.796666666666667,\n \"heavy\": 56.4853556485356,\n \"car\": 302.510460251045,\n \"bike\": 58.9958158995816,\n \"pedestrian\": 52.7196652719665\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03185149599902,\n 50.9443433024502\n ],\n [\n 4.03189389599902,\n 50.9443791024502\n ],\n [\n 4.03194889599901,\n 50.9444205024502\n ],\n [\n 4.03214139599897,\n 50.9444771024502\n ],\n [\n 4.03377009599862,\n 50.9449773024502\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002028,\n \"last_data_package\": \"2021-09-30 07:16:49.551100+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0,\n \"heavy\": 0,\n \"car\": 0,\n \"bike\": 0,\n \"pedestrian\": 0\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.02922799599903,\n 50.9382487024516\n ],\n [\n 4.02923279599904,\n 50.9383725024516\n ],\n [\n 4.02917739599907,\n 50.9385396024515\n ],\n [\n 4.0291172959991,\n 50.9386621024515\n ],\n [\n 4.02903649599913,\n 50.9387769024515\n ],\n [\n 4.0288194959992,\n 50.9389799024514\n ],\n [\n 4.02867949599925,\n 50.9390907024513\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002030,\n \"last_data_package\": \"2021-09-30 08:13:22.045100+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.778333333333333,\n \"heavy\": 3.85438972162741,\n \"car\": 39.8286937901499,\n \"bike\": 114.346895074947,\n \"pedestrian\": 44.9678800856531\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03505509599715,\n 50.9338986024535\n ],\n [\n 4.03409129599734,\n 50.9335165024535\n ],\n [\n 4.0323526959977,\n 50.9328788024535\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002045,\n \"last_data_package\": \"2021-09-30 08:14:00.493000+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.735833333333333,\n \"heavy\": 17.6670441676104,\n \"car\": 137.259343148358,\n \"bike\": 74.7451868629671,\n \"pedestrian\": 23.1030577576444\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.04409589599648,\n 50.9486245024503\n ],\n [\n 4.0445071959963,\n 50.9479195024506\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002048,\n \"last_data_package\": \"2021-09-30 08:13:10.128000+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.798611111111111,\n \"heavy\": 16.2782608695652,\n \"car\": 88.9043478260869,\n \"bike\": 13.7739130434783,\n \"pedestrian\": 0\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.04878889599487,\n 50.9442154024521\n ],\n [\n 4.04830909599493,\n 50.9436280024522\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002066,\n \"last_data_package\": \"2021-09-16 08:52:23.243400+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"\",\n \"period\": \"\",\n \"uptime\": \"\",\n \"heavy\": \"\",\n \"car\": \"\",\n \"bike\": \"\",\n \"pedestrian\": \"\"\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03502679599807,\n 50.942598902451\n ],\n [\n 4.03482759599812,\n 50.942640002451\n ],\n [\n 4.03370329599844,\n 50.9431283024507\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002076,\n \"last_data_package\": \"2021-09-30 08:13:43.898200+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.587222222222222,\n \"heavy\": 10.2175969725639,\n \"car\": 122.611163670766,\n \"bike\": 112.393566698202,\n \"pedestrian\": 1.70293282876064\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03856889599608,\n 50.9319489024545\n ],\n [\n 4.03645439599652,\n 50.9311822024544\n ],\n [\n 4.03597629599664,\n 50.9312081024544\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002107,\n \"last_data_package\": \"2021-09-30 08:12:59.564400+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.805555555555556,\n \"heavy\": 2.48275862068966,\n \"car\": 4.96551724137931,\n \"bike\": 6.20689655172414,\n \"pedestrian\": 9.93103448275862\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03411909599803,\n 50.9401378024516\n ],\n [\n 4.03406709599804,\n 50.9400903024516\n ],\n [\n 4.03401019599804,\n 50.9400321024517\n ],\n [\n 4.03375739599807,\n 50.9396912024517\n ],\n [\n 4.0334585959981,\n 50.9392930024518\n ],\n [\n 4.03337329599811,\n 50.9391721024518\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002129,\n \"last_data_package\": \"2021-09-30 08:14:09.497200+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.72,\n \"heavy\": 72.2222222222221,\n \"car\": 594.444444444449,\n \"bike\": 131.944444444444,\n \"pedestrian\": 95.8333333333333\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.03263829599824,\n 50.9386508024519\n ],\n [\n 4.03203379599846,\n 50.9394121024516\n ],\n [\n 4.03152819599865,\n 50.9400561024514\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002132,\n \"last_data_package\": \"2021-09-30 08:12:22.771700+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0,\n \"heavy\": 0,\n \"car\": 0,\n \"bike\": 0,\n \"pedestrian\": 0\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.027918096,\n 50.9445175024497\n ],\n [\n 4.0281769959999,\n 50.9440788024498\n ],\n [\n 4.02846379599979,\n 50.94371740245\n ],\n [\n 4.02874269599968,\n 50.9433998024501\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002165,\n \"last_data_package\": \"2021-09-12 17:56:04.831900+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"\",\n \"period\": \"\",\n \"uptime\": \"\",\n \"heavy\": \"\",\n \"car\": \"\",\n \"bike\": \"\",\n \"pedestrian\": \"\"\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.027918096,\n 50.9445175024497\n ],\n [\n 4.02573769600046,\n 50.9437693024497\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002282,\n \"last_data_package\": \"2021-09-30 07:48:04.386000+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.556111111111111,\n \"heavy\": 10.7892107892108,\n \"car\": 291.308691308691,\n \"bike\": 89.9100899100899,\n \"pedestrian\": 7.19280719280719\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 4.05612519599375,\n 50.9506273024511\n ],\n [\n 4.05541289599387,\n 50.9501119024511\n ]\n ]\n ]\n },\n \"properties\": {\n \"segment_id\": 9000002426,\n \"last_data_package\": \"2021-09-30 08:13:46.966500+00:00\",\n \"timezone\": \"Europe/Brussels\",\n \"date\": \"2021-09-30 06:00:00+00:00\",\n \"period\": \"hourly\",\n \"uptime\": 0.554444444444444,\n \"heavy\": 19.8396793587174,\n \"car\": 230.861723446894,\n \"bike\": 50.501002004008,\n \"pedestrian\": 9.01803607214429\n }\n }\n ]\n}"}],"_postman_id":"23269374-d1a7-4d40-93ec-1ab4e841678d"},{"name":"live quarterly traffic snapshot [Advanced API]","id":"b50c4c93-cb82-4862-8054-9e11af1a73e4","protocolProfileBehavior":{"disableBodyPruning":true},"request":{"method":"GET","header":[{"key":"X-Api-Key","type":"text","value":"Your personal API Token comes here."}],"url":"https://telraam-api.net/advanced/reports/traffic_snapshot_live","description":"This API is only available for users with a Data subscription. This HTTP GET call is the live version of the quarterly traffic snapshot API (see documentation there). The returned GeoJSON is compiled and cached on our servers every 5 minutes, meaning that this API performs much faster than the original live option under the quarterly traffic snapshot API.
This API is only available for users with a Data subscription.
\nThis HTTP POST request method can be used to retrieve the typical traffic of a given segment (or segments). *The API for now only works on segment level, since we only calculate segment level data. Instance level typical data and the 10 modes will be added in a future update.
Typical traffic is the foundation of many of the analytical tools and data products that we offer to our subscribers. As an example, typical data includes the typical (average) counts (and the most likely range) for the expected car traffic for a Monday morning between 08:00 and 08:15 based on the last 12 months' actual traffic - but it is available for all modes, days of the week, time of day, and baseline period. Note: typical traffic is calculated in local time, unlike aggregate traffic returned by other APIs, which is in UTC! How does this work in practice?
\nParameters for the API call can be provided in the body portion of the call in a similar manner to the traffic plus API, while the returned fields are also following a similar nomenclature:
level is always \"segments\" for now. In the future \"instances\" will be added as an option.id can be a single ID or a list of IDs - corresponding to the chosen segment(s), and when using a list it will return the individual typical values for all included segments (so it is not going to be an average of them).format can be \"per-quarter\" or \"per-hour\", returning quarterly or hourly typical values.duration can be \"year\", \"season\", or \"month\", which will return the typical traffic calculated for periods of 365 days, 91 days, and 30 days, respectively.latest_only can be true or false, when it is true, the API will return only the most recent typical traffic with calculation time (or more precisely, duration period end time, or simply epoch) inside the [time_start,time_end) interval. If it is false, it will return all typical traffic entries (with end_times inside the interval) from the interval. Since we wil be calculating typical at least traffic every two weeks, if your interval is 1 year, then you can get up to 24 (or more) historical typical traffic entries (e.g. 24 monthly entries calculated half a month apart). Of course right now you will get less entries, as we just started calculating typical-pro data, so it will take a year to create a time series of a year worth of typical traffic data.time_start and time_end define the range of the query, and it relates to the end_time (a.k.a. the epoch) variable of a typical calculation (in UTC). end_time (or epoch) of a typical calculation is the date where the duration of the typical interval terminates. So if a typical traffic is calculated from data from 20220101 to 20221231, then end_time of this typical is 20221231, so the time_start and time_end interval of the API needs to include this date for the typical in question to be returned. This parameter is also returned by the API.columns Defines the list of requested variables. Default - when columns are not specified - is all columns, but this is not recommended, as including for example the columns of the new 10 modes for the time being is pointless until we enable instance level typical calculations, and it will lead to an easier timeout of the API call. While a complete list of available columns is seen on the top, we included an example suggested use case with the most commonly used variables below. DO NOT EXCLUDE 'segment_id,instance_id,duration,interval,day_of_week,time,time_start,time_end' from the call, because they are needed for the API to work properly.An example call with the most used data columns:
\n{
\"level\": \"segments\",
\"id\": \"9000001463\",
\"format\": \"per-quarter\",
\"duration\": \"season\",
\"latest_only\": false,
\"time_start\": \"2023-06-15 00:00:00Z\",
\"time_end\": \"2024-01-01 00:00:00Z\",
\"columns\": \"segment_id,instance_id,duration,interval,day_of_week,time,time_start,time_end,avg_heavy,std_heavy,avg_car,std_car,avg_bike,std_bike,avg_pedestrian,std_pedestrian,avg_car_speed_hist_0to120plus,std_car_speed_hist_0to120plus,avg_v85,std_v85\"
}
An explanation of the returned data: while the returned data is very similar to the traffic plus API, there are some extra naming conventions and variables to familiarise yourself with:
interval denotes if the returned data is quarterly or hourly aggregate (as for times at the full hours that is not evident otherwise), options are \"hour\" and \"quarter\".day_of_week denotes which day of the week the typical traffic corresponds to. This ranges from \"mon\" (Monday) to \"sun\" (Sunday), while \"wday\" and \"wend\" denote the five weekdays and the two weekend days, which also have their separate general statistics. time denotes the local timestamp corresponding to the beginning of the hour or quarter of the typical traffic entry - it is very important to note that typical traffic is calculated in local time!time_start and time_end are the defining starting and ending dates and times in UTC (epoch) of the data range that has been used for the calculation of this typical traffic value.flag is always \"obs\", and means that this typical data is observed. In the future extrapolated or estimated values might be introduced, this is why a specific flag field exists.avg_heavy: the typical number of heavy vehicles (uptime weighted average).std_heavy: the observed standard deviation of heavy vehicles (uptime weighted).\n... followed by the same for all modes (aggregate and left and right directions separately) and other parameters (e.g., uptime) that are returned by the traffic plus API.samplesize: the number of individual aggregate hours or quarters contributing to the typical calculation. When duration is a year, this could be as high as 52.avg_car_speed_hist_0to70plus: the average speed histogram (sum is 100%).std_car_speed_hist_0to70plus: the observed standard deviation of the speed histogram.\n... followed by the same for the 0 to 120 km/h plus data.avg_v85: the 85th percentile speed calculated from the (0 to 120 plus) speed histogram.std_v85: the estimated standard deviation corresponding to the calculated avg_v85 from a Monte Carlo simulation using the avg_car_speed_hist_0to120plus and std_car_speed_hist_0to120plus arrays.This HTTP GET request method is meant to retrieve all available camera instances from the server. An instance is defined by the mac_id of the connected camera, the user_id of its owner, the segment_id of its location, and its direction relative to the road segment (this translates to the left or right side of the road), if any of these parameters changes - because for example the camera is being moved to another road segment -, then there will be a new instance created for this new situation, and the old instance will be closed by adding a time_end value to it.
instance_id: The unique identifier of the camera instance
mac: The unique identifier of the camera
user_id: The unique identifier of the user who has registered this camera
segment_id: The unique identifier of the street segment where the camera is installed
direction: The Boolean (false or true) that encodes the side of road (relative to the direction of the segment defined by its coordinate chain) on which the camera is installed
status: The status of the camera (active, sending good data / non_active, not sending data / problematic, active but not sending good data)
manual: Boolean (false or true) encoding some additional internally used information
time_added: The registration date and time of the instance (UTC)
time_end: null for active instances, or the date and time of the last active moment for the instance (UTC)
last_data_package: The date and time of the last transferred data packet, if available (UTC)
first_data_package: The date and time of the first transferred data packet, if available (UTC)
pedestrians_left: Boolean (true or false) that encodes if pedestrians are expected to be seen by the field of view of the camera in the left direction (user input) - the remaining values are similar for the other traffic modes.
is_calibration_done: A \"yes\" or \"no\" field that gives information about the status of the calibration process that is neccesary to distinguish heavy vehicles from cars. When the field is \"yes\", heavy vehicles will be counted separately, when it is \"no\", heavy vehicles are still counted as cars.
hardware_version: 1 represents a Telraam V1 (Raspberry Pi based original sensor), and 2 a Telraam S2 device. Occasionally a 0 or a null might be returned when this field has not been filled out properly. For now all sensors with a mac that start with a 2 are Telraam V1 units, and those that start with a 3 are Telraam S2 devices.
This HTTP GET request method retrieves all camera instances from the server that are associated with the given segment_id identifier. The returned parameters are the same as in the all available cameras API. Some identifiers will return a single entry, but some will have multiple entries, for example when there are multiple cameras on the same segment, or in case some property of the camera was changed resulting in a new instance, e.g., the camera was replaced (new mac_id), the direction of the camera was changed, etc In the latter case both archive and active instances will be returned.
This HTTP GET request method retrieves all camera instances from the server that are associated with the given mac_id identifier. The returned structure is the same as in the cameras by segment id call.
This HTTP GET request method is used to retrieve all road segments from the server in GeoJSON format (coordinate pair strings). The retreived data has the same structure as in the active segments API, except that only the oidn property is returned for each coordinate list, this identifier is used as segment_id in some other API calls. Also, the returned coordinates are in EPSG 31370 format.
We suggest using one of the traffic snapshot APIs instead, which return the more commonly used EPSG 4326 format (and other useful data).
\n","urlObject":{"protocol":"https","path":["v1","segments","all"],"host":["telraam-api","net"],"query":[],"variable":[]}},"response":[{"id":"2ed4b5dc-af99-48ef-8d4b-6ede4cc0cde5","name":"all segments","originalRequest":{"method":"GET","header":[{"key":"X-Api-Key","value":"Your personal API Token comes here.","type":"text"}],"body":{"mode":"urlencoded","urlencoded":[]},"url":"https://telraam-api.net/v1/segments/all"},"status":"OK","code":200,"_postman_previewlanguage":"json","header":[{"key":"Content-Type","value":"application/json"},{"key":"Content-Length","value":"909175"},{"key":"Connection","value":"keep-alive"},{"key":"Date","value":"Thu, 11 Feb 2021 18:06:06 GMT"},{"key":"x-amzn-RequestId","value":"b4f061c5-9311-43f9-81e2-dbaaa739a5e2"},{"key":"Access-Control-Allow-Origin","value":"*"},{"key":"x-amz-apigw-id","value":"al7BgHNEliAFZ9Q="},{"key":"X-Amzn-Trace-Id","value":"Root=1-60257209-1c03ad3e781c5e050a36c0fb;Sampled=0"},{"key":"X-Cache","value":"Miss from cloudfront"},{"key":"Via","value":"1.1 d78b645a0212e56f1a04609bf83554e4.cloudfront.net (CloudFront)"},{"key":"X-Amz-Cf-Pop","value":"BRU50-C1"},{"key":"X-Amz-Cf-Id","value":"DBGrDGMP4uYsi7tzbN6sirM_gUf7xB5UWwv6535fViPG_6jVlsreyg=="}],"cookie":[],"responseTime":null,"body":"{\n \"status_code\": 200,\n \"message\": \"ok\",\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 74377.1756373668,\n 166520.517694023\n ],\n [\n 74381.3268517003,\n 166528.256447544\n ],\n [\n 74422.1604468314,\n 166590.423685651\n ],\n [\n 74474.8381539995,\n 166677.162638661\n ],\n [\n 74569.0884186763,\n 166825.859581641\n ]\n ]\n ]\n },\n \"properties\": {\n \"oidn\": 9000000189\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 73963.2028362033,\n 167332.11607395\n ],\n [\n 73980.0087372602,\n 167346.334724784\n ],\n [\n 74009.6803940185,\n 167381.103947569\n ],\n [\n 74031.7985618011,\n 167405.870173926\n ],\n [\n 74043.1930296684,\n 167416.841126035\n ],\n [\n 74047.5790529116,\n 167420.370919583\n ],\n [\n 74056.5918668594,\n 167426.537014197\n ]\n ]\n ]\n },\n \"properties\": {\n \"oidn\": 9000000192\n }\n },\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiLineString\",\n \"coordinates\": [\n [\n [\n 73314.3707936761,\n 166844.562512203\n ],\n [\n 73316.7393662411,\n 166846.007470044\n ],\n [\n 73327.2186604027,\n 166852.061850125\n ],\n [\n 73342.8307213708,\n 166859.142497545\n ],\n [\n 73359.0839217298,\n 166866.202681984\n ]\n ]\n ]\n },\n \"properties\": {\n \"oidn\": 9000000179\n }\n }\n ]\n}"}],"_postman_id":"864d7e84-7c19-4591-b990-06dc8a1b191b"},{"name":"segment by id","id":"675af7fd-fe68-44c5-8e98-b9fd833423bc","protocolProfileBehavior":{"disableBodyPruning":true},"request":{"method":"GET","header":[{"key":"X-Api-Key","value":"Your personal API Token comes here.","type":"text"}],"body":{"mode":"urlencoded","urlencoded":[]},"url":"https://telraam-api.net/v1/segments/id/9000003481","description":"This HTTP GET method is similar to the cameras by segment id API, but it also returns the geographical data for the segment in question (see Example Response on the right), along with the list of properties fore each Telraam instance that is - or has ever been - installed on the segment.
For a given segment ID the call will return a JSON structure with a feature that contains the geometry (a MultiLineString of EPSG 4326 coordinate pairs defining the segment) and properties of the segment. The properties contain the following parameters:
\noidn: The segment ID
first_data_package: Timestamp (UTC) of the first count data
last_data_package: Timestamp (UTC) of the most recent count data
timezone: The timezone - TZ identifier - of the segment (which can be used to convert the timestamps of data collected on the segment to the local time of its location)
This is followed by a list of all instances associated with the segment, where for each instance ID the same fields that were explained under the all available cameras API are given.
WARNING! THIS IS A LEGACY VERSION THAT IS NOT SUPPORTED ANYMORE. Please use the new traffic snapshot API call instead.
\nThis HTTP GET request method is meant to retrieve basic geographic and traffic information for all road segments from the server which have a camera installed on them. (The result of this single call can be used to make the live traffic map that can be seen on the front page of Telraam.net.) For more accurate live traffic values for a selected segment we strongly suggest using the traffic API instead.
The call returns a GeoJSON Feature Collection, which is a set of Features (in this case, these Features are the Segments). Each Feature consists of a MultiLineString, which is a list of geographical coordinate pairs (WGS 84 / EPSG 4326) that define the given Segment, and a set of properties, that provide some information over the given Segment (mainly the traffic levels on it). The properties contain the following information:
\nid: The unique identifier of the street segment where the camera is installedpedestrian: Daily number of pedestrians the previous full calendar day (so not the last 24 hours, but yesterday)bike: Daily number of two-wheelers (mainly cyclists and motorbikes) the previous full calendar daycar: Daily number of the previous full calendar daylorry: Daily number of heavy vehicles the previous full calendar daylast_data_package: The date and time of the last received data packet (UTC)pedestrian_avg: Number of pedestrians the previous full hour (so not the past 60 minutes, but if now it is 12:30, then between 11 and 12)bike_avg: Number of two-wheelers (mainly cyclists and motorbikes) the previous full hourcar_avg: Number of cars the previous full hourlorry_avg: Number of heavy vehicles the previous full hourtypical_data: Typical (median) hourly traffic data (local time) for the segment.WARNING! THIS IS A LEGACY VERSION THAT IS NOT SUPPORTED ANYMORE. Please use the new traffic API call instead.
\nThis HTTP POST request method can be used to retrieve the observed traffic statistics for a given segment for a given (set of) hour(s). Parameters for the API call can be provided in the body portion of the call with the following keywords:
level: currently only \"segments\" work (in the future \"instance\" will be another option), denoting that the statistics are calculated on segment (and not individual camera instance, a.k.a. \"instance\") levelformat: can only be \"hourly\" for now, resulting in hourly aggregate traffictime_start: The beginning of the requested time interval (UTC, note: keep in mind when setting the start and end times that daily aggregates are produced with a timestamp of local midnight.)time_end: The end of the requested time interval (UTC)The call returns a JSON data table, with the following columns:
\nsegment_id: the segment identifier for \"segment\" level calls (in the future when \"instance\" calls are implemented too, this will read \"-1\")date: ISO timeflag (date and UTC time) of the reporting interval (beginning of the interval)pct_up: between 0 and 1, represents the portion of the reporting interval (hour or day) that was actively spent counting the traffic (background calculation intervals in ohurly periods, and the night time in daily periods contribute to values being less than 1)timezone: The name of the Time zone where the segment can be found, which can be used to convert reported UTC timestamps to local timespedestrian: the number of pedestriansbike: the number of two-wheelers (mainly cyclists and motorbikes)car: the number of carslorry: the number of heavy vehicles (called lorry in older APIs, but all stand for the same: anything larger than car) on this day (and in this hour)direction: \"1\" - disregard, this is an internal consistency value making sure that when multiple cameras on different sides of the street are aggregated then the left and right directions are handled properlycar_speed_histogram: the estimated car speed distribution in 10 km/h bins from 0 to 70+ km/h (in percentage of the total 100%), see note on car_speed_bucket to decode datacar_speed_bucket: the index values identifying the histogram bins for which data is reported in car_speed_histogram - buckets range from 0 to 70+ km/h (in percentage of the total 100%), bucket 0 being 0-10 km/h, bucket 1 being 10-20 km/h, etc.