Traffic data Cars (infrared detectors) Hamburg

Description

Note in advance: Currently, the data of the counting points published here may not be complete and therefore not predictable. The reason for this is that at individual counting points in certain time periods under certain circumstances, e.g. in the event of failure or replacement of individual detectors, it is not guaranteed that the count field data is correctly aggregated in the data-holding compartment system of Teledyne FLIR. This also applies to historical data. The city side is working on a solution to this problem, hoping that the problems will be resolved as soon as possible, especially in Teledyne FLIR’s data-holding technical system. General information: The data set includes traffic data from all locations in Hamburg where motor vehicle traffic (car traffic) is recorded by means of infrared detectors at 24 hours a day and all the days of the year. The data contains real-time traffic strengths and is made available at counting points summarised for the road cross-section in 15 minutes, 60 minutes, day and week intervals. The data of the counting points are also visualised in the corresponding geoportals of the FHH, e.g. Geo-Online and Traffic Portal. In addition to real-time data, historical data are also available to the following extent: all data for the last two weeks in 15-minute intervals, all data for the last two months for the 60-minute intervals, all data for the current and last year at daily intervals, all data since the beginning of collection at weekly intervals. Information about the technology: The infrared detectors are usually installed on light-signalling systems, but also on other masts to a small extent. The detectors record and count traffic via the heat radiation of the individual traffic participants. Since only infrared images are evaluated, data protection is guaranteed at all times. Notes on data quality: The data is transferred in real time to FHH’s Urban Data Platform. Thus, these are available in a timely manner for all users and interested parties. Due to the real-time component, however, different framework conditions must be observed: The data is not fully quality-assured. Unusual deviations from the expected data and data gaps are automatically detected by the system, but cannot be corrected in real time. Gaps that occur, e.g. due to a demolition of data transmission, can still be subsequently returned. Under certain circumstances and in the event of prolonged failures, changes in the historical data can therefore be made after a few days. Therefore, the data will be updated daily for the following periods: Previous day: 15-min intervals 6 days ago: 15-min intervals and daily intervals Day ago 28 days: Daily intervals Weekly values are updated weekly for the previous week and the week before four weeks ago. The data published here are not officially verified data of the FHH. If such data are needed, e.g. the data set “Traffic Strengths Hamburg”, which contains the “average (work)daily traffic” in the development of recent years. As with any traffic count, whether automated or manual, there are certain tolerances in the measurement accuracy. The claim to the system used here is accuracy of ± 5 % for the detection of vehicle traffic strengths. For more information about the real-time service: The real-time data service contains the locations of the vehicle volume counting points recorded with infrared detectors. The data is provided in JSON format via the SensorThings API (STA). For each counting point in the SensorThings API (STA), an object was created in the entity “Thing”. For each temporal resolution level at the counting points or each traffic reference size, an object in the entity “Datastreams” stands. The real-time data on the number of cars per counting point and time interval is published in the STA in the entity “Observations”. The following spatial and temporal levels are differentiated: —Counting point 15 min, 1 hour, 1-day, 1-week: Number of cars All times are given in the Coordinated World Time (UTC). In the entity Datastreams, there are other “key-value pairs” in the JSON object under the “key” “properties”. Based on the service and layer structure in the GIS, we introduced service and layer as additional “key-value pairs” under the JSON object properties. Here is an example: { “properties”:{ “service name”: “HH_STA_Automated Traffic Volume Recording”, “layername”: “Number_Kfz_Zaehlstelle_15-Min”, “key”:“value”} } Available layers in the layerName are: * Number_Kfz_Zaehlstelle_15-Min * Number_Kfz_Zaehlstelle_1-hour * Number_Kfz_Zaehlstelle_1-Tag * Number_Kfz_Zaehlstelle_1-week With the help of these “key-value pairs” filters can be defined for the REST request, e.g. https://iot.hamburg.de/v1.1/Datastreams?$filter=properties/serviceName eq ‘HH_STA_Automated Traffic Volume Recording’ and properties/layerName eq ‘Number_Kfz_Zaehlstelle_15-Min’ The real-time data can also be obtained via an MQTT broker. The necessary IDs can be obtained via a REST request and then used for the subscription to a data stream: MQTT brokers: iot.hamburg.de Topic: v1.1/Datastream({id})/Observations

Resources

Name Format Description Link
0 https://iot.hamburg.de/v1.1/Things?$filter=Datastreams/properties/serviceName%20eq%20%27HH_STA_AutomatisierteVerkehrsmengenerfassung%27%20&$count=true&$expand=Locations
0 https://iot.hamburg.de/v1.1/Things?$filter=Datastreams/properties/serviceName%20eq%20%27HH_STA_AutomatisierteVerkehrsmengenerfassung%27%20and%20Datastreams/properties/layerName%20eq%20%27Anzahl_Kfz_Zaehlstelle_1-Stunde%27&$count=true&$expand=Datastreams($filter=properties/layerName%20eq%20%27Anzahl_Kfz_Zaehlstelle_1-Stunde%27;$expand=Observations($top=10;$orderby=phenomenonTime%20desc))
0 https://iot.hamburg.de/v1.1/Things?$filter=Datastreams/properties/serviceName%20eq%20%27HH_STA_AutomatisierteVerkehrsmengenerfassung%27&$count=true&$expand=Locations,Datastreams($expand=Observations($top=3;$orderby=phenomenonTime%20desc),Sensor,ObservedProperty)
0 https://iot.hamburg.de/v1.1/Things?$filter=Datastreams/properties/serviceName%20eq%20%27HH_STA_AutomatisierteVerkehrsmengenerfassung%27%20and%20Datastreams/properties/layerName%20eq%20%27Anzahl_Kfz_Zaehlstelle_15-Min%27&$count=true&$expand=Datastreams($filter=properties/layerName%20eq%20%27Anzahl_Kfz_Zaehlstelle_15-Min%27;$expand=Observations($top=10;$orderby=phenomenonTime%20desc))
0 https://iot.hamburg.de/v1.1/Things?$filter=Datastreams/properties/serviceName%20eq%20%27HH_STA_AutomatisierteVerkehrsmengenerfassung%27%20and%20Datastreams/properties/layerName%20eq%20%27Anzahl_Kfz_Zaehlstelle_1-Woche%27&$count=true&$expand=Datastreams($filter=properties/layerName%20eq%20%27Anzahl_Kfz_Zaehlstelle_1-Woche%27;$expand=Observations($top=10;$orderby=phenomenonTime%20desc))
0 https://iot.hamburg.de/v1.1/Things?$filter=Datastreams/properties/serviceName%20eq%20%27HH_STA_AutomatisierteVerkehrsmengenerfassung%27%20and%20Datastreams/properties/layerName%20eq%20%27Anzahl_Kfz_Zaehlstelle_1-Tag%27&$count=true&$expand=Datastreams($filter=properties/layerName%20eq%20%27Anzahl_Kfz_Zaehlstelle_1-Tag%27;$expand=Observations($top=10;$orderby=phenomenonTime%20desc))

Tags

  • infrarot
  • hmbtg
  • geodaten
  • wärmebildkamera
  • opendata
  • infrarotkamera
  • wärmebild
  • zählstellen
  • verkehrsdaten
  • belegungsgrad
  • tran
  • avme
  • verkehrsmengenerfassung
  • verkehrsmengen
  • kfz
  • infrarotdetektoren
  • kamera

Topics

  • TRAN

Categories