Dante dynamic mapping solutions
Modular Dynamic GIS platform with HTML view
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DANTE Dynamic Mapping

Spatio-temporal GIS platform

Dante in brief
  • Modular GIS software platform
  • Time slider for temporal changes
  • Big data ready
  • Hierarchically organised spatial data
  • Fast configuration of new sources
  • Data type independent
  • Launch directly from the browser
  • Geographic web services hosting
  • Available through an API
Due to the modular approach, Dante is highly configurable during all three steps of development

Some examples of the variability of the model-view-controller, without aiming at completeness:

  • Data
  • Model
  • Control
  • View


Json, XML, Protofub

openLR, datex2, NTCIP

CSV, excel, noSQL, SQL


Opensreetmap, Twitter, weather services, websites

Propriatery software programs: shapefiles, autocad, vissim, matlab, etc

Video feeds, country specific traffic sources for NL, UK


Elements define a location in time and space

Dataobjects linked to elements

Industry standard techniques




Standardization of data sources as a first step

Link, nodes, segments, areas

Algorithms work wit standardised data

Traffic Algorithms

Queue tracking on motorways and cities

Shockwave tracker

Rich html5 app for browser and mobile

Interactive map with options to dinamically adjust time

Situational Awareness: focus only on extra-ordinary eventualities

Traffic 360, zooming-in/out adjusts datasource volume and intensity to reflect scope

Leveraging NodeJs, LeafletJs, Meteor



Fileradar wins two contracts
August 18, 2016

Fileradar won two contracts in the last phase of the CHARM PCP competition.

In the ADAPT project, Fileradar will develop a fully functional CHARM module called ADAPT, that fuses diverse data sources to provide traffic operators with maximum situational awareness and advanced information such as predictions. One of the data sources is BeMobile's Floating Car Data, which uses Flitsmeister as its primary data source in the Netherlands. In the DNOM_IV project, Fileradar cooperates with UK-based Mott MacDonald to develop a system that computes optimal responses to incidents for traffic operators.

The two highly innovative new modules will be integrated into Dynac, the new traffic operator software due to be launched in 2017. The modules will be demonstrated at the Traffic Management Innovation Centre in Helmond early 2017.

Unc Inc to make the Fileradar App
February 21, 2016

Today we can announce that we have selected Unc Inc to build the Fileradar App, the consumer app that will first launch in The Netherlands and that will revolutionize the way car travellers make decisions. We are working very hard to get our traffic predictions to the highest possible quality, and to finish the API that will feed the apps.

Setting up the Rotterdam trial has commenced
February 21, 2016

Today we started with setting up the field trial in Rotterdam in which we will compare three different detectors to estimate urban queues in real-time. The video camera is working now – next is the SmartMicro radar detector!

Fileradar tests SmartMicro radar
January 26, 2015

Vandaag is bekend geworden dat we de kans krijgen om in Rotterdam een SmartMicro radardetector te gaan testen. Daarnaast zullen we ook een videocamera ophangen om de data uit de radardetector en uit de detectielussen te kunnen valideren. De proef zal 6 weken duren en zal in het voorjaar van 2015 plaatsvinden.

Testing a SmartMicro radar detector
January 21, 2015

Today we can announce that we will be able to test a SmartMicro radar detector in Rotterdam. Along with the radar detector data, we will collect loop detector data and video images, so that we can compare 3 different methods of estimating queues. The trial will take about 6 weeks and will commence in the Spring of 2015.

PPA evaluated
November 7, 2015

Now that the ‘PPA‘ field trial has finished, we have extensively evaluated the results in cooperation with Arane. Based on the data, it can be concluded that the system in its current setup didn’t achieve positive results yet. However, we have identified several optimisations that can lead to significant improvement of the system. We have computed that a total of 150 vehicle hours of delay may be saved by the system for 1 single bottleneck. Hopefully we can verify these predictions in 2015, in the followup ‘PPA2′!

Beside the regular static representation of spatial data, Dante includes by default the time dimension and presents dynamically periodic changes, without hindering the rendering process and the user experience.

Dante is a modular server platform from the ground up written in Java using the OSGI standard. Currently there are well over a hundred modules that can be combined in different ways to achieve a broad range of functionality. Below we list some of the frequent combinations.

Assimilate any data source

Any type of data source can be assimilated in Dante. So far the following sources have been made accessible: open streetmap, openLR, loop detectors, bluetooth, floating car data, radar detector, video, weather imagery, structured feeds e.g. RSS, Datex2, unstructured data from twitter or websites. In general connecting to an API, accessing a known database or assimilating data directly from a collection of sensors is trivial.

Standardization of data: become source agnostic

All traffic observations and meta data are standardised in the so called D-State where D Stands for discretisation. The DState has a short term buffer of a few minutes where all data from all connected asynchronous sources can be placed correctly. At any point in time the DState tells us what the state currently is and recently was. The DSTate keeps track of the original data but also aggregates these into usable components such as: speed, travel time, density, flow, occupancy, blockage.

Tracking congestion phenomena

On top of DState we build ‘Trackers’. Trackers are advanced algorithms that track a specific traffic phenomenon. They are not data source dependant anymore but operate on the DState.

  • Shockwaves: detection and ultra short term prediction. Shockwaves are stop-and-go traffic jams that move upstream through the traffic network. Shockwaves are sometimes known to induce congestion where traffic is at a critical stage.
  • Motorway congestion: ‘regular’ congestion associated with a temporal/spatial capacity problem. During the peak hours at a certain (well known) bottleneck congestion starts. Vehicles start queuing until the peak demand falls below a certain threshold and the queue starts to dissolve.
  • Urban queue: The queue of waiting vehicles before a traffic light or urban intersection which have to wait one or more cycles before they can pass the traffic light..
  • Gridlock: The congestive condition when an urban network as a whole becomes congested..
  • Accidents: A special case of congestion is related to accidents. On average about 25% of traffic can be associated with accidents.
  • All trackers come with two qualities. Phenomena can be tracked at the link level or at the lane level. The difference is the lateral detail which is higher when tracked specifically per lane.
  • Congestion analytics

    Given the tracked phenomena over a longer range of time, we apply various algorithms to the data in order to gain more insight. We define ‘seed regions’ in the network where at a certain time and place the probability of regular congestion onset becomes significant. Similarly we can define the shock wave frequency at certain links in the network.

    Metadata Enrichment

    Given the tracked phenomenon, we enrich these by adding as much meta data as we can. The meta data considered may be some statistical properties, properties from structured sources like traffic operators or meta data automatically derived from unstructured data sources via twitter, or internet. The metadata itself is in turn used for different cases. It is widely regarded as very useful by traffic operators. And in our prediction engine it is used as a way to pre classify certain types of congestion before we start to predict them.

    Congestion Prediction

    Based on the combination of tracked traffic phenomenon, the short time knowledge of the recent past in DState and the enriched meta data we employ various algorithms to make a prediction. They are based on statistics, traffic flow theory, exact/discrete analytics, Bayesian data assimilation and sometimes black box models.

    TrafficBrowser360 (HTML5)

    Finally all information is made accessible via internet on our traffic browser. The traffic browser is accessible on all html5 capable devices and is build on standards such as leaflet, meteor and nodeJS. The traffic browser is simply put: a dynamic mapping application, just like Google maps but with dynamic data. The traffic browser offers highly 2way interactive user specific sessions where users can view the traffic state at any historic, recent or future time at any place. In the past 7 years we have learned how to visualize data in such a way it is kept understandable.


    Fileradar App

    The Fileradar app employs the expertise of Dantesoftware, providing routeplanning on the go. Thanks to our prediction engine, the app will consider the fastest route for the given time of day, weather conditions, latest news, etc and weighs each information appropriately.


    Dante is fed with TomTom data, video data and data from 5 radar detectors. In Dante all data is made accessible, and visible. The data is used to estimate motorway queues and urban queues.



    For the city of Utrecht a Dante setup is made using video, loop, 24 radar detectors, bluetooth, manual counts, and public transport data. For the first use case, the data is analyzed to estimate the effect on traffic flow in a before and after study.



    The Adaptive Queue Management system, or AFM-system, is a system that is being built for the Maastunnel, one of the most important links between the northern and southern parts of Rotterdam, the Netherlands. The goal of AFM is to ensure a safe usage of the Maastunnel as long as possible, while maintaining the level of service of the traffic network around the tunnel. To realise that goal, it is necessary to measure the real-time traffic situation on a large number of locations in Rotterdam.

    Fileradar is responsible for the monitoring of queues at over 40 intersections in Rotterdam using streaming radar data. The system will be realised and thoroughly tested between 2016-2018 and will have to meet the highest reliability standards, as the AFM system is primarily a safety system.


    Rhoon Traffic Management Centre

    For the Rhoon Traffic Management Centre we provide traffic flow predictions.



    In a combined project with Highways England And Rijkswaterstaat we developed a series of modules called ADAPT. In ADAPT we run a regular prediction engine but also continuously listen to unstructured data from twitter and internet and incorporate an event feed from Bemobile.

    Highways England


    From Images to Numbers (FIT#) project comprises the application of the open source SegNet convulational neural network tool. FIT# applies the analytical tool to quantify road use from deployed and existing CCTV cameras. Instead of relying on manual road user counts, the software package empowers city stakeholders to gain an objective, numerified analysis of road users - accounting for all modes of transport.


    The Bicyclize project merges the findings of FIT# and presents cycling and pedestrian traffic flow in the TrafficBrowser360. Bicyclize provides insights for city planners and stakeholders to make better decisions on insfrastructure appraisal.

    Dante Housing

    We took non-transport data to prove how easy to visualise the housing market in Greater London and predict future changes in housing prices.

    Dante Construct

    Development visualisation, safety assessment, project delivery estimation


    Contact us

    Fileradar BV
    Our location

    Overhoeksplein 2

    1031 KS, Amsterdam