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

Formats

Json, XML, Protofub

openLR, datex2, NTCIP

CSV, excel, noSQL, SQL

Sources

Opensreetmap, Twitter, weather services, websites

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

Video feeds, country specific traffic sources for NL, UK


Standardization

Elements define a location in time and space

Dataobjects linked to elements


Industry standard techniques

Java8

OSGI

Jetty/Netty

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

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.
Modules

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.

    Projects

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    Madrid

    Fileradar wins Kapsch's Factory 1 startup accelerator to deliver PoC in Madrid. The team proves that the prediction data model works reliably in urban environments achieving 95% accuracy rate at short term traffic condition predictions.

    Amsterdam

    www.Future-Traffic.com
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    GoyLaan

    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.

    Utrecht

    utrecht.dantesoftware.com
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    AFM

    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.

    Rotterdam

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    Rhoon Traffic Management Centre

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

    Rhoon

    rhoon.dantesoftware.com
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    ADAPT

    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

    adapt.dantesoftware.com"
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    PPA-West

    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.

    Amsterdam

    ppa.dantesoftware.com
    b
    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.


    www.FileRadar.nl
    d
    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.