Past, Present & Future Road Traffic Analytics.

Traffic Prediction

Big Data & Floating Car Data based predictions

Past Present & Future Road Traffic Analytics

Using our dynamic mapping backend we can assimilate any type of data and provide powerful and indepth road traffic analytics for past, present and future conditions.

Utrecht app

Just three steps: Data, Analytics and Dissemination

Flexible & Customizable data input.

Any kind of data source

Any type of data source can be assimilated. 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 a routine task. And more is on the way.

Past Present & Future Analytics

Trackers & Algorithms

A tracker is our construct to record any type of geo/spatial phenomena and we have applied it to congestion. We use trackers for regular (bottleneck) congestion, urban queues, shockwaves and incidents. We use databases of traces for our past analytics or as input to our prediction engine and always have trackers follow the current state of traffic on our networks.

Dissemination via HTML5 or API

Browser, API or mobile app

All information is made accessible via internet on our traffic browser just like Google maps but with a dynamic map that changes when you change the time slider. It is accessible on all html5 capable device, secure, build on standards such as leaflet, meteor and node js.

Off-the-shelf modules

Network Analytics

Providing network analytics to identify areas and probability of congestion. Our algorithms solve decision making and management

Automated Network Balancing (GNV)

We provide input for network balancing concepts that delay the onset of congestion and keep traffic flowing.

Congestion Trackers

For any network we have out of the box trackers to record all types of congestion and use it as input to other modules or API's.

Video Analytics

We use Convolutional Neural Networks to tranform video feeds of traffic into usable anonmyous traffic statistics.

Smartmicro Radar

A module for smartmicro's microwave radar input that determines level of urban congestion and potential build-up

Quick Data setup

Our quick data setup module allows us to quickly and scalably assimilate any type of datasource, index, store, monitor and retrieve it.

Adapt Traffic Operator Support

Decision support tool for Traffic Operators providing situtaional awareness and in depth traffic 360.

Travel Times API

An API for past, present or future travel times on AB routes or complex hypergraphs.

Traffic 360

Our module that provides a comprehensive 360 view of a tracked congestion instance for manual or automated use.

Engine 6 Predictions

The 6th generation of our hybrid prediction approach using decision trees and shockwave theory.

In App support

We use as a module in our browser to allow one-on-one communication with our end users for optimal service and feedback.

HTML5 Traffic Browser

Our traffic browser is highly server side configurable and allows us to interact with our end users via any html5 enabled device

Raw data influx
Active datasources
No. of Data source types
Number of trackers