WhereOS and EEE Innovations Speed Up Data Science Solution Development for Automotive Data

Speeding Up Development and Deployment of Solutions for the Customers of EEE Innovations


EEE Innovations products and services featured in The Advancements TV series with Ted Danson View the full video.

EEE Innovations Oy develops E3 ForeC and E3 Sense products for passenger cars and heavy traffic to collect and refine data from CAN bus of the vehicles, such as accurate location, outdoor temperature information, unexpected braking situations, vehicle diagnostics information and CO2 emissions. The E3 solution produces information from vehicles to develop data driven products and services for drivers, fleet managers, authorities, road maintenance operators, insurance companies, vehicle computer manufacturers, self-driving cars and other road users.

The need of EEE Innovations is to create specific analyses, visualizations, dashboards and applications of the data they collect their customers’ fleets of vehicles. The goal was to speed up innovation work – how can we speed up the cycle of identifying customer needs and rapidly creating analyses, visualizations and applications that can be used for marketing and as a proof-of-value concepts for customers, to get more brand exposure and actual sales leads for EEE Innovations.

Cross Functional Kick Off Day

The process started with WhereOS and EEE Innovations hosting together a workshop day, with EEE cross-functional team with people from different backgrounds including data science, R&D, marketing, communications and management. The goal was to kick-start the development by identifying the value adding use cases from the customers and marketing point of view, and with the help of EEE Innovations & WhereOS technical teams rapidly develop the first applications to match the identified use cases.

The result of the kick off day was excellent both from EEE Innovations and WhereOS point of view: “It was amazing to see how fast our team picked up the idea of rapidly creating new kinds of data visualizations, that actually help us to sell our products & data better to our customers”, says Jarmo Leino, CEO of EEE Innovations. “I was impressed by the team at EEE Innovations, and how they could innovate new ideas and develop and deploy new applications just within one day, to create something new and interesting for customers”, says JP Partanen, CEO of WhereOS.

Geospatial Time-Series Data Development by EEE Innovations

After the kick off day, EEE Innovations continued the development with the lead of the EEE Innovations CTO Paula Silvonen: “Using WhereOS for creating value adding analyses and visualizations of geospatial time-series data from our EGRIP data was easy and definitely produced a productivity boost for our team”. EEE Innovations team continues to use WhereOS as a part of their data science tool-chain, and plans to extend their work to also participate in the WhereOS Ecosystem.

EEE Innovations Featured in the Advancements TV series and Expanding to Passenger Cars

The world-leading work of EEE Innovations was recognized by ForeC and EGRIP systems being featured in an episode of Advancements TV with Ted Danson on CNBC. Some of the time-series visualizations implemented with WhereOS are shown in the TV episode.

“We continue to work together to create new innovations together with WhereOS in this space – there’s more to come especially in the area of passenger cars so stay tuned”, says Jarmo Leino, CEO of EEE Innovations.

Jarmo Leino, CEO, EEE Innovations: jarmo.leino@e3inno.com,
More info: www.e3inno.com Twitter LinkedIn

JP Partanen, CEO, WhereOS: jp@eaglepeaks.com,
More info: www.whereos.com Twitter LinkedIn

RoadCloud and WhereOS Collaborate on Automotive Data Collection and Processing

Traffic Jams in Helsinki

Are there traffic jams in Helsinki? People living in Helsinki would say YES, and people living in central Europe would probably laugh at this. The traffic jams are relatively mild compared to many other places but there are clearly times and locations where the traffic slows down significantly.

RoadCloud and WhereOS have joined forces to collect, analyze and visualize data collected from the fleet of commercial vehicles through RoadCloud sensors. “It’s amazing how detailed information you can collect from road network conditions through RoadCloud sensors”, says JP Partanen, CEO of WhereOS.

In the series of articles, we explain how the data can be collected from vehicle fleets and processed into models, that can further be used to solve different business problems such as traffic conditions, road conditions, or even creating machine learning / artificial intelligence model to predict these conditions based on other external variables such as weather forecasts. In this article, we explain how we created a video visualization of traffic conditions & jams throughout the day. “We are impressed with WhereOS, and how it quick it was to turn our data into an insightful video. We are integrating our data API to WhereOS and making our anonymized vehicle data easily accessible”, says Ari Tuononen, CEO of RoadCloud.

Capturing Vehicle Flow Data with RoadCloud IoT Sensors

RoadCloud has equipped commercial vehicle fleet with RoadCloud IoT sensors to collect and monitor vehicle data and road conditions. The sensors are automatically collecting basic information such speed, heading and acceleration, but more importantly information about road surface conditions such as road friction, road state (dry/snow/ice/water), temperature, bumps and pot holes as a few examples. The data collection is taking place 24/7, as the commercial vehicles are continuously throughout the day and night.

The sensor data is uploaded to the RoadCloud data backbone, where it is stored for further use & analysis. The data can be processed as historical data, or as a real-time feed of continuous updating data.

Analyzing and Visualizing the Sensor Data with WhereOS

WhereOS connects to the RoadCload data backbone and can process the data further. In this visualization, the RoadCloud data is processed and combined with OpenStreetMap (OSM) street network data in several pipelines:

  1. OSM data preparation pipeline extracts the OpenStreetMap street network for the desired region, Helsinki capital area in this case, and splits the streets into segments of desired length (e.g. 200m).
  2. RoadCloud data extraction pipeline loads data from RoadCloud data backbone, and assigns joins the GPS (latitude & longitude) data points into corresponding street segments. Joining uses WhereOS geokey/geohash based operation for matching massive amount of geographical shapes – GPS points and polylines/linestrings (street segments) – together.
  3. NTILE pipeline takes the joined data, and approximates maximum speed for each street segment, by taking average of the 10% highest speeds driven on that specific street segment.
  4. Hourly traffic pipeline calculates average speed driven for each individual street segment for each hour, and also the speed decrease from the maximum speed.
  5. Rendering pipeline takes speed decrease for each street segment for each hour and produces an MP4 video where each frame represents traffic conditions (speed decrease) for each street segment for the given hour. Each street segment is colored so that red equals to high speed decrease (i.e. high traffic / traffic jam) and green means no decrease (i.e. traffic speed close to maximum).

WhereOS uses Spark and Hive as the execution engine for the pipelines. WhereOS pipelines can be created using SQL and R programming languages including built-in functions for ETL (extract, transform, load) operations, statistical analysis, machine learning and artificial intelligence (AI), geographical and geospatial analysis, data visualization etc.

Traffic Jam Video & Further Innovation

The data used for this video has been collected from the vehicles over the time of one full year (2018). You can see how the rush hour traffic affects the average speeds at different locations on different times of the day.

The RoadCloud data can be used for many other analyses such as: How the road conditions – friction, road state (dry/wet/ice/snow), temperature – affect the average speeds. Or how a speed bump or some other new traffic arrangement affects the traffic around it. In the upcoming articles we will dig deeper into the data and create new interesting visualizations out of it.

Ari Tuononen, CEO, RoadCloud: ari@roadcloud.com, +358 50 5604 702
More info: www.roadcloud.fi Twitter LinkedIn

JP Partanen, CEO, WhereOS: jp@eaglepeaks.com, +358 50 486 9257
More info: www.whereos.com Twitter LinkedIn