Managing Roadways with MongoDB: How Edeva Powers Intelligent Traffic Systems with MongoDB and Iron.io

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Many of the Internet of Things’ applications are focused on unlocking and monitoring real-time insights from the wider world. Governments and private manufacturers have been implementing real-time monitoring tools for interstate roads, enabling governments to unlock deeper operational intelligence into traffic management, including insights on individual vehicles such as speed and vehicle type and external conditions including weather and congestion. The challenges of building these types of systems is capturing and tracking these dynamic data points gathered by sensors.

Background

Edeva is one of those businesses enabling real-time traffic management. A developer of intelligent traffic systems, Edeva manufactures a dynamic speed bump called Actibump, a road module that helps control vehicle speed. Actibump consists of one or more of these road modules that connect via a signal and transmit information to a central control system.

When Actibump is installed on a road, as it is in Linköping, Sweden, it measures a vehicle’s speed as it approaches the in-ground foundation. If the speed is within the legal limit, the Actibump stays uniform. If the car is traveling above the speed limit, the Actibump changes the configuration of road, creating a depression in the roads surface. The dynamic speed bump notifies the driver that they need to slow down, and in the three years since Edeva installed the Actibump in Sweden, the percentage of speeding vehicles has decreased from 70 to 20 percent. You can view the Actibump in action in this video.

Dynamic Data

With the opportunity to centrally collect and analyze traffic data in real time, Edeva sought a different toolkit for the job. Their overall goal was to enable their customers to unlock deeper operational intelligence into traffic management. The first iteration of their application would collect data on each vehicle, including maximum speed, average speed and vehicle type, but was designed with the intention to expand this in the future to include weather conditions, congestion levels and other metrics that would improve traffic flow and the safety of road users.

The team considered using MySQL as their central database, but quickly realized that their dynamic data requirements would not be supported by the relational data model. With the need to ingest highly variable and rapidly changing sensor data from their Actibump modules and run analytics against it in real time, the development team chose MongoDB and Iron.io’s IronMQ as core pieces within their infrastructure to collect and process real-time data and create more intelligent traffic systems. As such, Edeva can dynamically adjust measurements and configuration, while aggregating data to customer dashboards in real time. In addition, the engineering team can quickly evolve their application to meet new customer requirements.

John Eskilsson, the technical architect of the Actibump system, spoke with us about the combination of MongoDB and IronMQ:

We use MongoDB for capturing all the vehicle data. It is perfect for this since the various vehicle events we collect can send different types of data. Since MongoDB has a dynamic schema, we can easily change the data being sent from the bump and still be able to run analytical queries on the data set, without any extra work, and run queries on this new data instantly.

IronMQ has been very reliable so far and was extremely easy to implement. It was one of the primary reasons for why we selected IronMQ. We can take down the central server for maintenance and still rely on the data being gathered in IronMQ. When we start up the harvester again we can consume the queue in parallel using IronWorker and be back to real-time quickly. We can also perform statistical data calculations via IronWorker and MongoDB without putting any additional loads on our main app servers.

With this combination, we now have near real-time data. We have a system that can easily scale to take data from a large amount of speed bumps from the rest of the world. The ready availability and ease of use of MongoDB and IronMQ allowed us to get up and running and into production quickly. But it’s the flexibility, scalability and reliability of this architecture, and the companies behind the products, that give us confidence we’ll be able to handle the traffic loads we foresee.

Reducing Speeds on the Øresund Bridge

In December 2013, Edeva AB won a government procurement for variable speed impediments to the Øresund Bridge. The bridge is a double-track railway and dual carriageway bridge-tunnel across the Øresund strait between Sweden and Denmark.

Edeva’s combination of mechanical engineering with advanced data collection techniques and processing shows that the Internet of Things is within reach.

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For more information on Edeva AB and their intelligent traffic systems, visit their website.

See the full case study by Iron.io

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