It's been 17 years since I've visited the city of Dublin, but I still have some very distinct impressions from my one and only visit.
I remember taking the ferry over to Dublin from the English coast. The waves were huge but the Guiness Stout I had on the ride over was the freshest I'd ever tasted.
I remember my first train ride from Dalkey, where I was staying, into the city center of Dublin, and I remember visiting Trinity University and checking out the "Book of Kells" (an illustrated manuscript gospel in Latin, which contains the four gospels of the New Testament and was believed to have been created around 800 AD!).
But what I won't soon forget was the traffic jam I encountered on the way to the airport when I was trying to leave that lovely country.
So I was glad to see this announcement today: That IBM is helping the City of Dublin use big-data to identify and solve the root causes of traffic congestion in its public transport network throughout the city, which will ultimately mean improved traffic flow and better mobility for commuters.
The Dublin City Council (DCC) delivers housing, water, and transport services to 1.2 million citizens across the Irish capital. To keep the city moving, the council's traffic control center works together with local transport operators to manage an extensive network of roads, tramways, and bus lanes.
In a collaboration with IBM researchers, its road and traffic department is now able to combine big-data streaming in from an array of sources -- bus timetables, inductive-loop traffic detectors, and closed-circuit television cameras, GPS updates that each of the city's 1,000 buses transmits every 20 seconds -- and build a digital map of the city overlaid with the real-time positions of Dublin's buses using stream computing and geospatial data.
Traffic controllers can now see the current status of the entire bus network at a glance and rapidly spot and drill down into a detailed visualization of areas of the network that are experiencing delay. These insights and the interface allow visualization of the data give them an opportunity to identify the cause of the delay as it is emerging and before it moves further downstream. This approach can accelerate the decision-making process to clear congestion more swiftly.
With improved reporting now in place, the data can help the city identify the optimal traffic-calming measures to reduce congestion. It can also help answer questions such as whether the bus line start times are correct or the best place to add additional bus lanes and bus-only traffic systems.
For example, using advanced analytics on data collected on each bus's journey showed that some buses were being passed on route by buses that departed at a later time during rush hour. Now, IBM researchers, the DCC, and city bus operators are working to pinpoint why the distance or time between buses, also know as headways, are diverging in this manner and what measures can be quickly put into action that will improve traffic flow at these specific peak times.
Based on the success of the use of data from the city's bus fleet, DCC and IBM Research are engaged in additional projects to look at how traffic control can be assisted and congestion eased in the city.
These projects include: integrating meteorological data into the traffic control centre so operators can take prescriptive actions to reduce extreme weather conditions impact on commuters and a predictive analytics solution combining data from the city's tram network with electronic docks for the city's free bicycle scheme.
This effort is part of a unique collaboration between IBM and Dublin City Council that began in 2010. As a part of Dublin's effort to becoming a leading Smarter City through its embrace of technology to stimulate economic activity and meet the challenges of a globally competitive city for the future, it shares data generated by city services, such as transportation and its operational expertise running the city with IBM researchers.
The IBM Research lab in Dublin focuses on cities and advancing science and technology for intelligent urban and environmental systems through big-data, analytics, and optimization.
Using big-data analytics techniques, the new Vehicle Awareness and Prediction feature, which is based on IBM InfoSphere Streams and developed by IBM Research, is now part of IBM Intelligent Operation Center's Intelligent Transportation solution. IBM's software solutions for cities draw on experience gained from Smarter Cities projects with cities around the world. IBM InfoSphere Streams software, part of IBM's big-data platform, can analyze and share data in motion, providing real-time decision making in environments where thousands of decisions can be made every second.
Go here to get more information on IBM Smarter Cities, and follow @IBMTransport and @IBMSmartCities to keep pace of some of the latest trends in smarter cities and transportation.
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