Application Case 6.5 (Continued)
entire bus network at a glance. Because the interface is so intuitive, our operators can rapidly home in on emerging areas of traffic congestion, and then use CCTV to identify the causes of delays before they move further downstream.”
Taking Action to Ease Congestion
By enriching its data with GPS tracking, DCC can produce detailed reports on areas of the network where buses are frequently delayed, and take action to ease congestion. “The IBM Smarter Cities Technology Centre has provided us with a lot of valuable insights,” says O’Brien. “For example, the IBM team created trace reports on bus journeys, which showed that at rush hour, some buses were being overtaken by buses that set off later.
“Working with the city’s bus operators, we are looking at why the headways are diverging in that way, and what we can do to improve traffic flow at these peak times. Thanks to the work of the IBM team, we can now start answering questions such as: ‘Are the bus lane start times correct?’, and ‘Where do we need to add additional bus lanes and busonly traffic signals?’”
O’Brien continues: “Over the next two years, we are starting a project team for bus priority measures and road-infrastructure improvements. Without the ability to visualize our transport data, this would not have been possible.”
Planning For the Future
Based on the success of the traffic control project for the city’s bus fleet, DCC and IBM Research are working together to find ways to further augment traffic control in Dublin. “Our relationship with IBM is quite fluid–we offer them our expertise about how the city operates, and their researchers use that input to extract valuable insights from our Big Data,” says O’Brien. “Currently, the IBM team is working on ways to integrate data from rain and flood gauges into the traffic control solution–alerting controllers to potential hazards presented by extreme weather conditions, and allowing them to take timely action to reduce the impact on road users.”
In addition to meteorological data, IBM is investigating the possibility of incorporating data from the under-road sensor network to better understand the impact of private motor vehicles on traffic congestion.
The IBM team is also developing a predictive analytics solution combining data from the city’s tram network with electronic docks for the city’s free bicycle scheme. This project aims to optimize the distribution of the city’s free bicycles according to anticipated demand–ensuring that citizens can seamlessly continue their journey after stepping off a tram.
“Working with IBM Research has allowed us to take a fresh look at our transport strategy,” concludes O’Brien. “Thanks to the continuing work of the IBM team, we can see how our transport network is working as a whole–and develop innovative ways to improve it for Dublin’s citizens.”
1. Is there a strong case to make for large cities to use Big Data Analytics and related information technologies? Identify and discuss examples of what can be done with analytics beyond what is portrayed in this application case.
2. How can a big data analytics help ease the traffic problem in large cities?
3. What were the challenges Dublin City was facing; what were the proposed solution, initial results, and future plans?