Helsingin Bussiliikenne Oy (HelB) is a bus operator for the Helsinki metropolitan area of Finland. Each year, their 1,000 bus drivers and 430 buses serve more than 60 million passengers, drive 25 million kilometers and consume 12 million liters of fuel.
Operating in this highly competitive public transportation sector, HelB desired to increase cost efficiency, cut fuel consumption (for both environmental and financial reasons), and improve customer satisfaction. They knew that an effective way to help accomplish these objectives would be to reduce hard braking, fast acceleration and idling.
Working with CGI and Microsoft, HelB installed sensors and data collectors in all buses and linked that data to ERP data about drivers and routes. Large quantities of data were then visualized and analyzed on a map to identify areas for improvement. In the first analysis, it was clear there were big differences in the driving habits of drivers. The drivers were interested in the results as well, which caused a welcomed response: they started to compete to show the best behaviors.
Making the data available to the individual drivers also improved driving habits as a result of personal feedback. The results showed less hard braking, fewer major collisions, a drop in fuel consumption by 5%, improved driver work satisfaction and greater customer satisfaction as the rides became smoother. According to Michael Andersson, Technical Director of HelB, “Our ratings in customer satisfaction surveys have clearly improved since we got these tools.”
HelB has followed this success with a “More with Less” campaign offering incentives for their best and most-improved drivers. They are also implementing map technology to identify reasons for and parties in collisions, and to answer claims related to minor collisions, such as for broken side mirrors of parked cars and injury claims due to hard braking.
The map shown here allows a HelB officer to use visualization to investigate a claim for damaged property by checking for any incidents for the bus in question, with the ability to drill down by time and location and see data for speed, braking and more.
This is a great example of putting data to work and improving business value. Next steps could be online detection of driving habits for elevated risk and prevention of collisions, predicting the need for maintenance, or even identifying and preventing driving habits that cause the premature need for maintenance.
This combination of telematics, analysis and visibility could well be used to bring about major improvements in other transportation sectors such as taxi, courier, truck and rail service.
My CGI colleague Mika Lempinen and I are pleased to share these results with you.