The Traffic Prediction Project is setting out to crunch all traffic data, including historical numbers where available, from transport departments, road cameras, Microsoft’s Bing traffic maps, and even drivers’ social networks, to see if established patterns can help foresee traffic jams from 15-60 minutes before they happen.
Big data is increasingly being used to analyze global problems to find solutions; this extends into the medical realm too, where it’s being used to discover new drugs and even combat health care fraud. So beating traffic jams is just one of many real-world issues that can be tackled by combining lots of data from multiple sources.
While there are a growing number of tools and online services that can show drivers congestion hotspots in real time, including Google Maps, it’s often too late given that a driver may well be approaching the bottleneck already. Being able to accurately predict jams before they happen has yet to bear much fruit, though many companies have been working on solutions.
In 2014, it’s estimated that 54 percent of the planet’s people lived in cities, up from 34 percent in 1960. This is expected to grow at almost 1.84 percent a year until 2020, then 1.63 percent until 2025. The growing urbanization of the world’s population means that whoever cracks the traffic jam-prediction nut will be onto something lucrative, with drivers able to take alternative routes, use public transport, or simply stay at home.