Transportation analytics in the era of big data: mapping the potential of IoT rail, air, car and truck
Some of the quickest wins using IoT and big data could come via edge computing. For example, smart cameras could employ video analytics. That information, coupled with the data provided by roadway sensors could help enable near-real time changes to signaling, potentially leading to improvements in traffic conditions, intersection safety and pedestrian safety.
At the airport, the IoT could manifest itself in smart gates that use computer vision and transportation analytics to streamline passenger boarding or enhance traveler safety. Sensors and digital signage deployed at bus stations could give passengers greater insight into when the next bus is arriving and reduce wait times.
Edge computing and the IoT can also provide an essential foundation for autonomous or self-driving vehicles by improving response times to unexpected changes on the road.
Meanwhile, organizations are able to combine IoT and big data analytics to better understand the dynamics of changing weather patterns and what they mean for major transportation systems.
Transportation analytics can also recognize common patterns or movements that lead to traffic problems or accidents. This could accelerate the development of smart cities by optimizing intersection safety.
Deploying sensors directly on trucks, airplanes and other vehicles, meanwhile, can help organizations use transportation analytics to identify parts that need to be replaced or repaired. Organizations could also derive insight from the data to improve shipment tracking, routing, driver performance and support regulatory compliance.
These are still the early days of transportation analytics, which means more IoT rail, air, sea and car use cases are likely still to come.