Successful companies are always on the move, whether they're sending a field support technician to a customer site or shipping products to a warehouse. Transportation analytics in the era of big data are opening up opportunities to boost the speed, efficiency and safety of those activities by harnessing the power of big data and the Internet of Things (IoT).
Traditionally, problems in transportation were studied after the fact because businesses lacked visibility into what was happening to a vehicle and the conditions in which it was traveling. If a delivery was late or an accident occurred, an organization would be left to deal with the consequences.
The ubiquitous connectivity offered by the IoT, aided by big data analytics, is changing that, providing the ability to monitor entire transportation ecosystems in near-real time. The same technologies are also providing the agility to proactively address transportation challenges as they happen—or even earlier.
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.
The data transportation analytics applications need
While organizations may already be sitting on a treasure trove of big data based on historical transportation records, the deployment of IoT technologies means they will be generating even more as they continue to move goods and people.
Some of this data will include information about the vehicle itself, from the engine speed and fuel levels to idle time. However, there will also be data about external conditions including temperature, moisture and even the degree to which a vehicle faces exposure to light or darkness.
As vehicles move, the IoT and big data can be used to analyze shocks, vibrations to help determine whether a vehicle is being tampered with by a third party. Organizations could also be in a better position to support drivers and staff by applying IoT to analyze their driving patterns and even their degree of fatigue.
Few organizations will have all the IoT rail, car or air expertise they need to make the most of transportation analytics in the era of big data. That's why it's a good idea to talk to a trusted partner about how to develop the ideal strategy to support your business.
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