Intelligent Transportation Systems are made up of advanced applications developed with the main goal of providing innovative services relating to different modes of traffic management and transportation.
These applications are deployed with the ultimate goal of allowing users to be better informed and make safer and smarter use of transportation networks.
Some of the most frequent and basic Intelligent Transportation Systems already implemented globally include calling for emergency services when an accident occurs, the use of cameras to enforce traffic laws or the installation of signs that mark speed limit changes on highways. However, these systems are expected to be more complex in the future, with new Intelligent Transportation Systems use cases emerging as technologies such as IoT, 5G, artificial intelligence (AI) and edge computing become more widely available.
For a more detailed primer on this topic, as well as commentary from subject matter experts, read the companion article, “What are Intelligent Transportation Systems?”
How do 5G, IoT, AI, and edge computing enable Intelligent Transportation Systems?
Rather than a monolithic, fixed solution, Intelligent Transportation Systems exist at the intersection of a number of technologies. Chief among them are:
- 5G, the latest generation of cellular connectivity, is marked by massive throughput, ultra reliability, very low latency, and the ability to support massive numbers of connected IoT devices.
- The IoT is an umbrella term covering internet-connected objects; in the context of Intelligent Transportation Systems this could include cars, roadways, roadside infrastructure, bicycles, pedestrians, cameras, and a wide-range of sensors.
- Artificial intelligence software is needed to process the huge amount of data being transmitted over 5G, and other types of networks, from IoT sensors. AI can take these data sets and turn them into actionable insight.
- Edge computing refers to the decentralized compute and storage functionality needed to power AI systems and ingest and process data quickly enough to make the latency gains of 5G meaningful.
The combination of the above mentioned technologies could support use cases such as real-time traffic monitoring and driver notifications. Imagine you’re on a highway and several miles ahead an accident occurs. This accident would be detected by the involved vehicles and roadside infrastructure. In addition to alerting the appropriate authorities, this information could be sent to you to serve as advance notification of pending delays or suggesting alternative routes.
The intersection of 5G and IoT in Intelligent Transportation Systems—ITS is “the original IoT”
According to Michelle Maggiore, Country Digitalization Manager, Cisco, “ITS is essentially the original IoT, since traffic data from sensors has been collected and recorded for quite some time. With edge computing, traditional sensors can provide information in real time, and with more reliable network connections, these sensors allow for automation. As more devices/assets become connected, there are more data points, and more inputs and outputs. This will allow ITS to be delivered with more granularity, to react quicker. When it comes to pedestrian safety, milliseconds count. 5G will help give fiber-like speeds over the air, but 5G by itself does not deliver everything — rather 5G can be a key connectivity technology, and connected vehicles in this area will make the biggest impact.”
Dominique Bonte, Vice President at ABI Research, said IoT is “underutilized…in today’s ITS deployments. A lot of this underuse comes from the challenges to integrate these sensors and their systems with legacy hardware and technology solutions. 5G opens up greater possibilities for bringing real-time data, insights, and decision making to an ITS system anywhere within the network’s footprint. This flexibility will be key when designing or deploying a solution at scale, when the physical connectivity infrastructure cannot scale or support the deployment approach.”
Big picture on IoT, more sensors means more data and more data means better outcomes. As Mike Mollenhauer, Division Director of Technology Implementation at the Virginia Tech Transportation Institute, put it, “More things are now being connected and can report their position and status automatically, and I think that is one of the things that IoT supports well. IoT allows for more things within the ITS ecosystem to be connected and therefore more useful. 5G will support faster and higher bandwidth data transfer for data generated by cameras, lidar and radar. For basic connectivity for simple status or location information, I think today’s communications technologies can support typical data transmission needs.”
How do AI and edge computing fit into Intelligent Transportation Systems?—”These services are still in their infancy”
Jim Misener, Senior Director of Product Management and C-V2X Lead with Qualcomm discussed how AI and edge computing “fit into this IoT paradigm and are quite relevant in the transformation of the original and still relevant ITS vision to reality. Think of a traffic signal and its controller. There are local, distributed actions such as actuating signals, soon enough done more accurately and safely with even more sensors. AI and edge computing transforms this further into an intelligent intersection. Join that to a progression of nearby intersections and coordinate them – and you have a problem that could be addressed quite well by both AI and edge computing.”
Ericsson’s Stefan Myhrberg, Head of Road ITS, said AI “will be key to achieve efficient development and operation of many ITS services, and competition will make this happen. Edge computing is an important tool to optimize workload on computing and communication resources, which will be important to make many ITS applications efficient.”
Peter Meckel, a program manager with ASFINAG, an Austrian publicly owned corporation which plans, finances, builds, maintains and collects tolls for the Austrian highways, noted how AI, edge computing, and other technologies enable “collective perception…the transmission of sensor-based object detection shared between road users. In order to fuse multiple sensors and/or message in real time, edge computing could be the solution. But these services are still in their infancy.”