Future of Smart Cities and Real-time Traffic Predictions (Part II)
by SWIM Team, on Oct 13, 2017 11:05:00 AM
This is a part 2 of a two part series. Read more about the future of Smart Cities Part 1.
New innovations in Machine Learning, Edge Computing, and real-time data analytics are shaping the development of Smart Cities projects around the world. One benchmark use case for Smart Cities efforts is that of Intelligent Transportation Systems (ITS), which include the use of connected vehicles and transportation infrastructure to automate or optimize transportation systems. With 58% of in-development Smart Cities projects relating to either Smart Buildings or Smart Transportation efforts, and 250 million connected vehicles expected to be on the road by the year 2020, the adoption of Smart Cities technologies will have a transformative effect on the transportation systems of the future.
When utilized together, Machine Learning, Edge Computing, and real-time analytics technologies can make traffic systems smarter, cheaper, and more efficient for both public operators and citizens. In part 1 of this series, we discussed Machine Learning and Edge Computing. In this post, the Swim team breaks down how real-time analytics technologies will shape how cities can use data to the improve transportation systems of the future.
Edge Computing provides a solution to the scale and resource (bandwidth) constraints of cloud-only architectures. The scale problem is solved by outsourcing compute power to the devices themselves, and bandwidth needs are significantly reduced by processing raw data at the edge. However, when combined with real-time analytics, the efficiency benefits of Edge Computing architectures can truly be maximized.
Real-time analytics can run on the edge to reduce data volumes and react locally, enabling a variety of use cases. For example, connected traffic signals can perform real-time analytics on an intersection, providing traffic information to drivers or autonomous vehicles (which are another real-time analytics use case). This can lead to safety improvements, as vehicles can be made aware of hazards as they approach an intersection, well before any hazard is visible. This can also improve traffic congestion, as drivers and autonomous vehicles can be rerouted, optimizing traffic flow in real-time.
Similarly, real-time analytics can aid emergency response dispatch and routing, as a real-time (milliseconds vs. minutes) map of traffic can be made available to municipal agencies. Furthermore, individual intersections will be equipped to react to the presence of emergency vehicles, clearing paths for emergency responders in real-time. This can lead to decreased response times, potentially saving lives.
Many other beneficial Smart City applications can be enabled by intelligent traffic infrastructure. Machine Learning, Edge Computing, and real-time analytics can power predictive maintenance applications (e.g. traffic signal maintenance), optimize traffic flows for major events (based on past performance), and enable countless other applications by making real-time traffic data available to third-party developers.
This is a part 2 of a two part series. Read more about the future of Smart Cities in part 1.
Learn how SWIM brings together the benefits of Machine Learning, Edge Computing, and real-time analytics to power the next generation of intelligent transportation and Smart Cities applications.