On the leading edge: Decentralized computing can transform smart-mobility infrastructure
December 02, 2019
December 02, 2019
Edge computing test bed shows potential for cost-effective upgrades to connected roadway efficiency and safety
We’re steadily driving toward a future of connected and autonomous mobility. Estimates show that that the enterprise (including automotive) Internet of Things market will grow to 5.8 billion endpoints in 2020, a 21% increase from 2019. While we’re in the early stages of this mobility revolution, forward-thinking communities and transportation agencies are already busy at work considering ways to update infrastructure systems and reconsider traffic-management systems to help ensure the safety and effectiveness of connected and automated vehicles (CVs and AVs) on our roadways.
As a result, agencies are beginning to develop investment and policy priorities for smart-mobility networks as part of their long-range transportation plans. This involves identifying legal implications of CVs and AVs and planning for infrastructure upgrades needed to support connected transportation networks.
This planning and preparation aren’t without challenges. Transportation agencies are already carefully managing a fine line between balancing limited budgets and prioritizing the ongoing demands of upgrading aging infrastructure. Consider that the American Society of Civil Engineers reported that it would take $2 trillion just to address current necessary improvements in our roads and bridges. Couple this with the reality that there are added costs to modifying infrastructure to make connected transportation corridors possible. This involves systems management, updates to traffic operations, strategic modelling, and forecasting to consider ideal timelines for implementation.
Fortunately, there are nimble and scalable solutions that can enable transportation agencies to ramp up smart-mobility networks in a cost-effective manner.
In our testing of smart-mobility technology, edge computing has proven to offer an ideal approach. Edge computing involves information processing that’s done at or near where it’s needed, reducing issues of latency and bandwidth for real-time response. This approach involves the installation of wireless technology along transportation infrastructure and the use of vehicles that can process data at the source to create a more direct system of analysis and communication.
There are nimble and scalable solutions that can enable transportation agencies to ramp up smart-mobility networks in a cost-effective manner.
Edge computing also comes with cost benefits. It’s wireless, so there is a reduction in connections needed via hardwire or in areas where there may be limited existing connectivity. The devices themselves are relatively low-cost, even when hardened for outdoor environments. In most cases, they also fit into existing transportation networks where devices are already within the built environment.
We’re actively testing and vetting smart city mobility technologies like this via a partnership in Tennessee with the Space Institute Research Corporation, an independent research and development organization at the University of Tennessee Space Institute. With an eye on developing real-world solutions for connected infrastructure systems, our team is focused on leveraging technology in a way that can enhance the safety and efficiency of our infrastructure network. As part of this test bed, we conducted a field test of an edge computing environment to recognize and respond to a wrong-way driver event.
Results of this test provides promising opportunities for improving roadway safety and efficiency. We found that edge computing devices can to alert to critical events, such as the wrong-way driver scenario tested in this case, within seconds. Compare this with traditional CCTV/human-verification systems, with average alert times of up to three-times as long as edge computing (based on field tests conducted in April 2019).
The benefits to driver safety and emergency response in these seconds are immense.
In our test scenario, when the system recognized an object traveling the wrong way, the edge device collected information from the sensors and recorded it in a cloud-based database, transmitted by the cellular network. Every wrong-way event resulted in a series of LED lights flashed on the roadway to notify the driver that they are traveling the wrong way. When a second sensor recognized the same event, a subsequent series of alerts were triggered to send email and text notifications to system operators (traffic management center staff) and request confirmation that the event is valid. Once confirmed by redundant systems (human or sensor), secondary events were configured to display dynamic message signs, broadcast a dedicated short-range communications warning signal, and deploy operator help/response teams to the location of the event.
Ultimately, this shortened response time to dangerous driving situations, once applied in the real world, will help reduce accidents and road fatalities.
In addition to improvements in roadway safety, edge computing offers:
Edge computing will drive integration among Intelligent Transportation Systems (ITS) devices. From safety systems such as wrong-way driver detection to vehicular prioritization systems, traffic management, and even autonomous vehicles and driverless cars, edge computing will be the backbone for a transportation revolution.
As we face this shift, agencies must extend the security policies of their organization to edge devices in order to protect the integrity of the physical transportation network while balancing the integration of these devices across the range of ITS systems. The result will help transportation agencies verify and respond to the rapid changes associated with the range of potential transportation situations as quickly as they occur.