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AI Traffic Management: The Road to Sustainable Smart Cities

AI traffic management

Every city driver knows the frustration of spending hours in traffic congestion—and the anxiety over safety on crowded, chaotic roads.

City managers and traffic engineers have the same worries plus a few more. “The most urgent challenge is to reduce accident rates through efficient, demand-oriented traffic management,” says Gurur Yildiz, Lead Systems Engineer at ISSD Electronics, a maker of intelligent traffic management solutions. “But there are also important quality-of-life issues to consider as well, such as big-picture challenges like cutting CO emissions.”

It’s a difficult situation for everyone involved. But the good news is that AI deep-learning technology and next-generation, high-performance processors have enabled intelligent transportation systems (ITS). These solutions offer an answer to the global challenges of traffic management—and open the door to a safer, more efficient, and more sustainable future.

AI Traffic Management in Action

ISSD municipal customer implementations show how intelligent transportation systems can deliver dramatic results in the most challenging of traffic management scenarios.

The company’s deployment in Konya, Turkey illustrates how ITS can be leveraged to modernize transportation even in unlikely venues. Konya is a truly ancient place: a site of human habitation since 3000 BCE. Today, it is one of Turkey’s largest cities, with a population of more than 2 million. And that number often swells due to the influx of visitors, tourists, and religious pilgrims to the city’s numerous shines and archeological sites.

Modern Konya is a beautiful and beguiling mix of old and new. But that has created some serious traffic management challenges. “The urban planning in Konya simply isn’t adequate to the current needs of the city,” says Yildiz. “As a result, there is tremendous congestion during rush hours and around the big mosques and tourist sites.”

ISSD worked with Konya’s municipal authorities to deploy an ITS to alleviate these pain points. They installed a network of smart cameras throughout the city to help manage traffic flow. The cameras can calculate average occupancy and vehicle count in real time, decide which traffic lanes should be given a green light and for how long, and change traffic signals accordingly.

The results of the new system are impressive. Wait times at Konya’s traffic junctions are down 30%. Carbon emissions have fallen by 40%. In addition, the data insights provided by the system have allowed traffic engineering teams to create detailed simulations of traffic flow and make changes to optimization efficiency.

Another ISSD implementation is in Istanbul. For years, the Ministry of Transportation and the local tollway operator had struggled with a stubborn class of accidents at tunnel entrances and toll booths on the Northern Istanbul Highway. Most frustrating of all: These accidents never should have happened in the first place. They were being caused by drivers of large trucks who didn’t realize that their vehicles were too high to clear a tunnel opening or toll gantry.

ISSD implemented an intelligent transportation system that detects over-height vehicles approaching these critical locations. Smart cameras scan oncoming traffic for potential problem vehicles. If an over-height vehicle is identified, its license plate information is broadcast to the tollway’s overhead electronic display screens to warn truck drivers that they are in imminent danger of crashing and allow them to find an alternate route.

The number of over-height vehicle crashes on the highway has fallen from an average of one or two accidents per month to zero incidents for the entire year.

AI and Hardware That Enable Intelligent Transportation Systems

Significant results like these are characteristic of newer intelligent transportation systems, which have managed to overcome many of the limitations of their predecessors.

Legacy incident detection systems used image-processing algorithms wholly dependent on CPUs—a costly and difficult-to-scale approach. In addition, these systems struggled with image-processing accuracy under adverse weather conditions and when using data from pan-tilt-zoom (PTZ) cameras.

A modern ITS relies on VPU-accelerated, AI-enabled automatic incident detection (AID). This is why they outperform older systems at visual processing tasks and tend to be more cost-effective as well.

ISSD’s solution, for example, sends traffic camera data to a centralized server optimized for visual processing. The server is equipped with Intel VPUs built to handle computer vision workloads in parallel. They also run ISSD’s fine-tuned SPECTO visual processing software, which leverages the AI deep learning capabilities of the Intel® OpenVINO toolkit. The system CPUs are freed from inferencing tasks, and control only response behaviors such as sending alerts to drivers and operators.

This combination of AI optimization and workload differentiation makes the overall solution remarkably fast. If an incident is detected, human traffic safety officials are alerted in less than 10 seconds—and automated responses are taken in near-real time via integrations with SCADA systems and roadside traffic equipment.

Yildiz credits ISSD’s technology partnership with Intel to making this type of deep-learning-optimized processing possible: “OpenVINO was a technological breakthrough for us. It has a direct impact on the overall product performance by optimizing and improving the efficiency of deep-learning models we are using in our algorithms.”

#IntelligentTransportation systems deliver impressive results. But just as important, the innovators behind these systems are taking a holistic, forward-looking approach to solutions development. @issdelectronics via @insightdottech

Building the Future of Transportation

Intelligent transportation systems deliver impressive results. But just as important, the innovators behind these systems are taking a holistic, forward-looking approach to solutions development. And that bodes well for the future.

ISSD incorporates software masking and anonymization algorithms into its solutions—future-proofing their development work against cybersecurity and digital privacy regulations for today and in the coming years.

The company is also looking at how to adapt existing technology to other use cases and verticals. “We are working on complementary use cases like electronic toll collection systems and intelligent parking systems,” says Yildiz.

Longer term, ISSD’s R&D teams are laying the groundwork for Cooperative Intelligent Transportation Systems (CITS) that will one day broadcast safety alerts directly to drivers in their vehicles. Remarkably, they’re also preparing for a traffic management future where commercial and private vehicles have taken flight, says Yildiz: “We are currently planning for the coming age of flying vehicles and drones by exploring flight-based logistics and traffic management.”

Intelligent traffic management will usher in a world in which transportation is safer, more efficient, and more sustainable. But despite all the high technology involved, Yildiz expresses the ultimate goal of his company’s work in very human terms: “Intelligent transportation systems can save lives by reducing accidents. And they improve our day-to-day quality of life by making travel more efficient and giving drivers back all those lost hours.”

Edited by Georganne Benesch, Associate Editorial Director for