Artificial Intelligence(AI) is rapidly transforming the landscape of transportation infrastructure, intelligent transportation systems, and traffic signal maintenance. From optimizing traffic flow to enhancing safety, AI-powered sensors and connective infrastructure are enabling unprecedented levels of monitoring, prediction, and optimization across our roadways.
However, when it comes to the specific details of preventative maintenance for traffic signals, many agencies are still missing out on the full potential of AI. While robust preventative maintenance programs typically include monthly services to prevent common issues, our research revealed that only about 15% are currently leveraging AI for their preventative maintenance effort (based on interviews of select customers from within our public agency accounts). This represents a significant missed opportunity. Understandably many public agencies are not yet able to seize the full AI potential as many of their devices, detectors and sensors many still be early generational and not fully connected to the software systems that can fuel the machine learning required of AI.
So, how exactly can AI play a pivotal role in traffic signal maintenance?
Unlike traditional data analysis, AI empowers us to delve into more complex and comprehensive datasets. These richer datasets provide invaluable insights, guiding traffic signal maintenance crews to focus their limited time and resources on the most critical and impactful aspects of a traffic signal maintenance program.
Here are a few examples of how AI can elevate your traffic signal maintenance:
1. Predictive Reports from Historical Data
Imagine unlocking hidden patterns and trends within years of historical traffic signal maintenance records. Signals that have been in operation for many years often possess a wealth of data on system cycles, failures, and cumulative usage. Once digitized, AI can analyze these robust historical records to generate predictive reports, and action plans that human analysis alone would likely overlook. This allows for proactive maintenance, addressing potential issues before they lead to costly disruptions.
2. Analysis of Critical Environmental Data
Traffic signals are constantly exposed to various environmental factors, all of which can impact their performance and lifespan. AI can collect and analyze these diverse factors on a large scale, across numerous signal instances. This includes crucial data points such as:
• Weather patterns: Extreme temperatures, heavy rainfall, or snow that can stress components.
• Moisture levels: Humidity and water ingress can lead to electrical issues.
• Soil content: Ground conditions can affect foundational integrity.
• Manufacturer historical records: AI can even analyze data from manufacturers to predict which components are more prone to requiring extra maintenance or failing sooner than others.
By understanding the interplay of these environmental factors, agencies can anticipate and address maintenance needs more effectively.
3. Anomaly Detection with AI Sensors
AI-powered sensors are another game-changer. These intelligent sensors can continuously monitor electrical parameters, detecting subtle anomalies like voltage fluctuations or early signs of bulb deterioration. By identifying these deviations from normal operating conditions, traffic signal maintenance teams can intervene promptly, preventing minor issues from escalating into major malfunctions and ensuring optimal signal performance.
In conclusion, integrating AI into your traffic signal maintenance program is not just a technological upgrade; it's a strategic move towards a more efficient, predictive, and ultimately, more reliable transportation network. By harnessing the power of AI, agencies can move beyond reactive repairs and embrace a proactive approach that saves time, reduces costs, and enhances public safety.
