Client: Olympia Odos

Olympia Odos is a major motorway concession project in Greece, which is responsible for the operation, maintenance, and improvement of a key highway network in the country. It covers the Athens-Patras-Corinth motorway, a critical transportation route connecting the capital, Athens, with the western part of the Peloponnese peninsula, including the cities of Corinth and Patras.

Asset under management:

• 201.5KM of modern motorway
• 29 tunnels of a total length of 26.5km
• 80 Bridges
• 29 Interchanges
• 9 Ramp Toll Stations
• More than 50 million passengers per year
• 9 Ramp Toll Stations
• 80 Bridges
• 29 Interchanges

The need

Olympia Odos performed a study for All Lane Covers of the highway using the Look Through Percentage (LTP) method. This approach evaluates the necessity of daylighting, resulting in reduced lighting requirements in some areas and eliminating the need for lighting in others, leading to significant energy savings. However, as road conditions are dynamic, incidents may arise that demand full lighting capacity. This makes an adaptive lighting system essential.

The IntelliLight project is a collaboration between the technical expertise of Extrabit and Olympia Odos Concession Company SA (https://www.olympiaodos.gr). Our multidisciplinary team of engineers, data scientists, and innovation experts work closely with Olympia Odos Traffic Management Engineers and project partners to create a high-standard solution, delivering smart adaptive lighting for highways under operation..

Where

IntelliLight was fully developed as part of a large-scale project by Olympia Odos Concession Company SA (https://www.olympiaodos.gr). This initiative was implemented along the Elefsis–Patras highway, spanning over 190 km. The system manages street lighting across 30 intersections and controls more than 8,000 lighting fixtures, optimizing energy efficiency and improving visibility.

Key Technical Features

Assets Under Management (AuM): 8000 lighting fixtures
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Machine Learning Algorithms: Utilizes advanced algorithms to predict and adapt to traffic patterns.
Predictive Analytics: Anticipates vehicular flow, enabling proactive adjustments to lighting conditions.
Real-time Monitoring: Provides continuous data streams for instant insights aboutthe road traffic condition.
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Energy-efficient Controls: Dynamically adjusts lighting levels for optimal energy consumption.
Explore Technical Insights: Delve into the technical intricacies of IntelliLight, from machine learning algorithmsto real-time data analytics. Uncover how predictive analytics redefine street lightingthe era of smart cities.

Using machine learning algorithms, inputs are processed, and an accurate dimming decision is directed to the dimming controllers.

Final deliverables

• Retrieve and process traffic volume data originating from the inductive loop detectors.
• Predict the traffic volume in intervals of 15 minutes for the following four days.
  Receive traffic related incidents from a video incident detection system installed on the I/C.
• Receive meteorological alarms such as heavy rain and low visibility though a newly installed meteorological station
• Classify the predicted traffic volume to predefined dimming levels and send the output to the dimming controllers
• React to external events and raise the dimming in cases of incidents or bad weather conditions

The results exceeded the original expectations, with a prediction accuracy of more than 96% in a four day period and more than 90% for predictions in a 30 minute window. Moreover, the system was able to consistently recognize external events and respond by raising the dimming levels.
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The figure presents the overall architecture of the system that was implemented as part of the pilot phase. Blue rectangles depict the processes that were developed whereas the yellow rectangles represent existing infrastructure. The glue logic was implemented by building custom RESTful APIs that organized and presented in a meaningful way the data to the required components.

The modularity of the system allowed us to cover additional requirements from the highway operator such as dimming in predefined levels instead of linear dimming. Specifically, dimming decisions were made on Annual Average Daily Traffic (AADT) limits. As a result, three plus one dimming levels were used:

• Contractual class which is the maximum possible brightness used on incident detection and bad weather conditions
• Nominal class (Initial dimming level)
• 1st Dimming class (lower traffic volume)
  
• 2nd Dimming class (minimum traffic volume)

Finally, in the event of low accuracy (less than 85% in a four day period) the machine learning algorithm enters a re-train mode that utilizes recent traffic data to adjusts its decision weights.

For the obligatory screenshots of the IntelliLight user interface, figure 3 depicts the predictions of the ML algorithm with red (30 minutes ahead) and green (1 hour ahead) lines aligned to the actual traffic with a blue line in a period of one day. The data are for the Ancient Corinth I/C. As it is evident, the predictions even for the pilot phase were quite accurate with the 1 hour being much closer to the real-time traffic than the 30 minute prediction.

The figure shows raffic prediction compared to actual traffic. The accuracy of the four day
prediction is also visible in the right gauge.

Figure 4 presents the final dimming decision for an uneventful night. The output is enabled after a specified time in the afternoon and is disabled in early morning. As it is evident from the diagram, traffic was predicted to rapidly decrease after 23:00 and hence the dimming was lowered to the minimum allowed class (2nd dimming class) at approximately 23:30. On the contrary, traffic was predicted to rapidly increase at 6:00 in the morning and as a result IntelliLight proactively increased the dimming to the Nominal class at 5:30.