In our previous post, we gave a short definition of digital twins and discussed why it’s important to use connected sensors for data capture of your road network. Now we are going to discuss the benefits of building a digital twin of your road network and why capturing data using digital twin technology is far more cost-effective and efficient than the traditional methods.
Model simulations for real world scenarios
Building digital twins in other sectors, such as modular construction, has already proved its worth. For example, using a digital twin of an 18-story modular building to simulate the on-site assembly helped CadMakers get the work done ahead of schedule, and, of course, saved them money. Using digital twins in health care for modeling various outcomes has been beneficial, in cases such as treatment planning, patient diagnoses, and drug development. In the future, digital twins can even be used for fighting viral infections.
Digital twins have considerable potential also in road network-related fields, such as road maintenance and road network engineering. Running simulation on digital twins using big data can predict road network deterioration and help to plan timely road maintenance jobs. This saves a lot of time and resources because the repairs can be conducted before the damage becomes too severe and thus bigger works can be prevented.
The right data collection tools for your digital twins
Before you can build a digital twin and be ready to run proper analysis on the data, it is important to collect the data with the equipment that provides you the necessary level of quality for processing. There are various options on the market ranging from smartphone-based to high-tech solutions. When choosing, it is good to bear in mind what it is that you actually need and what resources are available to you.
Solutions that use smartphone data capture are budget-friendly and simple to use because all of us are familiar with the technology. Just place the smartphone on your windscreen, drive through the roads, and capture the data. However, because of their smaller image sensors, the resolution is poorer and there is noise in difficult shooting conditions, such as low light and rain. In addition, since smartphones use basic location positioning technology, the geodata is subpar as well. This means that while this solution can be used in road maintenance generally, the quality of the data does not suffice for building digital twins.
The high-tech devices use high-end sensors, such as GNSS/INS systems and LiDAR scanners. The level of accuracy of those sensors can range from centimeters to millimeters. Depending on the accuracy, the captured data can be used for a plethora of purposes, ranging from road asset management to road engineering.
However, the return on investment of the highest accuracy sensors may not be realized for several years. In certain use cases though, such as in road maintenance and safety auditing, the captured data does not have to reach the highest level of accuracy.
Smarter road networks with AI-based software
Instead of manually annotating every road individual asset and defect, which is very time-consuming and labor-intensive, AI software does this automatically. An engineer can only annotate one mile per hour, while AI can digitize unlimited miles per day. Efficiency is not the only benefit of AI software: the quality of the digitized data is also much more uniform compared to the output of human digitizers due to the absence of subjective bias. Thus, the data processing is optimized and cost-efficient, and the quality level is consistent across the processed data.
Combining automatically digitized and AI-based processed data with analytics tools allows for advanced predictive road maintenance. The future lies in automating the process of combining these layers into one digital platform to see how changes in traffic, weather, and different maintenance and construction practices affect road quality. These use cases are highly valuable for cost-effective and sustainable road maintenance plans and safety assessments.
Ultimately, these advanced applications are all dependent on the quality of the data that you capture, and the hardware you choose should suit your needs. The solutions range from less accurate, budget-friendly devices to more expensive high-tech systems with superior precision.
In our next post, we will go more in-depth regarding real-world use cases, to help you decide which solution is ideal for your given needs and resources.