Autonomous Vehicles, Digital Twins, and the Future of Public Transportation | Smart cities, part III

Author: Chen Gabay

How do you travel around your city? As urban areas get larger and crowdier, private cars won’t do, and good public transportation becomes crucial. New technologies like Autonomous buses and shuttles offer a game-changer solution already being tested in cities around the world. Cognata’s 4D model, the Dynamic digital twin with AI mobility layers, combines those technologies and offers an innovative way to help validate and test this promising technology and its effects in a more safe and cost-effective manner.

Public transportation in tomorrow’s cities: the search for better solutions

Many great cities worldwide share a similar trade – public transportation is the primary means of traveling. Rather than having private cars jam the streets, demanding thousands of parking spaces, good public transportation can help decrease traffic congestion, reduce pollution and return valuable residential and commercial spaces. Trains, buses, taxis, rented bicycles, and scooters allow people to move around the city at low costs and at different levels of comfort. But, as cities grow and get denser, the need for even more effective and efficient public transportation becomes crucial and increases the search for better methods. Rising technologies such as Autonomous vehicles, IT, and big data are being developed, in an effort to improve public transportation.

Autonomous vehicles: a Game-changer for Public Transportation

One of the more useful methods that can improve our public transportation is the evolving technology of Autonomous vehicles, as it offers many benefits. First, transportation companies could operate their fleets 24/7 in a more flexible way, while using different scales of transportation like buses and shuttles, according to demand. Those autonomous vehicles could be used for example fixed routes, dynamic routes, and first and last-mile commutes. Furthermore, connected autonomous buses and shuttles will be able to communicate with each other on the road for more effective, constant, and safe driving. The outcome will help transportation companies manage their fleets and serve the public in a more sustainable and efficient way, as well as lower their costs while reducing road congestion and pollution in cities.

Multiple cities worldwide have started to enable testing programs for autonomous vehicles and shuttles, moving towards the much-needed regulation of AV. Those autonomous vehicles mostly drive on specific routes and at low speeds, for example on university campuses, industrial areas, airports, etc. Those fleets are managed by companies that can react immediately to any issue and even use teleoperation driving if needed. Such Autonomous vehicle trials allow the public to start gaining trust in this new technology and in its potential to answer their transportation needs, but there is still some public hesitation.

Dynamic Digital twin: Accelerating the Integration of Autonomous Public Transportation

Even though Autonomous public transportation carries a huge promise, some municipalities and citizens are still cautious about integrating this new technology into their urban environment. This concern is understood as buses and shuttles are large vehicles and their AI must be aware of their environment and have the best sets of sensors to “see” any potential threats. It’s also clear that Autonomous vehicles should be trained and tested for regular and extreme safety events in order to be certificated to drive urban roads. According to studies, Autonomous Vehicles would have to drive about 8.8 billion miles in order to drive safer than a human driver. However, this number can be lowered dramatically by using virtual simulations for testing and validating the AI and the sensors, such as Cognata’s Dynamic Digital twins with its AI mobility layers.

Dynamic Digital twins can replicate the urban environment and the routes of the public transportation fleets. Using those virtual models, the AI of the Autonomous vehicles can be examined, tested, and validated in regular and extreme scenarios such as artificial safety hazards, and different road and weather conditions. Different scenarios can test how the AV reacts to other vehicles on the road, motorcycles, cyclists, pedestrians, and other vulnerable road users. These virtual models can also be a great way for cities to examine the integration of those autonomous buses and shuttles in their environments, choose the best and safer routes and speeds, and examine the Autonomous public transportation behavior in different planning alternatives. For example, how bus lane placement affects traffic congestion and safety. In addition, virtual models and advanced safety tests can help promote public acceptance of this technology.

Cognata’s dynamic digital twins: An innovative way to help integrate autonomous public transportation

Autonomous vehicles offer cities the opportunity to significantly improve their public transportation, but safety should come first and public trust must be gained. Cognata’s 4D models with the dynamic mobility layers of Autonomous Vehicles could help accelerate the development of autonomous public transportation safely, reduce costs, and help cities and planners understand how changes will affect the urban environment to the benefit of all.

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