Making flying taxis a reality by 2035: what needs to be done to prevent inner-city deadlock?
According to the UN, 68% of the world’s population is expected to live in urban areas by 2050. As more and more people flock to cities, we will see denser populations, increased traffic congestion, and greater pressure on urban transportation and mobility.
Most cities have limited public transport options, a lack of parking space and busy roads. Add more people to the equation, and the result is cities in deadlock. The muscle of the economy translates into disgruntled commuters who are finding it increasingly difficult to get around, and our infrastructure capacity is being stretched to its limits. When reimagining cities, it is crucial that we identify how technology can be used to find ways to ease urban congestion, and how it can make city living and mobility faster and better, while remaining affordable.
So, why not look to create an airborne highway? It is not entirely surprising that Uber has partnered with NASA to design an urban-air traffic control system, with the aim of launching a flying taxi service by 2023. Ultra short-haul air travel, such as flying Uber taxis, could help ease some of the pressure that simple daily commutes will put on cities in the future. Not only could it ease congestion worries, but also might remove the need for people to own their own vehicles. Shared ownership and ride-sharing, apps like BlaBla Car and Ola, are flourishing as commuters look for better and more comfortable travelling alternatives.
The idea may seem far-fetched in terms of product design, but manufacturers are further ahead than we would think, and the UK government is already in discussions over exploring public opinion on the technology as advised by the Department for Transport. In fact, if public opinion lies in favour of flying cars, they may be manufactured in as little as 15 years, although certain safety considerations and regulations would also need to be clarified in the process.
So, will flying taxis and a three-dimensional highway be the cure to inner-city deadlock by 2035?
Starting at the bottom: building a digital infrastructure
What is most likely to slow down the emergence and adoption of flying taxis is something that we take for granted: our infrastructure. A transport system is traditionally made up of a number of components – humans, infrastructure, businesses, government, policy and all different modes of transport. Technologies such as Internet of Things (IoT), Blockchain, artificial intelligence (AI), mobile and other smart personal devices are beginning to play an increasingly important role in our transport system. In fact, these technologies are becoming infrastructure in their own right, and will play an increasingly significant role as consumers and businesses evolve.
As the population booms, communities and cities must re-think the future of human mobility. Mobility needs are constantly shifting in line with human behaviour, but economic considerations, such as city plans for diversification, demographic changes and migration patterns will also have a significant impact on the transportation system. Our cities are going to need to become an ever-evolving system that not only takes into consideration the physical aspects and people’s experiences, but also has a digital infrastructure that can adapt to the needs of a changing community.
Replacing one type of congestion with another
Congestion of road networks is a problem infiltrating cities globally. Flying taxis have the potential to alleviate it. But what happens as more and more people begin to use this new alternative?
- Siemens agrees $1.92bn deal to develop 94 trains for London Underground
- BigRep launches first fully 3D printed and functional e-motorcycle
- Manufacturers must overhaul infrastructure and resources to support maximised product uptime
Imagine lifting half the current traffic commuting in and out of London above ground. As we currently stand, our systems simply are not ready to deal with the complexities of high-density traffic in the air. And unless businesses and governments work together to scale to the demand, we will face a new kind of congestion – digital congestion.
Real time, closed-loop feedback will be vital for optimising the flying taxi ecosystem. This means creating platforms that can interact with each other to capture, process and share vast amounts of data from other flying cars, weather and traffic sources.
To prevent digital congestion from becoming a problem, taxi platforms, such as Uber, will need to have Edge Intelligence to be able to handle increased digital traffic. It will be critical that these central systems act as nerve centre for the “Intelligent Mobility” and are able to handle the volume of data passing through at the required velocity. To that end, flying taxis will need to be enabled with low latency network capability with 5G roll outs.
This will also be important for ensuring our airways don’t become congested and dangerous. As more drones and eventually flying taxis enter our airspace, manufacturers will need to ensure that vehicles can be tracked and controlled, so we can be transported safely from A to B. 5G will also help flying taxis establish real-time communication networks between vehicles, and other modes of transport and infrastructure. But to achieve this at scale, Uber and a range of other new start-ups entering this space will need to develop a secure communication arrangement, like our current air traffic control system, so that it doesn’t over burden networks.
Furthermore, since most of our transportation needs are currently fulfilled through a “physical” system (such as a car or a bike), marrying up digital systems with physical components will be essential. This means creating platforms that interact with each other in capturing myriads of data from “physical assets” to offer the best public transport option personalised to the needs of the user.
The energy ecosystem
The energy Ecosystem for flying taxis is yet another element that needs attention. The changing landscape of propulsion technologies from fossil fuel based to an electrically charged battery powered environment needs further development to enable flying taxis. Significant advances in battery technology coupled with a viable charging infrastructure will have to be planned in tandem with the adoption curve.
Before flying taxis take off, businesses and government bodies across the world will need to re-think the basics of urban human mobility. Road congestion is only set to worsen with time, but 5G and the Internet of Things could prove to be a prime opportunity for businesses beyond Uber to experiment with ultra short-haul air travel.
To overcome the risk of digital congestion and respond to every aspect of the design of three-dimensional motorways, we need to adopt a consortium-based approach, where entrepreneurs, organisations and governments work collaboratively to introduce flying vehicles one step at a time. As cities become an ever-evolving web of connections, we must make sure the right digital infrastructure is in place to support an entirely new mode of transport. Only if we increase cross-industry collaboration between public and private sector bodies will we be able to prevent gridlocked cities in the future.
5 Minutes With PwC on AI and Big Data in Manufacturing
Please could you define what artificial intelligence is, and what Big Data is?
AI is the ability of a machine to perceive its environment and perform tasks that normally require human intelligence, and it’s a whole field of different technologies, techniques and applications.
Big data is a set of tools and capabilities for working with, for processing, extremely large sets of data.
How does AI and Big Data work together?
Big data is just one of the enablers of AI, though as we see increasing volumes of data, it’s one of the most important
How can this be applied to a manufacturing setting?
Broadly speaking, there are many benefits of AI, and the use of data, which include reducing costs, minimising human error, and increasing productivity and efficiency. The important thing to consider is any setting - for the use of any technology - is what is the problem you are trying to solve? Be it merely automating repetitive tasks or to reinventing the nature of work in factories by having humans and machines collaborate in order to make better and faster decisions.
Why should manufacturers use AI and Big Data when adopting smart manufacturing capabilities, what is the value for manufacturers?
One view is, again, the economic benefits of AI, which come in manufacturing as a result of:
1. Productivity gains from automating processes and augmenting the work of existing labour forces with various applications of AI technologies.
2. Increased consumer demand due to the increased ability to personalise and tailor manufactured products, along with higher-quality digital and AI-enhanced products and services.
Manufacturing (and construction industries) are by nature capital intensive, and in our 2018 report, “The potential impact of AI in the Middle East,” we estimated that the adoption of AI applications could increase the sectors’ contribution to GDP gains by more than 12.4% by 2030.
How can AI and Big Data help manufacturers to evolve in the Industry 4.0 revolution? What about those already looking at Industry 5.0?
It’s really about the investment you make now, in order to futureproof your business.
We typically see two broad strategies or approaches to the adoption of AI. There are things that we can do immediately, without any recourse to Big Data - which is to adopt technologies we describe as Sensing, those involving computer vision, for example. There are plenty of use cases where these can be used immediately in manufacturing, such as for automatic fault detection. However, there is a longer term play which requires investing in data - getting the right collection mechanisms in place, storage, data governance, Big Data capabilities etc - in order to develop increasingly valuable machine learning driven AI use cases. This is absolutely necessary for long term adoption success.
What is the best strategy for organisations looking to realise the value of AI and Big Data in manufacturing?
AI and Big Data are only one part of a successful smart factory. The organisations that lead on AI adoption are those who have already made the most progress in digitising core business processes. In order get ahead in using AI solutions at scale, there are a number of technology investments and organisational decisions to be made, including:
1. Digitising processes ultimately leads to improved ability to generate data, and in the manufacturing setting - with many 100s of sensors generating 1000s of measurements in real time, the result is Big Data. Data is key to building AI so reliable and accurate data acquisition, management and governance are key. The production line and factories play a critical and direct role in the data-acquisition process.
2. AI strategy, both long and short term, begins with the use cases, the business applications. Manufacturers need to ask where they want to use AI and gather these use cases together and prioritising projects based on a balance of expected impact and complexity of implementation.
Of course, in addition to technology and business processes, people are at the heart of any successful technology adoption. AI teams need to be composed not only of data scientists, also data engineers and solution architects to enable their work, data stewards to ensure accuracy, and increasingly so call “Analytics/AI translators” who are able to communicate with business leaders and technology experts. Culture is also key, and manufacturers need to enable a data and AI-driven culture, building trust in data and algorithms by educating their workforce about AI and its capabilities, how best to extract value. It’s not just the positive of course, but also the risks and limitations, as these when encountered without expectations having been set, can significantly impact willingness to invest.
What are the challenges when it comes to adopting AI and Big Data in manufacturing?
has shown that one of the major challenges to implementing AI is uncertainty around return on investment (ROI). As I said, there is significant investment required for a long term data and AI strategy to be successful, and expectations around the time to see tangible returns must be set realistically.
Many companies also struggle with the data side: collecting and supplying the data that an AI system needs to operate, and ensuring that it is accurate. Again, this speaks to the bigger investments required in digitisation.
Some of the main challenges for manufacturing companies with implementing AI at a scale from our research include:
- 40% → Technologies not mature
- 40% → Workforce lacks skills to implement and manage AI
- 36% → Uncertain of return on investment
- 33% → Data is not mature yet
- 32% → lack of transparency and trust
- 24% → Work councils and labour unions
- 22% → Regulatory hurdles in home & important markets
One element highlighted here, particularly around lack of trust, and labour unions, is that AI is typically misrepresented in the media as “replacing” workers, and taking jobs. Yes, there are efficiency gains to be made from automation, as there have been since the first industrial revolution. But we believe that Data and AI are at their most valuable when they are used to augment workers, enhancing their abilities and the products being manufactured.
Another challenge we’re starting to see emerge is cyberattacks increasingly targeting interconnected equipment and machinery in smart factories. PwC recently hosted a webcast, in cooperation with the National Association of Manufacturers in the US and Microsoft to discuss this.
What are the current trends in AI and Big Data in manufacturing?
- We see companies putting slightly more focus on adding AI solutions to core production processes such as the engineering, and assembly and quality testing
- Safety is of significant importance, with techniques adopted in protocol adherence capabilities (for example maintaining safe distance from specific machinery) being adopted in more every day scenarios for COVID-19 protocol adherence
- There is considerable interest in predictive maintenance for large machinery involved in manufacturing processes, and also supply-chain optimisation
What do you see happening in the AI and Big Data industry in manufacturing in the next 12-18 months?
Honestly, I think we’ll see a continuance of where we’ve already been going for the last 12- 18 months. AI and data are already being used in manufacturing but this use doesn’t get as much attention in the media as, say, healthcare, but the success stories are there, and they will continue as operations continue their digital journeys.