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Theories and concepts about Artificial Intelligence were born in the 50s. Yet, Artificial Intelligence (AI) achieved functional applicability in the last two decades, with the rise of machine learning and deep learning. Today, everybody is talking about AI, and businesses across various industries have realized that this revolutionary technology can drastically increase the speed and quality of their products and services.
At this stage, AI is on its path to creating a revolution in the mobility industry. Cities around the world are constantly suffering from increasing traffic, limited availability of space in urban areas, traffic-induced noise, and pollution. Meanwhile, cities are becoming more and more connected and harmonized with sensors, and so the possibilities for AI to improve energy efficiency, liveability, and mobility, will grow exponentially.
Let’s take a look at why AI is the key enabling technology in creating individualized, environmental-friendly, and autonomous mobility systems.
The role of AI in mobility systems will undeniably be tied with the development of smart cities. Both concepts are becoming a necessity, as the urban population is increasing constantly and consumer needs are changing every day.
According to research conducted by the United Nations Department of Economic and Social Affairs, 55% of the world’s population lives in urban areas. What’s more, this number is going to rise to 68% by 2050.
This rapid increase of population in major cities will lead to pressure for sustainable environment initiatives, demands for better infrastructure, and better quality of life. Smart cities, powered by AI technology, are a part of the solution to these demanding challenges urban areas face.
To function properly, smart cities require the processing of enormous amounts of data, also known as Big Data. Big Data is described in three terms: high volume, high-velocity datasets, or high-variety information assets. High volume represents massive datasets, high velocity datasets mean they are processed very quickly using algorithms, and high-variety information assets represent the use of different data sources.
When AI and Big Data collaborate, the results can be more than promising. AI is described as a non-human system that learns from experience and imitates human behavior. It can efficiently look through Big Data, and create predictions and cost-effective solutions to drive smart city technologies.
Smart city technologies will play an important role in fixing the ongoing problems of public transit and public safety. When it comes to public transit, cities with large transit infrastructures have realized that they must begin the process of harmonizing the experience of their passengers. Whether passengers are traveling by car, moped, scooter, train, or bus, they can provide real-time information using their mobile apps. As a result, passengers can communicate delays, breakdowns, and find less congested routes.
Once cities gather and analyze public transit usage data, they can make better decisions when improving routes and timings, and distribute infrastructure budgets more accurately.
Transportation is heavily tied to complex hardware-based ticketing systems that don’t have the flexibility for dynamic pricing. Once transit operators switch to software-based platforms, they will be able to know how many seats have been booked and how many tickets have been sold in real-time. Furthermore, operators can cross-reference these data points with the capacity of trains and busses to propose different prices throughout the day. Using AI, operators can learn from rider patterns and use that data to form a dynamic pricing strategy.
Dynamic pricing has been around since the 70s, and It was mainly used by the airlines for selling airplane tickets. Unlike fixed prices for airplane tickets, prices are chosen based on current demand and other factors. Therefore, prices can change every day and even throughout the day.
Artificial Intelligence can absorb large amounts of data from various sources including historical booking and pricing information, route information, schedule changes, competitor pricing, and web user interaction behavior. Dynamic pricing engines when combined with machine learning, can suggest price structures that can be regulated on a dynamic basis.
When it comes to urban transportation, they contrarily have fixed prices for mass transit. Passengers pay the same price at 1 a.m. as they would pay at 1 p.m. or 3 p.m. This system is pretty counterproductive if the goal is to prevent rush-hour crowds and create better vehicle distribution throughout the day. To fix this, transit operators could apply the highest price during rush hour and apply lower prices during less busy schedules. As a result, this would stimulate drivers to ride when the price is lower, and would also have a positive impact on urban mobility and general healthcare.
Interestingly, ride-hailing companies have implemented a similar strategy called surge pricing. In plain words, they raise prices during peak demand and lower them when there is less demand.
Another similar concept has made a good impact in London, and it is called congestion pricing. Drivers are charged a premium tariff, when they want to drive in or out of the city center, during rush hour.
It may be hard to believe, but a Swedish railway company called SJ has been offering dynamic pricing on its tickets since 2004. Namely, this company has been selling tickets online since 1997, and this is why more than 90% of their passengers are purchasing tickets through dynamic pricing.
The goal of smart mobility systems is to increase safety, reduce traffic congestion, improve air, and reduce noise pollution and costs. Smart mobility systems have been recognized as essential for decarbonizing the transport sector and reaching the EU emission reduction goals. Artificial intelligence is proving to be a powerful tool that has the potential to drive a sustainable transition to more efficient, liveable, and more human-centric mobility systems.
AI, when applied to urban mobility, can rely on data from existing infrastructures such as traffic controller detection, urban centers, video data, fleet data, and public and private third-party data. In this transition, the public sector will play an important role in ensuring that AI solutions are secure, inclusive, and rely on non-biased, fairly shared data that still preserve citizens’ privacy.
The shift towards an AI-driven mobility will bring a positive impact on all the value chains involved. Municipalities and private mobility operators will be working together to get closer to this evolution.
MaaS systems have proven to be a great alternative to personal transport. These systems offer different means of transportation, and users can book, manage, and pay using their smartphones. Also, MaaS is proving to be a key player in reducing traffic congestion, enabling vehicle-free cities, and system-level optimization of mobility investments.
When powered by AI controllers, MaaS systems can optimize, monitor, and coordinate fleets and, at the same time, offer great options to individual users. What’s more, AI-based MaaS can enable ride-sharing users to share autonomous vehicles across an optimized route in a much cheaper and safer way.
Self-driving vehicles are finally making their way into the transportation sector. Although the majority of these vehicles are still pilot projects, some companies have already deployed their vehicles on public roads.
Computer vision and deep learning systems are the foundation of self-driving vehicles. They are responsible for processing and giving context to all the data that is received from the sensors. Self-driving cars collect data from various sources such as cameras, radars, LIDAR, etc.
As AI technology continues to evolve, self-driving cars might become increasingly popular among consumers.
As AI and machine learning continues to evolve, there is no doubt that the mobility industry will be changed forever. In the future, AI assistants will organize our trips and instantly help us find, book, and pay for the best transport option, depending on our situation and needs.
As of now, mobility is such a fast-pacing phenomenon that we always have to be ready for the next change. Just think of the helicopter cab that takes you from Fiumicino airport to Rome, or Elon Musk who wants to replace Los Angeles-Sydney flights with Falcon Xs.
These are just a few novelties worth mentioning, what is certain is that the mobility of this era is the real booster for accessibility and flexibility.
Artificial intelligence will not just change the way we travel, but will also revolutionize our lives in urban areas, by improving energy efficiency, and the overall quality of life.
Customer Success Specialist at 2hire
I think that the most accurate adjective which describes me is: curious. My curiosity brings me to define my primary interest: reading. Read entails a contemplative state of mind.
