Of all the ways AI is expected to improve modern life, perhaps none is more intriguing than its impact on public transportation. The pandemic will not last forever. Therefore, at some point, people all over the world will need to move from one place to another again quickly, efficiently, and at a reasonable cost.
In most cities, however, mass transit systems like the subway, buses, and airplanes already struggle to keep up with passenger levels, resulting in traffic jams, accidents, and dissatisfied consumers. But since these are primarily logistics issues, and logistics thrives on data analytics, AI is poised to provide dramatic improvements to our mobility.
Better and faster solutions for public transport
Park City, Utah, for example, recently became the 500th city to implement a platform called Optibus. Take advantage of artificial intelligence to improve public transportation planning and scheduling. Performed manually with traditional automation, this Herculean task hampers the ability to flexibly and dynamically adjust resources to actual demand. Optibus’ intuitive system reduces the time to analyze complex scenarios to less than a minute. This gives authorities the ability to quickly determine the appropriate response to changing conditions. Worldwide, Optibus handles more than 2.5 billion passenger trips a year. This reduces carbon emissions by about 100,000 tons per year. The platform can also be applied to other pressing issues like supply chain delays and driver shortages.
AI has the potential not only to improve today’s mass transit system, but to reinvent it all together. Technology is already showing how it can drive coordination between GPS, navigation apps, connected cars, and even taxi and ride-sharing services to effectively combine them into a single entity. A. recent report on InTech He highlighted the many ways that AI can tidy up the myriad non-public mobility services to ensure better flow during peak and off-peak hours.
Connected cars, for example, can be steered around traffic jams and then straight to the most convenient parking spots. Meanwhile, AI-controlled drones can provide constant monitoring of conditions on land, sea and air to ensure a smooth and trouble-free journey regardless of the mode of transportation. In the near future, we can expect autonomous vehicles to enter the mainstream, providing even greater opportunities for AI to reduce both the duration and risk of rapid mobility.
The intention here is not to replace current modes of mass transportation, but to make the entire system more economical and reliable. Earlier this year, for example, Mexico City saw a significant earthquake that took half of its Metro system out of service. This caused some two million passengers to seek alternative services, mainly in the loose collection of private taxis and minibuses in the city called colectivos. By using Google Maps and a UK application called WhereIsMyTransport, these services operate in a coordinated manner. They match passengers with services quickly to turn this informal system into a more cohesive mass transit solution.
Smart mobility for smarter cities
Even without natural disasters driving these services, many cities around the world encourage the use of Mobility as a service (MaaS). This lightens the load on public transport while reducing traffic volume and improving citizens’ ability to travel.
In Japan, big cities like Tokyo and Okinawa fought for years to improve mobility. Thus, platforms such as Fukurou Go and SkedGo integrate with municipal transit agencies and together provide simplified means of travel. Using predictive artificial intelligence and other intelligence, they guide travelers to the most convenient and appropriate mode of transportation. At the same time, they provide critical data to buses, rail lines and private services, concentrating resources where they are needed at any given time.
In essence, AI is not doing anything for public transportation that it is not doing for any other segment of society. The difference is that transportation is a crucial aspect of modern life and is under extraordinary pressure these days. Mainly, it satisfies the contradictory requirements to increase the number of passengers while reducing resource consumption and congestion.
Achieving these goals is largely beyond the capabilities of manual human-driven processes, but they are within the scope of AI.
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