How AI/ML is impacting the transportation industry

• Intro

• How AI/ML is used in transportation today

• Benefits of AI/ML in transportation

• Issues with AI/ML in transportation

• Future of AI/ML and transportation

• Conclusion

Believe it or not, there was a time in history when there was no form of transportation and in which humans were forced to travel on foot. Not that there’s anything wrong with traveling by foot, we can all use a good walk from time to time; it just isn’t the most efficient nor the easiest way of getting to places. Just like most great inventions, it all began with a need — a need to travel faster, further, longer, and eventually for exploration and transporting goods for trade and so on. It was as early as 4,000 B.C., when humans began using animals such as oxen, donkeys, camels and horses to ride and carry goods for trade. Eventually, wooden wheels were invented in 3,500 B.C. and then the creation of boats in 1,500 B.C. for the primary purpose of warfare, trade, and piracy. All of which allowed for humans to go faster, further, and longer than ever before.

Fast forward to the 21st century and we now have everything from cars to boats, to trains and airplanes, and even rocket ships (

, 

, 

). All these forms of transportation once required human intelligence, such as visual perception and decision-making. Nowadays, with the advancement of technology and AI, we are beginning to see the shift from tools for driver assistance such as GPS navigation, cruise control, and real-time traffic updates, to autonomous driving capabilities with companies such as 

 and 

 leading the charge.

Public transportation has also improved the overall quality, safety, and efficiency through the application of AI and ML technology. Through deep learning, ML has been used to look at complex issues regarding activities of traffic, environmental elements, accidents, and potential to resolve these problems through data collection. 

, the main sponsor for the 

, announced that their specially-designed 

 would provide automated (driverless) ferries for athletes around the Olympic Village. These vehicles would operate as buses with pre-set routes, each with an operator to monitor the process for safety reasons. Though not fully autonomous, this comes as one of the largest self-driving experiments to date and paves the way for the emerging technology.

In recent years, we have seen an increased interest in applying AI and ML to the transportation sector in order to address issues pertaining to traffic, public and pedestrian safety, parking, and even the potential to improve air quality. Using AI/ML can have positive impacts on improving traffic, parking, reducing stress and anxiety on the road, while encouraging rideshare (

, 

), and carpooling to reduce vehicle emissions.

Other benefits include improvement to public and private transportation service sectors such as scheduling improvements and the potential to reduce production, labor, and overhead costs. This could also lead to new revenue streams in the areas of self-driving technology, predictive maintenance, and route optimization, while saving the environment with less emissions and push towards electric vehicles.

We’ve definitely come a long way from relying on donkeys and camels as our primary form of transportation, but there are still some issues we face when it comes to AI integration to transportation. Among the top barriers for AI adoption for businesses commonly are cost, data infrastructure, and the lack of expertise. AI technology, as of now, still requires human assistance and control, and rarely can run as an independent system. Getting the right talent in-house can become quite expensive. Even with the substantial amount of data available, it’s still difficult to detect errors and there are many issues to implementation due to lack of infrastructure in certain areas and regions.

There have also been concerns over security and the vulnerability to cyber attacks. This will require high-quality security systems, adding to the costs of adoption. Other related concerns involve loss of jobs for those in the transportation industry such as truck and taxi drivers.

Though we have yet to hear of talks on time travel, there are high hopes and much anticipation for fully autonomous driving vehicles in the near future. Over time, AI will be able to mimic humans, automate tasks and learn dynamically through ML. It’s then, when this new trend of 

 will emerge as a widely accepted mode of transportation. This will bring about many benefits such as improved ridership habits, transit network efficiency, decreased costs to the users, improved utilization of MaaS transit providers, reduced city congestion, and reduced emissions as more users rely on public transit and battery powered components.

AI/ML is already a huge focus within the transportation industry, among many other industries we’ve covered (see previous blogs 

, 

, 

, 

, etc.). Car companies such as 

, 

, and 

 have already begun their efforts towards AI integration with their vehicles working to launch their fleet of autonomous cars. As we move forward towards a data-driven future, it won’t be long before we see the increased improvements in the various areas of transportation, influenced by AI/ML technology.

Is cost or lack of expertise major barriers to your AI/ML adoption? Contact us at 

 and we can discuss how can help you find solutions and achieve your goals through AI/ML technology.