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How Will AI Impact the Transportation Industry?

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Artificial intelligence (AI) is at the root of many new services and is helping to advance a wide range of technologies and fields. Transportation is one of these fields. When AI is applied, transportation can become faster, more efficient, more reliable, and safer. It enables drivers and engineers to automate tasks and can help support decision making from individual to global scales.

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Currently, AI is being developed for both commercial and private transportation markets. Although incorporating AI is costly, as integrations and applications are refined, the cost of inclusion is sure to drop. Soon, AI may be an integral part of daily commutes, deliveries, and transportation infrastructures.

How AI Is Transforming the Transportation Industry

AI is transforming the transportation industry in some surprising ways. Below are a few of the most promising innovations.

Self-driving Vehicles

One of the best known, and perhaps most anticipated, applications of AI is in the form of autonomous vehicles. Autonomous, or self-driving, vehicles were once a thing of pure science fiction but in recent years have been implemented with varying success.

As of now, most companies developing this technology are still running pilot projects and working to refine the technologies that enable safe automation. For example, deep learning for computer vision. However, several vehicles have already been released to consumers that contain autonomous capabilities, such as the ability to self-park or maintain control on straightaways.

Commercialization of this technology has also occurred with autonomous taxis in Tokyo. As of now, a driver is still present in these taxis to take control if needed, however, in the future, this could change.

Traffic Management

Traffic management is a significant infrastructure concern for many cities. Poor management can lead to higher numbers of accidents, wasted resources, and longer commutes for everyone.

However, by applying AI cities can work to improve their management, dynamically adapting to current demands and conditions. This can be extended even further with the adoption of smart mobility services, which can help improve both public and private transportation services.

This integration is possible through the addition of traffic sensors, cameras, and data collected from a variety of other Internet of things (IoT) devices. This data can be fed to analytics and AI platforms to predict changing traffic patterns, direct vehicles to less congested routes, or to focus public services, like police, to areas requiring intervention.

Sustainable Transportation

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Consumers are becoming more aware of the impacts of transportation on the environment and the effects of pollution are increasingly difficult to deny. As a result of this, many consumers and cities are interested in more sustainable means of transportation.

For example, electric or solar-powered vehicles. While these technologies are possible without AI, vehicles can work more efficiently when recharging and energy expenditure is controlled with AI.

The development of smart roads is another application of AI that can increase the sustainability of transportation. For example, China has already begun piloting smart highways that use AI to charge electric vehicles during travel.

Other smart roads are incorporating additional technologies, such as solar panels or sensors used for AI traffic management. This enables formerly single-use areas to generate sustainable resources and increases resource efficiency.

Fleet Integration

Many industries require organizations to operate large fleets of vehicles. This requires fleet managers to monitor and orchestrate a large number of moving parts. Dispatchers need to be able to track and communicate with drivers. Mechanics need to track and perform repairs. Administrative teams need to ensure that vehicles are properly licensed and insured.

This orchestration takes significant time and effort but can be made simpler with AI. AI systems can help manage fleets by providing real-time updates to all members of a fleet team.

For example, notifying dispatchers when deliveries are made or mechanics when tire treads are low. Many of the manual tasks required to organize a fleet can be automated away, reducing the amount of oversight needed and freeing employees to focus on more complex tasks.

Crewless Cargo Ships

There are almost 17,000 cargo ships used to transport goods across the globe. These ships represent a massive industry worth hundreds of billions of dollars. It should be no surprise then that some industry innovators are looking for ways to improve their efficiency and lower their costs with AI.

Rolls Royce is one such example. In a partnership with Google, the vehicle manufacturer developed autonomous ships that are scheduled to be released this year. These ships integrate AI for navigation, engine performance monitoring, and loading and unloading.

Challenges of AI in Transportation

Despite its potential, there are some challenges transportation industries need to overcome before AI can be fully adopted. Three common challenges industries face are:

Cost of Adoption

Integrating AI technologies is not cheap, particularly if you do not have in-house developers and engineers. The various sensors, transmitters, and computing resources needed to implement AI can also be expensive. This makes AI cost-prohibitive for many organizations, making them less likely to adopt AI technologies until prices drop significantly.

System Reliability

Many AI systems are still far from perfect. Proof of this can be found in the multiple accidents that have occurred involving autonomous vehicles. Until these imperfections are resolved or otherwise accounted for, organizations need to spend time and effort implementing safeguards. This extra requirement deters many organizations for the time being.

Job Flow

AI can greatly increase system efficiency but often at the expense of human jobs. While some companies may not care about this threat, replacing mass numbers of workers with AI is not a sustainable system and eventually leads to increased customer engagement and employee resentment. For AI to be implemented sustainably, industries need to find ways to shift labor, not replace it completely.

Cloud Security Challenges

Transportation data is highly sensitive, and may expose private information or enable attackers access to transportation systems, with catastrophic consequences. Many AI initiatives rely on cloud technology, making this data much easier to compromise. AI initiatives must take cloud security seriously, and ensure cloud resources and datasets are properly configured and comprehensively monitored.

Conclusion

AI is transforming many industries, and transportation is no exception. It is AI that powers self-driving vehicles, which could help reduce the amount of traffic accidents. AI is applied in many more transport fields, like traffic management, sustainable transport, fleet integration, and crewless cargo ships.

Even at times of great uncertainty, transportation remains a fixed necessity in human life. You need transportation for shipping cargo from one country to another. If you’re not doing export, you need to ship locally. In western societies, there are hardly any households that are completely self-sustaining. It is transportation that delivers goods from supplier to customers. And it is AI that can help make the process much more efficient, supporting human lives throughout the globe.

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