The multiple applications of AI in transport

Artificial intelligence has become so deeply ingrained in our daily lives that its presence is no longer a source of wonderment. The transport sector is a case in point. Business operators are leveraging big data and technologies such as machine learning and neural networks to optimize routes, reduce costs and enhance safety while improving customer experience. This article discusses some of the ways that AI is being used in the transport industry. It also looks at how the international standardisation committees are supporting innovation.

Autonomous vehicles

Autonomous vehicles are one of the most significant applications of AI in transport. These vehicles use a combination of sensors, cameras and machine-learning algorithms to navigate roads and make decisions without human intervention. Machine learning algorithms analyze vast amounts of data collected by sensors and cameras to identify patterns and make decisions based on those patterns.

For example, machine learning algorithms can recognize traffic patterns and adjust vehicle speeds to optimize fuel consumption and reduce emissions. Additionally, autonomous vehicles can use neural networks to process large amounts of visual data in real-time, enabling them to detect and avoid obstacles, pedestrians and other vehicles.

Traffic prediction and management

AI can be used to predict traffic patterns and optimise traffic flow. Traffic prediction algorithms analyse vast amounts of data collected by sensors installed on roads, public transit systems and other transport infrastructure. This data is then used to predict traffic patterns and identify potential bottlenecks.

Machine learning algorithms can analyse historical data to identify patterns and predict future traffic volumes. This information can be used to optimise traffic flow by adjusting traffic signals, rerouting vehicles and managing public transit schedules.

Predictive maintenance

AI can be used to perform predictive maintenance by analysing data collected by sensors installed on vehicles and infrastructure. Machine learning algorithms can analyse this data to identify patterns that indicate when maintenance is required. This can help reduce downtime and prevent breakdowns, improving safety and reliability.

For instance, machine learning algorithms can identify the patterns of vibration and temperature that indicate that a vehicle's engine is likely to fail. This information can be used to schedule maintenance before a failure occurs, reducing costs and improving safety.

Supply chain management

AI can optimize logistics operations, including route planning, delivery scheduling and inventory management. Machine learning algorithms can analyze vast amounts of data collected by sensors and GPS trackers to optimize routes, schedules and inventory levels.

For example, machine learning algorithms can analyze traffic patterns, weather conditions and other data to identify the most efficient route for a delivery truck. Additionally, neural networks can analyse data from multiple sources to provide real-time information about the availability of inventory and the status of deliveries.

Customer service

AI-powered chatbots can help answer customer queries and provide real-time information on transport schedules and delays. Chatbots can analyse large amounts of data to provide customers with the most relevant information and answer questions about transport services.

For example, chatbots can use natural language processing algorithms to understand customer queries and provide real-time information on public transit schedules, fares and delays. Additionally, chatbots can analyse customer feedback to identify areas for improvement in transport services.

Energy efficiency

AI can optimize energy consumption in transport, such as route optimisation to reduce fuel consumption and emissions and smart charging systems for electric vehicles. Machine learning algorithms can analyse data such as traffic patterns, weather conditions and vehicle performance to optimize routes and reduce fuel consumption.

For example, machine learning algorithms can analyse data collected by electric vehicle charging stations to predict demand and optimise the use of charging stations. Additionally, neural networks can analyse data from multiple sources to predict weather conditions and adjust energy consumption accordingly.

International standards

International standards are crucial for enabling current and upcoming innovations in various industries, including the transport sector. These publications cover not only technical aspects but also non-technical requirements, such as regulatory and policy issues, business considerations, ethical and societal concerns, and application domain needs. Recent ISO/IEC publications cover topics such as AI-related risk management, bias in AI, big data analytics, computational approaches, ethics, governance and ML classification models.

These examples are based loosely on the use cases presented in ISO/IEC TR 24030:2021. The technical report published in 2021, includes over 130 AI use cases, from 24 application domains, including transport.