Generative AI is about to enter the transportation industry, are you ready?

The transportation industry is a multimodal global transportation network system for people and goods, with a total value of up to 10 trillion US dollars. But today, the industry is facing a host of external and internal challenges: subsidies, network fragmentation, competition among transport modes, and growing congestion, emissions, safety, and more. Outdated government policies have led to inefficiencies, and traditional technology approaches have made incremental progress in specific areas but have yet to achieve widespread transformation. This stems in part from the inherent limitations of the transportation industry, but is also driven to a large extent by changes in public opinion and behavioral patterns.

The entire transportation industry is currently in a mess—from excitement to frustration, and from convenience to cost, people don’t know where to start. Therefore, guiding policy changes and technological progress has posed a serious challenge, requiring policymakers and enterprises to not only work hard to alleviate the public transportation cost burden (it turns out that transportation costs often rank second in total household expenditures), but also have to deal with industry within a series of conflicting visions, rein in rapidly rising transportation costs and adhere to strict review requirements.

Another exciting news is that a new wave of innovation may close this gap. Generative AI has the potential to effectively combine policy and technology to reshape and optimize the way we transport people and goods.

What is special about generative AI for the transportation industry?

Unlike traditional predictive techniques that focus on analyzing existing data in closed systems, generative AI can delve deeper into thinking and creation layer, enabling real-time visualization and then providing support in multiple ways at different times and locations. Generative AI can also provide better accessibility to different user groups from different backgrounds, including vehicle designers, urban planners, community advocates, policy makers, and business practitioners. This good accessibility brings information, access, and collaboration to unprecedented new heights.

Most people are not familiar with policy documents and professional terminology, nor do they know how to interpret a two-dimensional design, building or construction plan, site plan, or color-coded community map. However, it is easier for people to understand information through images or videos accompanied by voice. With the help of powerful algorithms and generative artificial intelligence, it can analyze small data sets and generate new real data, enabling the display of real-time images and videos to display the surrounding environment and related perceptions to people of all levels.

Gone are the days of simply designing for two or three potential scenarios. Soon, different teams and communities will come together to plan dozens of scenarios for how neighborhoods, transit vehicles, services or stations will operate based on shared values ​​and expectations. Such design results are very different from people's original ideas, and new solutions often involve a large number of important variables that people have never thought of.

Imagine that AI can not only process data on traffic patterns, but also build a simulation system of future conditions based on historical data, weather forecasts, personal and cultural preferences, and real-time trends. This ability to create new things from existing things around it is the premise and foundation for generative AI to shine in the transportation industry.

Generative AI is being widely used in different fields, demonstrating its versatility and potential. The transportation industry is likely to be the next important application area for this technology.

Unique Properties of Generative AI in the Transportation Industry:

• Beyond personalized experiences from A to B: Generative AI is creating more granular personalized routes for drivers and passengers while optimizing Experience in road network driving, travel insurance and travel communication. This will effectively reduce travel time and fuel consumption, reduce operating and insurance costs, and increase the safety potential of the road network. Generative AI can also provide personalized out-of-car and in-car experiences, providing suggestions for next steps based on the user’s preferences, such as recommending routes with better scenery or scenic loops, and even personal driving, cycling and walking styles. Generate customized travel and surrounding historical and cultural information.
• Enhanced Safety: Generative AI can assist in taking proactive measures by predicting potential issues such as traffic accidents or mechanical failures in high-risk areas based on sensor data. Not only is this in line with the Zero Emissions vision, it will also help prevent disruption and improve overall network operational efficiency.
• Improving efficiency: By analyzing various data points, generative AI can make predictions before infrastructure and vehicles need maintenance. The preventive measures taken thus help eliminate breakdowns and unplanned downtime, ensuring that people and goods are transported to their destinations in a safer and more reliable manner.
• Dynamic Optimization: Generative AI can optimize transportation networks in real time by analyzing traffic (personal and commercial vehicle) data, crosswalks, and emergency vehicle locations while understanding the context of real-time events as they occur (such as upcoming major events) , temporary road closure plan, etc.).
• Data-driven design: Generative AI can build detailed 3D models of the entire transportation system (including vehicles, intersections, streets, communities and even the city as a whole), thus going beyond traditional simulation scenarios. This will help city planners virtually test the real-world impact of new projects, infrastructure projects, street traffic calming measures, pedestrian walkways or commercial loading zones, and parking management strategies on all supporting infrastructure before they start work. Unlike traditional pilot projects, generative AI can run dozens of simulations simultaneously, taking into account factors such as environmental impact, energy efficiency, resiliency and material waste minimization. This more comprehensive approach helps identify potential issues and optimize the design beforehand, thereby reducing the risk of unforeseen problems and costly modifications later on.

Companies are using generative AI to improve the readability of design plans through visualization and video.

Considering the unique functional attributes of generative AI, this technology is also expected to bring unprecedented novel applications to the transportation system:

• Road blocks: dynamically adjust traffic lights, optimize Lane usage and alternative route suggestions to alleviate congestion in real time.
• Public transportation: Forecast future demand and optimize timetables and electric fleets, 3D visualization, secure power supply and reduce waiting times.
• Aviation: Recommend energy-saving routes while minimizing contrails, and seize the industry's transformation period to achieve double reductions in operating costs and emissions.
• Logistics and Distribution: Forecast demand, set up virtual loading zones and optimize your fleet, using a variety of options such as trucks, cargo bikes and drones to achieve efficient and timely delivery while minimizing impact on communities and road networks .
• High Speed ​​Rail: Anticipate potential maintenance needs, minimize disruption and improve safety for passengers and staff.
• Marine transportation: Optimize the cargo loading and unloading process at the port, minimize the surrounding time and recommend energy-saving routes for ships.
• Construction: Create 3D models of construction projects to optimize workflow, identify potential challenges and improve safety planning.
• Mining: Design optimal mining routes to maximize resource extraction while minimizing negative impacts on the environment.
• Waste Management: Optimize collection routes based on the real-time capacity of trash bins, striving to improve collection efficiency and reduce environmental impact.

Generative AI has taken root in various fields in the transportation industry.

These are just a few examples of the many potential applications. We can imagine a transportation system that can seamlessly adjust traffic flow, perform predictive maintenance before failures occur, and provide a customized commuting experience for each traveler. Generative AI is one such powerful emerging technology that has shown great potential in optimizing passenger and freight transportation. Although it is still in the early stages of development, it also means that we are just scratching the surface of the possibilities of generative AI. In addition to optimizing daily operations, it is believed that generative AI will also become a game changer in shaping the future of transportation.

But realizing this potential requires not only technology itself, but also a new approach that puts people first. We need to understand both the “effect” of generative AI (how to optimize traffic routes) and the “reason” behind it (how it will affect our lives). In order to better control this coming wave of AI, we should start from the following angles to prepare for the application of generative AI in the field of transportation:

Take responsibility for data: there is no AI without data

• Advocate for data governance: Advocate for a strong data and AI policy framework to ensure responsible data collection, storage and use practices.
• Invest in data security: Protect sensitive data from leakage and misuse through strong security measures, including how team members use data at work and at home.

Skills development and empowerment:

• Cultivate employee skills: Provide employees with training in data analysis, AI collaboration, and technical ethics. Of course, it’s not possible for everyone to become a computer scientist, but we should all draw more inspiration from skills from a humanities background.
• Pay attention to data literacy: Encourage a broad understanding of how data is collected, used and protected, and pay attention to the corresponding specific impacts.

Promote innovation and collaboration:

• Expand investment in training: Support responsible and ethical AI-related skills training programs for teams and stakeholders.
• Promote transparency: Promote open communication about AI implementation to answer public concerns and build broad trust.
• Encourage pilot projects: An experiment is worth more than a thousand hypotheses. Pilot projects can unlock the huge potential of generative AI by refining ideas into implementation strategies.

The popularization of generative AI in the transportation field has begun - are you ready?

The various potential use cases and scenarios discussed in this article are only the application of generative AI in the transportation field A touch of silhouette may be applied. As this emerging technology develops and matures, more practical solutions will be available to everyone. Although there are still some challenges that need to be solved, generative AI does show great potential in creating new forms of greener and more equitable transportation, just waiting for us to turn this into reality.

By actively embracing the inherent limitations and application potential of generative AI, I believe we can cooperate with each other and guide it to maximize its value. We must also harness this force that is about to sweep the world in a responsible manner to ensure that generative AI becomes a positive change factor in transportation. As long as we can put aside differences and jointly shape a development concept based on trust and responsibility, we will surely be able to make good use of AI tools to fill in the important puzzle of transportation for the common vision of building a better tomorrow.

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