Artificial Intelligence in the Transportation Industry

NB: We do not resell your papers. Upon ordering, we do an original paper exclusively for you.

NB: All your data is kept safe from the public.

Table of Contents

Transportation
Self-Driving Vehicles
Traffic Management
Delay Predictions
Drone Taxis
Shipping, Navigation, And Ports
Intelligent Train Automation
References

Transportation

Transportation, the business of moving goods and people from one location to another, has undergone several studies, research, experiments, and modifications to get to where it is now. In the year 1787, the steamboat became one of the most significant milestones in the history of transportation. Previously, people had to rely on animal-drawn carts to go about. Following that, key achievements in the transportation business included the introduction of bicycles in the early nineteenth century, automobiles in the 1890s, railroads in the nineteenth century, and airplanes in the twentieth century (Lǎzǎroiu et al., 2020). The transportation industry has progressed to the point where vehicles can navigate and move without the need for human intervention. The industry has benefited from technological breakthroughs in its quest for innovation and evolution. Artificial intelligence (AI) is one such cutting-edge technology that has benefited the industry. Leveraging AI in transportation helps the industry improve passenger safety, reduce traffic congestion and accidents, reduce carbon emissions, and lower overall financial costs.

AI has long since moved beyond its theoretical presence in research labs to become prevalent in people’s daily lives. And, for the most part, technology has succeeded in its objectives. In a nutshell, AI is a technology that encompasses machines with human intelligence (Verganti, Vendraminelli, & Iansiti, 2020). Machines with AI skills may imitate people, automate manual jobs, and learn on the fly like humans do. With the introduction of automation, repetitive and time-consuming jobs fall under the purview of AI. Furthermore, according to Verganti et al. (2020), AI-powered systems exhibit human intelligence and learn over time, implying that these machines will eventually be able to perform critical-thinking tasks and make decisions on their own. Businesses in the transport industry are making considerable investments to boost revenue production and remain ahead of their competition, recognizing the exceptional potential of Artificial intelligence.

Self-Driving Vehicles

Autonomous cars are one of the most innovative uses of AI innovation. These vehicles, which were previously only in science fiction, are now a practical reality. Although some people were suspicious of this technology during its early phases, autonomous cars have now entered the transportation industry. In Tokyo, self-driving taxis have already begun to operate (Yaqoob et al., 2019). However, for safety reasons, the driver now sits in the car to take control of the cab in the event of an emergency. According to the manufacturers of this driverless taxi, the technology will result in lower taxi service costs, thus increasing public transit options in rural places.

Similarly, the logistics industry in the United States is adopting autonomous trucks in order to gain several benefits. According to the McKinsey Global Institute, vehicles move 65 percent of products internationally (Lǎzǎroiu et al., 2019). With the introduction of self-driving trucks, maintenance and administration costs will be reduced by around 45 percent (Lǎzǎroiu et al., 2019). For the time being, most corporations are still undertaking pilot programs to make self-driving vehicles faultless and safe for passengers. As this technology advances, self-driving vehicles will acquire widespread acceptance and become commonplace in the consumer market.

Traffic Management

Another issue that individuals confront on a daily basis is traffic congestion. AI is now poised to fix this problem as well. Sensors and cameras placed on the road capture a massive quantity of traffic data. This data is then transferred to the cloud, where it will be analyzed and traffic patterns revealed using big data analytics and an Artificial Intelligence-powered system (Javaid et al., 2018). Data processing can provide valuable insights, such as traffic projections. Important information such as traffic forecasts, accidents, and road closures can be delivered to commuters. Furthermore, users can be alerted of the shortest path to their destination, allowing them to travel without having to deal with traffic (Abduljabbar et al., 2019). AI may thus be used to minimize undesired traffic, enhance road safety, and decrease wait time.

Delay Predictions

Flight delays are another major issue confronting air travel today. According to a study undertaken by experts at the University of California, Berkeley, the estimated cost of aircraft delays in the United States is 39 billion dollars (Sun et al., 2020). Flight delays, in addition to financial loss, have a detrimental influence on passengers’ travel experiences. Negative flying experiences can diminish the value of a transportation firm, leading to greater client attrition. To address these difficulties, AI comes to the aid of the aviation sector.

Using data lake technologies and computer vision, the sector can provide great service to customers by reducing wait times and improving their travel experience. Because everything from adverse weather to technological malfunction can cause aircraft delays, it is critical to provide flight data to passengers ahead of time to avoid excessive wait periods. According to Gui et al. (2019), continuous monitoring of airplanes may be carried out with the use of computer vision systems, reducing accidental downtime. Furthermore, Artificial Intelligence and machine learning components will analyze real-time flight data, historical records, and meteorological data. On-the-fly computation will aid in the discovery of hidden patterns, which will provide the aviation industry with important insights into other potential causes of aircraft delays and cancellations (Gui et al., 2019). This information may be provided to passengers, who can then organize their itinerary appropriately.

Drone Taxis

A drone taxi is one of the most intriguing and creative AI uses in transportation. According to Jat & Singh (2020), pilotless helicopters provide a novel approach to reducing carbon emissions, eliminating traffic congestion, and reducing the need for costly infrastructure investment plans Furthermore, Gui et al. (2019) state that drone taxis will enable passengers to get to their location considerably faster, reducing commuting time. Additionally, growing populations have put city planners under intense pressure to ensure wise urban planning and infrastructure construction while conserving scarce resources (Jat & Singh, 2020). Drone taxis may be the true solution to all of the issues that these municipal officials are attempting to address. The recent demonstration of an autonomous aerial vehicle in China, in which seventeen passengers enjoyed smart air mobility for the first time, is an excellent predictor of such future uses.

As many firms still employ vessels to convey products, the last several years have been critical for the growth of the maritime logistics industry. Waterway transportation necessitates the analysis of a large amount of data in order to optimize shipping routes for ships of various sizes and carrying differing sorts of products (Alop, 2019). Artificial intelligence in transportation software enables organizations to collect particular data that aid in decision-making, enhancing shipment safety and vessel energy efficiency.

Many variables influence marine logistics and navigation, making route planning difficult without a comprehensive collection of data. It may be more efficient to have the ship under control on shore. For example, the MUNIN project investigated an autonomous warship that navigated aboard but was controlled from land (Abaei et al., 2021). This initiative advances the possibilities for leveraging automation in the transportation business to improve ship navigation and quality marine delivery.

Intelligent Train Automation

The railway sector was once the most inventive mode of transportation, and it still can be with the application of AI to improve management and operational processes. A collection of firms began developing driverless train prototypes for autonomous freight and passenger trains in 2018 (Singh et al., 2021). The integration of sensors, cameras, and radars with artificial intelligence transportation software enables the creation of the “train’s eyes” tool. By 2025, the business hopes to have a fully automated driverless train.

It is necessary for railway infrastructure to forecast potential failures. Operational intelligence enables the utilization of data from railway sensors for precise forecasting and repair suggestions. Furthermore, AI assists the railway sector in assessing long-term performance and identifying opportunities for development. Laing O’Rourke, for example, utilizes AI to cut logistics planning time down to 19 seconds. They can prepare for 23 days thanks to AI and transportation software solutions (Singh et al., 2021). In comparison, a person could only plan maintenance work for three hours and one day in advance.

AI has been one of the most amazing technological advancements in human history. Despite every fantastic creation so far, it is crucial to recognize that people have just scratched the surface of AI and that much more is left to be studied. The uses of artificial intelligence in transportation outlined above are only a taste of the possibilities and opportunities that the technology may provide.

References

Alop, A. (2019). The main challenges and barriers to successful “smart shipping”.TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, 13(3), 523-526.

Abaei, M. M., Hekkenberg, R., & BahooToroody, A. (2021). A multinomial process tree for reliability assessment of machinery in autonomous ships.Reliability Engineering & System Safety, 210, 107484.

Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of artificial intelligence in transport: An overview. Sustainability, 11(1), 189.

Gui, G., Liu, F., Sun, J., Yang, J., Zhou, Z., & Zhao, D. (2019). Flight delay prediction based on aviation big data and machine learning.IEEE Transactions on Vehicular Technology, 69(1), 140-150.

Javaid, S., Sufian, A., Pervaiz, S., & Tanveer, M. (2018). Smart traffic management system using the Internet of Things. In 2018 20th International Conference On Advanced Communication Technology (ICACT) (pp. 393-398). IEEE.

Jat, D. S., & Singh, C.. (2020). Artificial intelligence-enabled robotic drones for COVID-19 outbreak. In Joshi, A., Dey, N. & Santosh, K. C. (Eds.) Intelligent systems and methods to combat COVID-19 (pp. 37-46). Springer.

Lǎzǎroiu, G., Machová, V., & Kucera, J. (2020). Connected and autonomous vehicle mobility: Socially disruptive technologies, networked transport systems, and big data algorithmic analytics. Contemporary Readings in Law and Social Justice, 12(2), 61-69.

Sun, B. J., Huebner, C., Treidel, L. A., Clark, R. M., Roberts, K. T., Kenagy, G. J., & Williams, C. M. (2020). Nocturnal dispersal flight of crickets: behavioral and physiological responses to cool environmental temperatures.Functional Ecology, 34(9), 1907-1920.

Singh, P., Dulebenets, M. A., Pasha, J., Gonzalez, E. D. S., Lau, Y. Y., & Kampmann, R. (2021). Deployment of autonomous trains in rail transportation: Current trends and existing challenges.IEEE Access, 9, 91427-91461.

Verganti, R., Vendraminelli, L., & Iansiti, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3), 212-227.

Yaqoob, I., Khan, L. U., Kazmi, S. A., Imran, M., Guizani, N., & Hong, C. S. (2019). Autonomous driving cars in smart cities: Recent advances, requirements, and challenges.IEEE Network, 34(1), 174-181.

Click Here To Order Now!