Role of AI in Transportation and Its Effect on the Distribution of COVID-19 Vaccine Testing out page editing
Artificial intelligence (AI) is gaining global interest and recognition progressively as it is providing monumental solutions that are transforming and reinforcing diverse industries around the world. In 2025, it is expected that the global AI industry will grow rapidly reaching a market value of $190.61 billion (Markets and Markets). Furthermore, AI Contributions to the transport sector are becoming of great importance. Most recently, AI has helped in the development of effective Covid-19 vaccination delivery systems. It assisted in addressing the logistical challenges associated with vaccine distribution. This brings us to the question of what is Artificial intelligence and how it will impact the future of transportation. As explained by Shroff, AI involves using computers to do things that traditionally require humanintelligence. This means creating algorithms to classify, analyze, and draw predictions from data. It also involves acting on data, learning from new data, and improving over time. AI is a broad term, it is a collection of various approaches, methods and technologies that can do logical reasoning, problem solving and learning in different contexts. Lately, machine learning has been integrated with technologies that are used for finding and evaluating massive amounts of data generated by the world’s digital growth. Moreover, communication networks, Internet of Things (IOT) and transport devices progressed greatly. These developments have contributed to the enhancement of AI, especially in the transport sector. In Beijing, China, AI has been used in predicting the risk of traffic accidents by analyzing the spatial and temporal patterns from a traffic accident database and using deep recurrent neural network approach. It has also been discovered that machine learning techniques (k-Clusters and priori algorithm) can identify valuable hidden patterns from a vehicle crash dataset of historical accidents which in one way can predict risk of crashing and so can help in avoiding a possible accident.
Artificial intelligence is modernizing the transport sector. It is already helping different transport devices such as cars and airplanes to work autonomously. It can make traffic congestion flow smoother by analyzing traffic patterns and optimizing route of delivery. It has the potential to make all means of transportation safer, cleaner, smarter, and more efficient, in addition to making our lives easier. However, these advantages come with actual risks, like unforeseen effects and misuse, such as cyber-attacks and biased transportation decisions. There are additional implications for employment, as well as ethical concerns about liability for decisions being made by artificial intelligence in the absence of people.
AI-assisted simulations can take into account input factors that can help in wide scale vaccine distribution such as mobility data, current case levels, and hospital implementation rates. Vaccinations for COVID-19 have already been distributed to millions of people around the world. In the US and according to Our World in Data, over 61 per cent of the population have had their first dose, and 52 percent have been fully vaccinated. While in Turkey, 58.1 per cent got their first dose and around 44 per cent are fully vaccinated. AI technology is providing an ideal delivery path for vaccines when transported by planes and trucks, predicting weather as well as land or air traffic using historical data to estimate the exact timing of arrival in certain locations. Data can be collected from multiple sources ranging from sensors on the road, connected devices, toll gantries, GPS to Cloud applications and more. These sources store big data on different transportation features. Being able to transport the vaccine in the fastest and least error prone way is crucial when it comes to solving the logistical challenges of COVID-19 vaccine distribution.
To keep the vaccine effective, it must be stored in a cold place and in a suitable temperature range. It is critical that the temperature is closely maintained, a 2019 research estimated that 25% of vaccinations are usually damaged by the time they arrive at their required destination. As a result, Pfizer created a transfer container that contains GPS trackers and a small card size AI device to track the moisture, temperature, and checkpoint location throughout the journey. For the vaccine to stay within the required temperature range, This device can as well share the temperature and transmit it to any technological device, as long as it has Bluetooth. With this technology, the box does not need to be checked by a person at any point until the delivery time.
When it comes to the distribution of the COVID-19 vaccine, there are several obstacles, but they have created an unparalleled need for AI-based solutions. Ultimately, billions of individuals throughout the world will require at least one dosage, making logistics and transportation more important than ever. As our cities and transportation systems grow, much-needed data is being provided for AI application development. The aforementioned examples of AI in transportation represent just a small portion of the possibilities and opportunities that the technology may provide. It is expected that the spectrum of AI applications in transportation is projected to expand, however there is no consensus on the exact timing of such improvement.
Date: 06.09.2021
Author: Naya Shahin (Jr. Software Developer, TAZI.AI)
References:
[2] https://www.europarl.europa.eu/RegData/etudes/BRIE/2019/635609/EPRS_BRI(2019)635609_EN.pdf
[3] https://medium.com/mytake/artificial-intelligence-explained-in-simple-english-part-1-2-1b28c1f762cf
[4] Ren, H.; Song, Y.; Wang, J.; Hu, Y.; Lei, J. A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction. In Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 4–7 November 2018.
[5] Vasavi, S. Extracting Hidden Patterns Within Road Accident Data Using Machine Learning Techniques. In Information and Communication Technology; Springer: Singapore, 2018; pp. 13–22.
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