Introduction

The evolution of transportation dates back to ancient times when people traversed regions on foot. Around 4000 BC, camels and horses emerged as key modes of transport, and 500 years later, one of humanity's greatest inventions surfaced – the Wheel. This transformative period saw the emergence of various transport modes, including boats, chariots, and advanced technologies like satellites and space links. As we fast forward to the present, with continuous technological growth and innovative minds at work, the future holds endless possibilities.

In the contemprory, transportation preferences vary based on modes and personal choices. Most people, given a choice, prioritize reaching their destinations quickly and comfortably. Locally, cars and trains stand out for their comfort and efficiency, while for longer distances, airlines often top the list, influenced by factors such as travel class. These transportation choices mirror societal investments in time. In our fast-paced world, where time is equated with money, efficiency becomes paramount, and every second holds a potential economic value.



In this fast paced daily life, particularly for those engaged in frequent travel, the preference for air travel becomes apparent. Airlines serve as a crucial bridge connecting people to their desired destinations, becoming an integral part of life. However, amidst the tremendous advancements and increased dependence on flights, the issue of flight delays has also risen. Not everything is perfect in this world; some factors are beyond control, such as weather and natural disasters. Yet, imagine if we could predict flight delays. Wouldn't that alleviate some pressure and last-minute tension? Wouldn't it provide the opportunity to explore alternate options beforehand and potentially save time and money? Imagine a world where flight delays could be predicted with pinpoint accuracy, allowing passengers to adjust their plans accordingly and minimize the disruption caused by unforeseen delays. As discussed earlier, not all delays can be anticipated and predicted.  There are 4 different types of major delays. One is weather delay - a delay due to harsh weather like heavy snow or a very humid climate so that the flight may take off late or land late. The other type of delay is air traffic congestion commonly known as NAC delay, which occurs when there is a high volume of aircraft attempting to use the same airspace or airport infrastructure simultaneously. 

This congestion can lead to delays in departure and arrival times as air traffic controllers work to safely manage the flow of aircraft. Factors such as limited runway capacity, airspace restrictions, and peak travel times can exacerbate congestion, resulting in longer wait times for passengers and increased stress for airline staff. Additionally, technical issues with aircraft or airport equipment can cause delays. These issues may include mechanical malfunctions, computer glitches, or problems with ground support equipment. While airlines strive to maintain their fleets in peak condition and invest in state-of-the-art technology, unforeseen technical issues can still arise, leading to unexpected delays and disruptions for passengers. Finally, operational delays can occur due to factors such as crew scheduling issues, staffing shortages, or logistical challenges. These delays may not be directly related to weather or technical issues but can still have a significant impact on flight schedules. For example, if a flight crew becomes unavailable due to scheduling conflicts or regulatory limitations, it may result in delays or cancellations. There are some other delays like security delays and connecting flight delays which also cause potential flight delays.

Such a scenario may seem like a distant dream, but with recent advancements in predictive analytics and machine learning, it is becoming increasingly achievable. By analyzing vast amounts of data, including historical flight patterns, weather forecasts, and air traffic data, researchers are developing algorithms capable of forecasting flight delays with remarkable accuracy. The potential benefits of such technology are profound. Not only could it help passengers avoid the frustration and inconvenience of unexpected delays, but it could also enable airlines to better manage their operations and optimize their resources. By proactively identifying potential delays and implementing contingency plans, airlines could minimize the impact of disruptions and ensure a smoother travel experience for all passengers. Of course, predictive technology is not a panacea for all the challenges facing the transportation industry. Weather patterns can be unpredictable, and unforeseen events can always occur. However, by harnessing the power of data and technology, we can take proactive steps to mitigate the impact of these disruptions and create a more efficient and resilient transportation system for the future. In doing so, we can ensure that the promise of modern transportation – to connect people and cultures across the globe – is fulfilled more reliably and consistently than ever before. 


Motivation: 

The motivation behind this project lies in the observation of frequent air travel in today's fast-paced world. Flights often experience delays due to various factors, including weather conditions and related issues. The goal is to develop a forecasting model capable of predicting potential airline delays, empowering travelers to plan their trips more effectively. This proactive approach not only assists individuals in making informed decisions but also provides them with the opportunity to consider backup options, ensuring a smoother and more reliable travel experience.

Previous work done:

This research explores various data-driven approaches for predicting flight delays caused by weather conditions. It compares the effectiveness of different machine learning models, including Support Vector Regression (SVR) and Recurrent Neural Networks (RNNs), in achieving accurate delay predictions.

This paper proposes a novel approach called Flight Delay Path Previous-based Machine Learning (FDPP-ML) for predicting individual flight delay minutes. It utilizes a supervised learning model and focuses on readily available flight schedule features, achieving improved accuracy compared to traditional and deep learning models.

This journal is a study analyzing Brazilian domestic flights (2009-2015) and found that weather significantly impacts arrival times. Factors like visibility, temperature, wind speed, and precipitation all contributed to delays, with severe weather having a particularly strong influence. The research suggests that different airports exhibited varying levels of vulnerability to weather disruptions, highlighting the need for weather considerations in managing delays and improving the system's resilience.

questions addressed/ to be addressed: