With recent studies related to Neural Networks being used on different forecasting and time series investigations, this study aims to expand these contexts to ferry passenger traffic. The primary objective of the study is to investigate and evaluate an LSTM-based Neural Networks' capability to forecast ferry passengers of two ports in the Philippines. The proposed model's fitting and evaluation of the passenger flow forecasting of the two ports is based on monthly passenger traffic from 2016 to 2022 data that was acquired from the Philippine Ports Authority (PPA). This work uses Mean Absolute Percentage Error (MAPE) as its primary metric to evaluate the model's forecasting capability. The proposed LSTM-based Neural Networks model achieved 72% forecasting accuracy to the Batangas port ferry passenger data and 74% forecasting accuracy to the Mindoro port ferry passenger data. Using Keras and Scikit-learn Python libraries, this work concludes a reasonable forecasting performance of the presented LSTM model. Aside from these notable findings, this study also recommends further investigation and studies on employing other statistical, machine learning, and deep learning methods on forecasting ferry passenger flows.

Accurate prediction of flight-level passenger traffic is of paramount importance in airline operations, influencing key decisions from pricing to route optimization. This study introduces a novel, multimodal deep learning approach to the challenge of predicting flight-level passenger traffic, yielding substantial accuracy improvements compared to traditional models. Leveraging an extensive dataset from American Airlines, our model ingests historical traffic data, fare closure information, and seasonality attributes specific to each flight. Our proposed neural network integrates the strengths of Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), exploiting the temporal patterns and spatial relationships within the data to enhance prediction performance. Crucial to the success of our model is a comprehensive data processing strategy. We construct 3D tensors to represent data, apply careful masking strategies to mirror real-world dynamics, and employ data augmentation techniques to enrich the diversity of our training set. The efficacy of our approach is borne out in the results: our model demonstrates an approximate 33\% improvement in Mean Squared Error (MSE) compared to traditional benchmarks. This study, therefore, highlights the significant potential of deep learning techniques and meticulous data processing in advancing the field of flight traffic prediction.

Incorporating passenger pickup and drop-off data from 4,425 taxies, in addition to time of day and weather factors, NTT Docomo trained its AI technology over an 18-month period to predict where potential passenger concentration will be most active within the next 30 minutes. When a Shogi (chess) robot used AI to defeat a master player in Japan recently, it sent shockwaves through the country's media. Elon Musk says "humanity risks summoning the demon with AI," and stressed its development must proceed with a strict set of checks and balances. Musk says "humanity risks summoning the demon with AI," and has stressed its development must proceed with a strict set of checks and balances, something that is not happening now.

One taxi driver said he had noticed a 20 percent rise in passenger traffic since using the AI prediction system. In downtown Tokyo, just like in most major cities, taxi drivers make an educated guess as to where they might find their next paying passenger. Years of experience have honed their prediction skills to pinpoint customers depending on location, the time of day and weather. But even with this knowledge, tracking down passengers is still a hit and miss science with many drivers saying they can cruise around for up to two hours without finding a fare. Japan's biggest mobile phone operator, NTT Docomo, wants to change all that.

When autonomous vehicles are powered by artificial intelligence engines, individuals traveling to or from the world's busiest airports will no longer experience uncertainty. Jen-Hsun Huang, CEO of Nvidia, recently told the WSJDLive Conference that he would like his car to not just drive him to work, but to recognize who he is, set up his conference calls, and handle just about all the functions of a personal assistant. In the near future, personal artificial intelligence engines will read your emails, create travel itineraries, and summon your autonomous vehicle--all without you having to ask. This knowledge, combined with real-time traffic and route data, will allow your personal artificial intelligence engine to pre-summon an autonomous vehicle for your journey to ensure that you arrive on time. In particular, with the introduction of personal artificial intelligence (A.I.) engines and on-demand autonomous vehicles, the uncertainty of traveling to and from major international airports will be eliminated, and travelers will experience effortless commutes.

Jen-Hsun Huang, CEO of Nvidia, recently told the WSJDLive Conference that he would like his car to not just drive him to work, but to recognize who he is, set up his conference calls, and handle just about all the functions of a personal assistant. In the near future, personal artificial intelligence engines will read your emails, create travel itineraries, and summon your autonomous vehicle--all without you having to ask. This knowledge, combined with real-time traffic and route data, will allow your personal artificial intelligence engine to pre-summon an autonomous vehicle for your journey to ensure that you arrive on time. In particular, with the introduction of personal artificial intelligence (A.I.) engines and on-demand autonomous vehicles, the uncertainty of traveling to and from major international airports will be eliminated, and travelers will experience effortless commutes. Anyone who has ever departed from a major congested airport knows that arriving on time is not always easy.