Juan is a young professional living in a narrow street in Barcelona and he describes the street he lives in as a concrete hell. How air quality and greenery impact the experience, how a citizen feels, about a public space? The result will be an urban analytical tool for the city planners to manage air … Read more
The data we engage with everyday is growing rapidly, and our digital footprint is increasing! With the cyber-physical convergence and the fast expansion of the Internet, Volume of information created, copied, captured, and consumed worldwide went form 33 zettabytes in 2018 to an expected 175 zettabytes in 2051! That’s close 500% increase in 5 years. … Read more
This research study delves into the realm of advanced robotics and semi-automated manufacturing processes that take into account the material properties of cork. Specifically, it explores the design and fabrication of a surface system that is optimized for both aesthetic appeal and functional performance. Building on the knowledge gained in a previous term on robotic … Read more
As the Submission about Data Digital Tools & Big Data II, we analyzed the new york taxi data. We were given the data about Taxi Infomation in New York, in 2016.In this data, there is information, from the data, I made new information, Erased some columns, and fix the data. Reading the data the data … Read more
Clustering advanced industries to facilitate technology transfer in the Barcelona metropolitan region Spain has been at the forefront of pioneering research and innovation that have led to significant contributions to the world of technology. Technology Transfer Technology transfer from research to market seems like a straight-forward and linear process, but such is not the case. … Read more
The New York City Taxi Trip Duration competition is a challenge to develop a model that predicts the total ride time of taxi trips in New York City. Yellow medallion taxicabs, which number 12,779 in New York City, generate a substantial revenue of $1.8 billion per year by providing transportation services to around 240 million … Read more
Machine learning has been applied to a wide range of domains, including transportation, to improve the accuracy of predictions and optimize systems. In the context of taxi services, predicting the trip duration is an essential task to optimize route planning and estimate arrival times. In this post, we present a machine learning approach using Python … Read more
Context Additive manufacturing is growing exponentially in many industries at many levels, pushing towards new upgrades as a competitive alternative by reducing its logistic and production costs and increasing its flexibility and adaptability to the market’s demand. However, there is still an important challenge that it is facing: a gap on matching references between physical … Read more
The construction industry creates a waste that is expected to reach 2.2 billion tons globally by 2025 (Transparency market research). Because of this the market started trying different new techniques to construct with less waste, that is how with the 4.0 industrial revolution came the Additive manufacturing process, that fabricates physical 3D objects layer by … Read more
Goal: to predict trip duration of NYC cabs using machine learning models. Tools: Python + Nympy + Pandas + Datetime + Plotly.express + Matplotlib + Math + Seaborn + Bokeh + Sklearn Stages of project: data cleaning, data analysis, data preparation, data testing, evaluating prediction accuracy. Data cleaning The first dataset visualization with splitting datasets … Read more
This project is looking to be a tool that allow the monitoring, messure and documentation of the AM process at the moment of the printing with the help of conventional cameras, analog sensors, robot Data and open source technology Limitless project use different technology process to adquire information and process it. After the creation of … Read more
Machine Learning-based Analysis of NYC Cab Trip Duration “Data-Driven Rides” is an entry for the first MaCT Machine Learning Competition that hosted on Kaggle which involves predicting the duration of taxi rides in New York City. The dataset provided for this competition is based on the 2016 NYC Yellow Cab trip record dataset and the … Read more
The objective of this exercise is to build a machine learning model that will predict taxicab trip durations based on 2016 NYC Yellow Cab trip record data. Data Fields Based on this, this project will develop a machine learning regression algorithm capable of predicting the duration of a trip based on the variables provided by … Read more
The MaCT01 students were tasked with training a model that would be used to predict NYC yellow taxi ride durations using machine learning. The dataset included pickup and drop-off datetimes, location coordinates and passenger count. Visualizing the data helped to understand the correlation between the columns and remove the highly correlated values Understanding the distribution … Read more
#Objective #A Kaggle Competition to MaCT01 students to show their knowledge, designing an end-to-end machine learning project to predict the “Trip Duration” of NYC Taxi trips. #Workflow #First of all a workflow hast do be developed, which represents a classic approach for training machine learning models, analysing the provided training data provided by the submission, … Read more
INTRODUCTION Urban planning decisions have a significant impact on the development of cities, and using machine learning can provide decision-makers with valuable insights to make informed decisions. By clustering urban areas based on various factors such as population density, built density, POI density, green cover, and build diversity, we can reveal spatial patterns that can … Read more
