The Master in City & Technology’s academic structure is based on IAAC’s innovative, learn-by-doing and design-through-research methodology which focuses on the development of interdisciplinary skills. During the Master in City & Technology students will have the opportunity to be part of a highly international group, including faculty members, researchers, and lecturers, in which they are encouraged to develop collective decision-making processes and materialize their project ideas.
The project focuses on promoting the physical, mental, and social well-being of older adults in the city of Barcelona through various initiatives, the goal is to empower older adults to remain active and engaged in their communities, and to promote healthy ageing and independence. Projected population aged 65+ What is Ageing? Health in the older … 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
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
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
The data used to prediction is the New York City taxi data from January, 2016 to June, 2016 and New York City weather data from the same time duration. The taxi data has features about pickup time detailed to seconds. Considering the traffic condition could be affected by weekday and hour, so I deconstructed the … Read more
The source dataset provided for this project is derived from the 2016 NYC Yellow Cab trip records, which were made publicly available on the Big Query platform of the Google Cloud. The data was originally collected and published by the New York City Taxi and Limousine Commission (TLC). This dataset serves as the foundation for … Read more
INTRODUCTION The competition is based on the 2016 NYC Yellow Cab trip record dataset. The challenge is to build a model that predicts trip duration for New York City taxis using machine learning. The dataset includes pickup time, geo-coordinates, number of passengers, and several other variables. Based on individual trip attributes, a code was written … 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
The aim of the project is to predict trip duration, using 2016 NYC YELLOW CAB TRIP DATA. Structuring the dataset The analysis begins with outlier identification. The passenger_count variable has two outliers: 0 and 9, which compared to the amount of people allowed by the NY Limousine law, is impossible. Also, there were some pickup … Read more
The project reflects on how people in ancient times defined natural and weather phenomena. The proposal is to create an open public space – Inspired by the concept of an ancient sanctuary with places that used to be built for worship and predictions of weather conditions, the Meteo Sanctum consists of digital indicators that allow … Read more
Mixed use pavilion with dominant cultural function. Cultural venue Barcelona Cloud. Envisioned constraints: not less than 1/10 for public spaces on the North and South, permeable transit corridors the ground level for large groups of pedestrians, solar envelope by summer solstice and average height of neighborhood buildings restrict height and shape the shadowing canopy, trees … Read more
