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.
Starting in 2015 urban development spread beyond the administratively defined Melbourne metropolitan area, into Greater Geelong and the Shire of Mitchell. The Melbourne Metropolitan Region or Greater Melbourne comprises 31 Local Authority Governments (LGA, municipalities and shires) aggregated in 6 metropolitan partnerships. Two of them, Inner and Inner South-East allocate the metropolitan core. This project … Read more
The New Delhi Material Repository and Urban Mining project focuses on the city’s sustainable development by implementing the principles of urban mining and establishing a material repository. As the capital city of India, New Delhi holds significant historical and governmental importance, with its urban fabric comprising a combination of planned and unplanned elements. However, with … Read more
Considering the imminent demolition and in-situ rehabilitation of highly dense slum settlements, how might the application of generative algorithms facilitate the maximization and optimization of housing units within the existing spatial constraints? The slums and the unauthorized colonies are located across Delhi to identify which zones needs in-situ slum rehabilitation, there are over 700 slum … Read more
Recycling construction materials is of paramount importance in a city like Barcelona due to its numerous environmental, economic, and social benefits. Barcelona, like many urban centers, faces challenges related to rapid urbanization, resource scarcity, and environmental sustainability. By implementing a robust construction material recycling concept, the city can significantly reduce its construction waste, lower its … Read more
The Boulevard Method utilizes big data analytics and multi-objective optimization processes to map the existing materials building stock and propose adata-driven urban strategy for the future densification of the city of Helsinki. By making the hypothesis that certain roadways will become obsolete in the future due to the transformation of urban mobility, this study analyzed … Read more
Optimizing Helsinki’s Boulevard Densification Method Helsinki, the vibrant capital of Finland, faces significant changes as it prepares for the future. With important challenges ahead such as the continuous population growth for the next decades and the associated risk of urban sprawl in suburban developments, the city must address the issue of land fragmentation by transforming … Read more
Main challenge: densify city, connect nature, and mitigate embedded CO2 Singapore is one of the densest countries in the World. Nature-conscious city densification has to consider reimbursement of natural patches within built environment, applying an connecting nature approach similar to a multi-tiered tropical forest. Mitigation of embodied carbon stays in a row with nature connectivity. … Read more
Before the implementation of Cerda’s radical expansion plan, Barcelona was bound by their medieval walls and making every effort to accommodate its overflowing population. Cerda’s plan took into account scientific objectives that would create a city that is not just a well planned co-habiting space but also user-centric. Some of his objectives were gardens in … 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
The City of Marvelous Disorder is a study was conducted during the ‘CaaS studio (City as a Service): The future of cities’ services in the AI times’, that aims to built a methodology on mapping the creative sector and high education ecosystem of Barcelona, with a particular focus on social creative industry that works within … Read more
The rising problem of Digital Energy consumption Recent estimates put the contribution of the information and communications Technology, the (ICT) sector – which includes the data centers, devices and networks used for at around 4 % of global Greenhouse gas emissions. A trend that is unlikely to stop, as the amount of data produced and … Read more
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
