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.
By 2100, the global temperature is forecasted to increase from 2 to 4 degrees Celsius due the effects of Greenhouse Gases Emissions and Human Activities. The City of Viladecans has developed a Vision by 2030 which expects the city to become EMISSIONS NEUTRAL, and EMISSIONS NEGATIVE by 2050. For approaching this goal, HYPENERGETIC VILADECANS establishes … Read more
In the realm of urban design, understanding movement behaviors is paramount for creating efficient and people-centric city layouts. In this individual project, I delve into the intricacies of movement behaviors using computational tools and techniques, including agent-based modeling. By incorporating the loop evaluate field amplitude tool and employing vector display, scalar fields, heat maps, and … Read more
Our project embarked on a mission to tackle global shipping piracy without resorting to military action. We faced a major challenge: finding and using publicly available information to craft a proposal that countries and international organizations would support. 1. WHY SHIPPING PIRACY? Within the context of Networked Flows, our directive was to pinpoint potential disruptions … Read more
1. Introduction Urban air quality is a pressing concern, being the result of both natural elements and human-induced pollutants. The city of Barcelona grapples with local pollutants like NOx and PM10, primarily caused by ground transport and industrial activities. While current policies focus on mobility solutions, the project questions the need for complementary approaches. The … Read more
Introduction Known for its fashion, history, and culture, Milan also faces a pressing environmental issue: it stands as one of the most polluted cities in the World. The challenge of mitigating air pollution in Milan is complex, as it is shaped by a mix of geographical, meteorological, socio-economic, and cultural factors. From the city’s dense … Read more
A digital tool proposed to re-boost the economy of Ciutat Vella, Barcelona Overview Ciutat Vella being the oldest district of the city of Barcelona was founded in the 1500 BC. The Ciutat Vella district was a very important economic hub for Barcelona. But in the recent times, the district faces problems that are characterised by … Read more
Paris, often hailed as the world’s most popular tourist destination, beckons millions of visitors each year with its unparalleled blend of history, culture, gastronomy, and romance. At its heart stands the iconic Eiffel Tower, symbolizing the city’s architectural finesse and innovative spirit. The Louvre Museum, home to countless masterpieces, invites art aficionados to meander through … Read more
The General Transit Feed Specification (GTFS) is a standardized format designed for the distribution of public transport information globally. This format facilitates the integration of public transportation systems in cities worldwide with popular route planning platforms like Citymapper, Waze, or Moovit. By doing so, it enables users to access information on transport availability and plan … Read more
A Data-driven Approach of redefining the Accessibility of the Dispersed Urbanity of the Aegean Archipelago Accessibility is a multifaceted concept that expresses the case of access between two points in space. In the context of islands, accessibility is of utmost importance due to their inherent characteristics of isolation and small size. The Aegean Archipelago in … Read more
– 00– The project aims to analyze the sounds of streets in Barcelona, cluster them and compare. Firstly, a table of sound types evaluation is created. It consists of point data, network data and sound intensity data. – 01– The methodology and analysis of street segments is based on 1 dataset, that was combined using … Read more
The city of Mumbai, known as the financial and entertainment capital of India, has experienced a significant surge in housing prices and rents in recent years. The demand for residential properties in Mumbai has been steadily increasing due to factors such as population growth, urbanization, and the city’s thriving job market. In the midst of … Read more
London is a city with high density of bike lane networks, and the high costs on metro drives people using bike more. But how people interact with different factors such as tree canopy, amenities and the urban form? Data preparation To start the analysis, I download the bike lane network sample area from Openstreetmap. Urban … Read more
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 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
