MSc Students

Thesis and Semester Projects

Open MSc Projects

[1] Evaluation of fairness in addressing citizen complaints using the Dutch complaints database

[2] Preference Elicitation of public and private sector organisations in addressing climate change

[12] Supporting Decision Making for Time-Resilience of Public Transport Systems

Jakob Serlier [BSc Student]

BSc @ TPM, TU Delft [Feb 2020 - July 2020]

[11] Estimating Factors that Influence Migration Flows in Refugees

Jochem de Vlug

EPA @ TPM, TU Delft [Feb 2020 - July 2020]

[10] Designing a Space-Time Gravity Model for Meso-Scale Mobilty Flow Estimation

Karan Pappala

EPA @ TPM, TU Delft [Feb 2020 - July 2020]

[9] Measuring Ecological Footprint of Cities

Ruchik Patel

EPA @ TPM, TU Delft [Feb 2020 - July 2020]

[8] Measuring the Age of Building using Remote Sensing Data

Oana Garbasevschi

EPA @ TPM, TU Delft [Oct 2019 - Mar 2020]

[7] Design of Hierarchical Measures for Public Transport Networks

Abel Buijtenweg

TIL @ CiTG, TU Delft [Sep 2019 - Feb 2020]

[6] Riverine Flood Risk Screening with a Simple Network-based Approach

Bram van Meurs

EPA @ TPM, TU Delft [Aug - Dec 2019]

Changes in climate conditions lead to unanticipated variations in glacial runoffs, snowmelt and precipitation, both significantly changing river flows. An imbalance in river network equilibrium leads to flooding and often ends up causing tremendous damage to society and environment. Regions that are perceived to be downstream from the source of flooding may in fact end up taking the brunt of the river force due to flood cascades. However, most studies cater to flood risk . We propose to devise a novel methodology to map rivers as unidirectional networks using river network geometry and scaling relationships fundamental to its tree-structure. Following that, we aim to convert the unidirectional flow networks to Bayesian belief networks calibrated by precipitation data and changes in glacial terrain at the source. Thus, our goal is to develop a likelihood map of excessive flooding around river networks that takes cascading into account based on drainage basin topography and network effects of river streams. A posterior inference of flooding around river streams would arm policy initiatives with strong evidence to develop safeguarding mechanisms for life and property in good time.

[5] Identification of the Hierarchy in Public Transport Networks based on Passenger Flow Patterns

Ziyulong Wang

CiTG, TU Delft [Apr - June 2019]

In this study, a data-driven, generic and transfer-based methodology for separation and ranking the PTNs has been put forward. With the hierarchy of a network, this is beneficiary for the management and operation of operators for focusing on the higher level network layer and in turn provide better service for passengers. The study introduces three steps to rank the hierarchy of a PTN: (1) using the passenger journey and ride data to derive transfer flow matrix; (2) applying C-space network representation with community detection method to separate and visualize the PTN layer; (3) performing ranking method, regarding inner- and intra- transfer flow. To this end, the hierarchy of a PTN could be presented with temporal attributes. Different day of week and various time period of a day could potentially yield different hierarchy. The proposed unsupervised learning algorithm is based on passenger transfer flow data, independent from geographic location and the mode of transportation. The study shows that the level is changing based on the selected time slot and can be a mixture of different modes, which is dissimilar from the hierarchy purely based on qualitative method.

[4] The Structural DNA of Cities

Franziska Krummenacher

ETH Zurich [Apr 2018 - Mar 2019]

Since cities are growing faster than ever, city planning is crucial to maintain a fully functional and efficient infrastructure. However, still no comprehensive city model exists that is able to explain the structure of today’s cities and predict their future. In order to take a small step towards developing such a model, we aim at identifying the basic building blocks of cities. This thesis proposes a data-driven approach towards city modelling using unsupervised clustering techniques. Complete city maps of 251 cities worldwide are analyzed. First, clustering is conducted on scalar features and a similarity measure between cities. We show that although we obtain reasonable clustering results, this approach is unsuitable for the identification of the fundamental elements of cities. In the second part, we focus on network motifs in city graphs and also use latent Dirichlet allocation, a technique from natural language processing, for in-depth city analysis based on network subgraphs and motifs.

[3] Transit Ridership: Methods in Urban Studies and Transit Planning

Itto Kornecki

ETH Zurich [Sep 2018 - Jan 2019]

With the advent of Automatic Fare Collection in transit networks, there has been a dramatic rise in the availability of transit data. In this work, we show that this data can be used to reveal information about the urban structure and the accessibility of the rail network. Specifically, we analyze the diurnal ridership of the Transit for London underground stations in order to describe the urban structure of London and to quantify the suitability of the network for daily commuters. By removing the conventional dependence on origin-destination trajectories, our methods can be expanded to other transit networks, such as bus and tram. Our work serves as an easily applicable planning tool for urban planners and transit agencies.

[2] Impact of Perceived Distances on International Tourism

Luís Rebelo

FCUL, University of Lisbon [May - Oct 2018]

Worldwide tourism revenues have tripled in the last decade. Yet, there is a gap in our understanding of how distances shape peoples’ travel choices. To understand global tourism patterns we map the flow of tourists around the world onto a complex network and study the impact of two types of distances, geographical and through the World Airline Network, a major infrastructure for tourism. We find that although the World Airline Network serves as infrastructural support for the International Tourism Network, the flow of tourism does not correlate strongly with the extent of flight connections available worldwide. Instead, unidirectional flows appear locally forming communities that shed light on global travelling behaviour since there is only a $15\%$ probability of finding bidirectional tourism between a pair of countries. We find that most tourists travel to neighbouring countries and mainly cover larger distances when there is a direct flight, irrespective of the time it takes. This may be a consequence of one-way cyclic tourism that we uncover by analysing the triangles that are formed by the network of flows in the International Tourism Network.

[1] Modeling and Characterizing the Core-Periphery Structure of the World Airline Network

Fabian Russmann

ETH Zurich [Feb - Aug 2014]

The stability and efficiency of the global network of commercial airline con-nections has become a vital part of our globalized society. Going beyond empirical analysis, we present here a model trying to understand the forma-tion of the world airline network (WAN) from basic principles. In an iterative algorithm, our model employs two opposing forces: A passenger’s desire to fly on non-stop flights whenever possible and an airline’s strive to maximize profitability of each connection. As a function of a profitability threshold, we identify three distinct families of networks with a fully-connected, a core-periphery, and a tree-like structure, respectively, as outputs of this algorithm. Characterizing the regimes using several different metrics, we show that our model is able to recreate the unique core-periphery structure of the empir-ical WAN. Remarkably, in this regime of networks, the passenger load on each flight (airlines’ profitability) is maximized while the average shortest path (passengers’ convenience) stays stable. However, comparing results of a connectivity robustness analysis, we also find that the modeled networks are more robust than the real-world network, suggesting that further develop-ment of our model may help to improve the current state of the world airline network.