PhD candidates

The Allegro-project uses a total of eight PhD candidates to unravel slow mode traveling and traffic.


Foto Alexandra GavriilidouAlexandra Gavriilidou

E-mail Alexandra
Start date: 2016-10-01
Position: PhD candidate

Understanding and modelling individual walking and cycling behaviour, involving the within and between mode interactions and with infrastructure (Theme I, project T1.1).
This research aims to set up the behavioural theory of pedestrians and cyclists and develop mathematical models for walking and cycling. The models shall focus on the individual (microscopic) level, capturing the split-second decisions regarding motion and the interactions of traffic participants with each other and with the infrastructure. Psychological literature will be the starting point in setting up a comprehensive behavioural theory, while empirical and experimental data collection and analysis will be performed to underpin the theories and calibrate the models.

Favourite active mode: Walking and cycling
Favourite active mode quote: “It’s better to walk alone than with a crowd going in the wrong direction” – Diane Grant


Foto Vincent GongVincent Gong

E-mail Vincent
Start date: 2016-06-20
Position: PhD candidate

Retrieving information about crowdedness conditions and events from social-media data for off-line and on-line purposes.
This research involves 1) identification of relevant information from social media data sources, such as Twitter, Instagram, and Foursquare, 2) developing the methodology for analyzing those data, and 3) correlation analysis with other data sources, such as camera and wifi.

Favourite active mode: Cycling in town
Favourite active mode quote: “An early-morning walk is a blessing for the whole day.” – Henry David Thoreau


Foto Tim van OijenTim van Oijen

E-mail Tim
Start date: 2016-03-01
Position: Researcher / programmer

Development of the SL-UML app (Theme II, L3).
Project L3 deals with developing an interactive and context dependent app for monitoring, surveying, and actuation of cyclists and/or pedestrians. Besides this practical part of the research, algorithms will be developed to support pedestrians and cyclists to reduce the pressure on the urban network focussing on decentralized control mechanisms. These algorithms will address route choice problems as well as activity scheduling problems.

Favourite active mode: Walking and running
Favourite active mode quote: “All truly great thoughts are conceived while walking” – Friedrich Nietzsche


Foto Giulia ReggianiGiulia Reggiani

E-mail Giulia
Start date: 2017-11-15
Position: PhD candidate

Managing cyclist flows in urban areas.
Developing estimation, prediction and optimization/control models of cyclist flows in urban areas in order to use the cycling network more efficiently. This research will investigate how and which data is best to be used to estimate and predict specific aspects of cyclists’ behaviour at signalised intersections. Subsequently I will investigate on optimization methods for cyclist along a network in order to avoid crowded conditions (that increase waiting times and the manifestation of reckless actions) and make cycling an even more attractive and convenient alternative. The final idea of this Ph.D. project is that of informing the user where and when to take a detour from its initial route choice in order to reduce travel time and increase its comfort.

Favourite active mode: Sailing into the wind
Favourite active mode quote: “Give a man a fish and feed him for a day. Teach a man to fish and feed him for a lifetime. Teach a man to cycle and he will realize fishing is stupid and boring.” – Desmond Tutu


Foto Florian SchneiderFlorian Schneider

E-mail Florian
Start date: 2016-07-01
Position: PhD candidate

Establishing which factors determine the route and activity choices in an urban environment based on empirical data (Theme I, project T2.2).
This project analyses the factors that play a role for active mode travellers in deciding to choose a particular route through the city or to choose where and when to perform a certain activity in dependency of the trip purpose. To this end, an exploratory, big-data inspired approach to look for correlations between behavioural outcomes and various factors will be used in a first step. In a second step, a model-based analysis using the mathematical models developed in T2.1 will be carried out to reveal more complex determinants of the choice behaviour of slow travellers.

Favourite active mode: Climbing and ski mountaineering
Favourite active mode quote: “Life is like riding a bicycle. To keep your balance, you must keep moving.” – Albert Einstein


Foto Danique TonDanique Ton

E-mail Danique
Start date: 2016-02-01
Position: PhD candidate

An integrated activity scheduling, mode and route choice approach for active modes in the urban environment (Theme I, project T2.1).
In this project theory and models are developed for activity travel choices. Route choice, mode choice and activity scheduling are the components for integrated model and theory. Before the travel is initiated several choices need to be made. A person needs to determine which activities are performed (e.g. work, visiting friends, grocery shopping), in what order they need to be performed, how much time they will take and so on. Next to that, a person needs to determine how he will get to the activity destination. Will he go cycling or walking or will he for example walk to the train station, take the train and then rent a bike to get to the final destination. Also, a person needs to decide on the route he will travel to get to the activity location. These choices will first be viewed separately before integrating them into one model. The role of knowledge of the environment and the use of ICT services will also be evaluated regarding activity travel.

Favourite active mode: Walking (no matter what the speed will be)
Favourite active mode quote: “Everywhere is within walking distance if you have the time” – Steven Wright


Foto Alphonse VialAlphonse Vial

E-mail Alphonse
Start date: 2017-10-01
Position: PhD candidate

Sensing Platform: Unveiling Active Mode Mobility Patterns with Connected Intelligent Vehicles.
Intelligent vehicles will operate autonomously by perceiving their environment and implementing responsive actions. Different types of sensors generate important information on a vehicle’s inner state and its surrounding environment. Accessing a vehicle’s internal bus system, as well as external sensing sources, provide access to the entire nervous system of a vehicle in near real time. Now think of connected vehicles, continuously sharing and exchanging these gathered data with other vehicles operating in a city. If so, could vehicle sensing technologies then also be used for other purposes than the actual driving task? Taking advantage of this wealth of information, sensed by a network of “driving sensors”, could thereby help unveil active mode dynamics and behaviours in cities.

Favourite active mode: Does playing football count as active mode?
Favourite active mode quote: “I don’t know what my path is yet. I’m just walking on it.” – Olivia Newton-John


Foto Marie-Jette WierbosMarie-Jette Wierbos

E-mail Marie-Jette
Start date: 2016-10-31
Position: PhD candidate

The aim of this research is to develop macroscopic flow models for pedestrian, bicycle and mixed flow, for large scale applications (Theme I, project T1.2). By taking an aggregate approach, pedestrian and bicycle movements are assumed to be similar to a flow of people. Concepts used in fluid dynamics (such as conservation of mass and momentum) will be used to describe the flow of pedestrians and cyclists. Flow features of this active mode traffic will be identified and analysed. Ultimately, the newly developed macroscopic model will be calibrated against observational data.

Favourite active mode: Road cycling and hiking
Favourite active mode quote: “One of the most important days of my life, was when I learned to ride a bicycle.” Michael Palin

Foto Lara-Britt ZomerLara-Britt Zomer

E-mail Lara
Start date: 2015-10-01
Position: PhD candidate

Unravelling Urban Cognition – Acquisition, memorisation and utilisation of spatial cognition in urban environments (Theme II, T3).
Research will focus on theory development of conceptual and mathematical models focused on the perception and cognition of pedestrian and cyclists about the way they acquire, memorise and use (spatial) knowledge of the urban environment for wayfinding behaviour. Particularly of interest are 1.) how this cognition evolves over time, 2.) what the impact is of salient landmarks, 3.) what the role of ICT services can be to enhance urban cognition, and 4.) how network complexity influences urban cognition of individuals. Different types of experiments, deviating from low- to high-tech, will be conducted over a timespan of 2 years to gather qualitative and quantitative insights into the cognitive wayfinding behaviour of people in city centres.

Favourite active mode: Regatta sailing at sea
Favourite active mode quote: “A line is a dot that went for a walk” – Paul Klee