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Transportation in the shared economy is defined as a peer-to-peer activity of acquiring, providing and sharing access to transport services (e.g. car-sharing, ride-sharing etc.). The core of new business models for 'mobility as a service' is to provide a real-time and customizable service to their customers which is facilitated through an app-based solution.
These business requirements have shifted the planning paradigm from a focus on classical operator-side solutions to one that focuses on user-system interaction. In combination with the increasing presence of real-time data, this leads to a much more complicated planning problem. At the same time, it creates an opportunity for developing self-learning mechanisms trained by observations the decisions made by each individual traveler.
The primary goal of this project is to design and to develop innovative decision-making models to tackle such challenges and capitalize on emerging opportunities. More specifically, we are interested in introducing (classical) optimization models with (emerging) learning and real-time design approaches.
The candidate is required to conduct this research by exploiting operations research models as well as heuristic solution approaches while explicitly taking users' behavior into account (modelled with discrete choice and machine learning theories).
We seek candidates with a strong background in operations research and computer science with prior knowledge in transportation. Having knowledge in discrete choice models and related data-analysis methods is a plus.
Applicants must have completed a master degree or equivalent education. Furthermore, applicants should demonstrate a solid background in articulating themselves in English.
You will be offered full time employment (38 hours per week, 1 FTE) for the duration of four years, subject to a trial period of one year. Gross monthly salary will range from € 2.325,- to € 2.972,-.
TU Delft offers a customisable compensation package, a discount for health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. An International Children’s Centre offers childcare and an international primary school. Dual Career Services offers support to accompanying partners. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
As a PhD candidate you will be enrolled in the TU Delft Graduate School. TU Delft Graduate School provides an inspiring research environment; an excellent team of supervisors, academic staff and a mentor; and a Doctoral Education Programme aimed at developing your transferable, discipline-related and research skills. Please visit www.tudelft.nl/phd for more information.
Contract duration: 4 years
Delft University of Technology (TU Delft) is a multifaceted institution offering education and carrying out research in the technical sciences at an internationally recognised level. Education, research and design are strongly oriented towards applicability. TU Delft develops technologies for future generations, focusing on sustainability, safety and economic vitality. At TU Delft you will work in an environment where technical sciences and society converge. TU Delft comprises eight faculties, unique laboratories, research institutes and schools.
The Faculty of Technology, Policy and Management (TPM) develops robust models and designs, to solve the complex challenges of today’s networked society. TPM combines insights from the engineering sciences with insights from the humanities and the social sciences.
The PhD researcher will be positioned in the Transport and Logistic section (TIL), within the Engineering Systems and Services (ESS) department of TPM.
A core activity of the department is to model and to understand the interaction, at a system level, between emerging technologies and human behavior. These insights are used to improve the design, regulation and operations of such systems.
The transport and infrastructure group conducts research on improving the design and operation of transportation systems with an emphasis on behavioral and policy aspects.