Two PhD positions
Model-Predictive Railway Traffic Management
Mechanical, Maritime and Materials Engineering
The 3ME Faculty trains committed engineering students and PhD candidates in groundbreaking scientific research in the fields of mechanical, maritime and materials engineering. 3ME is the epitome of a dynamic, innovative faculty, with a European scope that contributes demonstrable economic and social benefits.
The Faculty of Civil Engineering and Geosciences follows in the tradition of centuries of engineering the Netherlands environment. It addresses today’s challenges with a versatile and socially relevant spectrum of courses, as well as top-level research in domains including hydrology, construction, geotechnology, water management and transportation. A dynamic and committed team of researchers and support staff provides education to over 2000 students and 100 PhD students.
The Delft Centre for Systems and Control (DCSC) coordinates the education and research activities in systems and control at Delft University of Technology. The Centre’s research mission is to conduct fundamental research in systems dynamics and control, involving dynamic modelling, advanced control theory, optimization and signal analysis. The research is motivated by advanced technology development in mechatronics and microsystems, sustainable industrial processes, transportation and automotive systems, and physical imaging systems. The group actively participates in the Dutch Institute of Systems and Control (DISC).
The department of Transport & Planning consists of five chairs, on transportation planning, design of transport facilities, dynamic traffic management, infrastructural planning, and traffic safety. The department focuses on the planning, modelling, design and control of transport systems with special attention for passenger transportation, road and railway traffic. Transport & Planning is known for its high-quality research in this field. Furthermore, Transport & Planning is responsible for the MSc variant Transport & Planning and participates in the interfaculty MSc programme Transport, Infrastructure & Logistics.
In this project new models and a new (predictive) control approach are developed for anticipative management of railway traffic networks. The project aims at closing the loop between timetabling and train operations with a continuous feedback of train positions and field data to allow fast rescheduling of train paths in case of disturbances. Using predictions based on railway traffic models, optimization procedures will be developed for finding the most effective measures for regulating train traffic in case of disruptions. This model predictive control (MPC) approach must maintain an up-to-date feasible plan on a national network level with respect to the current state of the infrastructure (speed restrictions, blocked tracks), resources (rolling stock and crews), train delays, and expected conflicts.
The research contains three main ingredients: (1) Monitoring: developing a methodology to actively monitor train positions, speeds, and infrastructure availability, and to determine up-to-date running time estimates; (2) Predictive traffic modelling: developing a real-time railway traffic prediction model that can be updated continuously with the latest information and selected control decisions; and (3) Model predictive control: developing a model predictive control system that optimizes future control decisions by using predictions of the future behavior of the railway traffic.
The core of the MPC approach is the railway traffic model for which an extension of max-plus linear systems is proposed. A max-plus linear system is a discrete-event dynamic system characterized by synchronization constraints which has been successfully applied to the modelling of large-scale railway networks. The conventional max-plus models assume a fixed structure of e.g. train orders and routings that may not be maintained in perturbed operations. In this project the model is extended to switching max-plus models, which can switch between different modes representing alternative decisions or circumstances. Effective modelling procedures are to be developed to build typical control decisions into max-plus models, such as rerouting, reordering, and changing meeting or overtaking locations. In this project the modelling and theory of switching max-plus linear systems will be studied with a focus on computational aspects and an embedding in a real-time MPC framework. The expected outcome is a model predictive railway traffic management system to be used as a supervisory and intelligent decision support system for railway traffic controllers and signallers. The project is supported by Arcadis, DHV, Movares, NS Reizigers, ProRail, and Siemens.
Applicants should have an MSc degree with a background in systems and control, civil engineering, mathematics, computer science or a related field, and preferably a strong interest in railway operations, optimization, and data analysis. Fluency in spoken and written English is an essential requirement.
Conditions of employment
The successful candidates will be employed full-time by TU Delft for a fixed period of four years within which each candidate is expected to write a dissertation leading to a doctoral degree (PhD thesis). The starting salary for a PhD is € 2042 gross per month increasing to a maximum of € 2612 gross per month in the fourth year.
TU Delft offers an attractive benefits package, including a flexible work week, free high-speed Internet access from home, and the option of assembling a customized compensation and benefits package (the 'IKA'). Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
Information and application
For more information about these positions, please contact Dr.ir. T.J.J. van den Boom (DCSC) firstname.lastname@example.org, or Dr. R.M.P. Goverde (T&P), email@example.com. For more information on the departments, see http://www.dcsc.tudelft.nl and http://www.transport.citg.tudelft.nl.
To apply, please e-mail a detailed CV along with a letter of application and a course/grade list by 15 February 2010 to Ms. M.J.M.E. de Groot, Application-3mE@tudelft.nl. When applying for this position, make sure to mention vacancy number 3ME09.44.
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