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Position: PhD Student in Smart Mobility Management and Operation
Institution: Technical University of Denmark
Location: Kongens Lyngby, Lyngby‐Taarbæk Municipality, Denmark
Duties: In this project we aim at leveraging the existing theoretical foundation on TCS and focus on its potential deployment and impact assessment on a realistic setting. More specifically, the PhD student will work closely with the supervisory team to develop a realistic modelling framework for testing different operational designs. The project will be rooted around the implementation in realistic simulation applications. Other data sources, namely from controlled experiments to be designed by the team, can be incorporated into the project to further test the developed frameworks
Requirements: A MSc degree in Transportation Modelling, Applied Mathematics, Economics, Computer Science and Simulation, Operations Research, Industrial Engineering or related is required; Excellent programming capabilities, in at least one scientific language (e.g. Python, Matlab, R, Julia) is required; Excellent background in statistics and probabilities is required; Transportation Modelling disciplines in the education background are preferable; Knowledge of C++ programming skills is preferable
   
Text: PhD Student in Smart Mobility Management and Operation DTU Management Share on Facebook Share on Twitter Share on Linkedin Monday 03 Jun 19 Apply for this job Apply no later than 24 June 2019 Apply for the job at DTU Management by completing the following form. Apply online DTU Management at the Technical University of Denmark invites applications for a 3-year PhD position in Smart Mobility Management and Operation. Road traffic externalities are a serious problem that affect urban transportation networks worldwide. Its severity continues to increase imposing significant costs on the traveller, environment, economy, and society. Countries are coming up with several strategies to tackle this issue; Denmark for example just recently announced a future strategy to have all new cars from 2035 as zero-emission cars, raising the need for a new management scheme for energy and congestion. In Singapore, while the existing COE (Certificate of Entitlement) and CEV (Carbon Emissions-based Vehicle) schemes already work as quantity control mechanisms, its connection to the upcoming distance-based ERP (Electronic Road Pricing) brings a new hope for a more efficient road usage management. Indeed, common management policies to minimizethe negative externalities from traffic involve the use of economic instruments such as quantity control, pricing and incentives. Academics have mainly dedicated their attention to the independent and theoretical analysis of congestion or emission control and little effort has been put into the development of mechanisms for its combined real-time management in real-world scenarios under current and upcoming technological feasibility constraints. This has lead to very little deployment in practice and also a limited design and evaluation of suitable systems for the connected and electrified paradigm. This bi-lateral project will fill the gap by designing, developing, evaluating and comparing two advanced dynamic management frameworks for emission and congestion management, congestion pricing and tradable credit schemes, under sensing, communication and vehicular technological constraints of tomorrow. Common demand and supply models for mobility and its energy will be developed jointly by DTU and NTU in a flexible test-bed platform. This includes the dynamic travel decision making and its associated energy demand, the mobility network model with multi-modal vehicular performance and the interaction between all these components. For this, choice modelling techniques, multimodal traffic network representation and dynamic traffic assignment models and algorithms will be developed. While both partners will share these energy and mobility modelling and simulation framework, each partner will also focus on the design one of two different management systems: dynamic congestion pricing (NTU) and tradable credit schemes (DTU). These two systems will be tested and compared under the same common platform and for two selected real-world multi-modal networks from Singapore and Copenhagen. The project combines behaviour modelling in both travel and emission models, network modelling, dynamic programming and machine learning for optimization, and simulation (for system evaluation), building on previous work by DTU and NTU, which have been engaged in the related areas. Project overview Historically, inefficiencies such as congestion and vehicular emissions have been generally addressed with information provision and pricing. Recently, quantity control has been under the spotlight in transportation research, leveraging from successful applications in other economic sectors, such as the communications, energy or environmental sectors. Limited supply is in the end a scarcity problem that can be dealt with a price instrument, a quantity instrument or a combination of both, such as tradable credit schemes (TCS). Within a TCS system, a regulator provides an initial endowment of mobility credits to all potential travellers. In order to use a transportation system, users need to spend a certain number of permits (i.e.: tariff) that could vary with the conditions/performance of the specific mobility alternative used. The permits can be bought and sold in a market that is monitored by the regulator at a price that is determined by demand and supply interactions. In this project we aim at leveraging the existing theoretical foundation on TCS and focus on its potential deployment and impact assessment on a realistic setting. More specifically, the PhD student will work closely with the supervisory team to develop a realistic modelling framework for testing different operational designs. The project will be rooted around the implementation in realistic simulation applications. Other data sources, namely from controlled experiments to be designed by the team, can be incorporated into the project to further test the developed frameworks. This project is a partnership with the Massachusetts Institute of Technology (MIT) and the Nanyang Technological University (NTU). Responsibilities and tasks Literature review and learning in the design and modelling of dynamic mobility management systems; Design and model a flexible platform for testing different management scheme mechanisms; Formulate and develop one of the management schemes of interest, its components and solving methods. Code, implement and validate for the developed platform within the context of simulation environment; Test the impact of different scheme designs within the developed framework; Benchmark and provide recommendations on the design and application of tradable credit schemes vs.congestion pricing. Prepare manuscripts for journal paper publication Qualifications A MSc degree in Transportation Modelling, Applied Mathematics, Economics, Computer Science and Simulation, Operations Research, Industrial Engineering or related is required; Excellent programming capabilities, in at least one scientific language (e.g. Python, Matlab, R, Julia) is required; Excellent background in statistics and probabilities is required; Transportation Modelling disciplines in the education background are preferable; Knowledge of C++ programming skills is preferable; The following soft skills are also important: Curiosity and interest about current and future mobility challenges (e.g. smart and integrated mobility, network modelling, travel behaviour); Good communication skills in English, both written and orally; Willingness to engage in group-work with a multi-national team; Approval and Enrolment The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see the DTU PhD Guide . Assessment The assessment of the applicants will be made by 15 July 2019 . We offer DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility. Salary and terms of employment The appointment will be based on the collective agreement with the Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The PhD contract is offered for a three-year period. You can read more about career paths at DTU here . Further information For more information, please contact Carlos Lima Azevedo, climaz@dtu.dk , tel.: 45 4525 1545. You can read more about DTU Management in www.man.dtu.dk . Application Please submit your online application no later than 24 June 2019 (local time) . Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply online", fill out the online application form, and attach all your materials in English in one PDF file . The file must include: A letter motivating the application (cover letter) Curriculum vitae Grade transcripts and BSc/MSc diploma Excel sheet with translation of grades to the Danish grading system (see guidelines and Excel spreadsheet here ) Candidates may apply prior to obtaining their master's degree but cannot begin before having received it. Applications and enclosures received after the deadline will not be considered. All interested candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply. The Machine Learning for Smart Mobility group belongs to the Transport Modelling division of the Department of Technology, Management and Economics (DTU Management) at DTU. The division conducts research and teaching in the field of traffic and transport planning, with particular focus on behaviour modelling, machine learning and simulation. DTU Management conducts high-level research and teaching with a focus on sustainability, transport, innovation and management science. Our goal is to create knowledge on the societal aspects of technology - including the interaction between technology and sustainability, business growth, infrastructure and prosperity. Therefore, we explore and create value in the areas of management science, innovation and design thinking, business analytics, systems and risk analyses, human behaviour, regulation and policy analysis. The department offers teaching from introductionary to advanced courses/projects at BSc, MSc and PhD level. The Department has a staff of app. 350. Read more here . DTU is a technical university providing internationally leading research, education, innovation and scientific advice. Our staff of 6,000 advance science and technology to create innovative solutions that meet the demands of society, and our 11,200 students are being educated to address the technological challenges of the future. DTU is an independent university collaborating globally with business, industry, government and public agencies.
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