UCL MaaSLab (www.maaslab.org) invites applications for a 3-year PhD studentship to focus on the development of long-, mid- and short-term dynamic travel behaviour models.
This PhD scholarship is part of the H2020 funded research and innovation project of MaaSLab entitled HARMONY.
HARMONY envisages developing a new generation of harmonised spatial and multimodal transport planning tools which comprehensively model the dynamics of the changing transport sector and spatial organisation, enabling metropolitan area authorities to lead the transition to a low carbon new mobility era in a sustainable manner. Small-scale demonstrations with Autonomous Vehicles (AVs) and drones take place to understand in real-life their requirements and collect data to be used for modelling. The HARMONY model suite is designed to assess the multidimensional impacts of the new mobility concepts (i.e. on-demand mobility, Mobility as a Service) and technologies (AVs and drones). The model suite integrates: 1. land-use models (strategic), 2. people and freight activity-based models (tactical), and 3. multimodal network (operational) models allowing for vertical planning. HARMONY's concepts and the model suite are applied and validated on six EU metropolitan areas on six TEN-T corridors: 1. Rotterdam (NL), 2. Oxfordshire (UK), 3. Turin (IT), 4. Athens(GR), 5. Trikala(GR), 6. Upper Silesian-Zaglebie Metropolis (PL).
This PhD scholarship will focus on the development of the tactical activity-based models of the HARMONY model suite. The proposed PhD topic includes the development of advanced dynamic travel behaviour models that take into account the dynamics of new mobility services and technologies. In your PhD you will be expected to master a broad range of theory including choice models, econometrics, machine learning and big data in order to tackle the challenges the new mobility services impose on travel demand models.
The project is well-suited to a highly-quantitative individual with strong mathematical, data handling and computing skills. Students should have a bachelor's or master’s degree in engineering, computer science, data science, mathematics, physics, geography or a closely-related discipline, awarded with first-class or upper second-class (2:1) honours, or an overseas qualification of an equivalent standard from a recognised higher education institute.
• Excellent analytical and computing skills. Passionate about modelling, programming, data analysis, and conducting research.
• A MSc degree in transport engineering/planning, computer science, data science, mathematics, physics, geography or a closely-related discipline.
• Candidates without a master's degree may be admitted in exceptional cases where suitable research or professional experience can be demonstrated.
• Knowledge of relevant programming languages or statistical software (such as Python, C++, R, MATLab etc.)
• Ability use own initiative, prioritise workload, and be a fair team player
• Good interpersonal and communication skills (oral and written)
• A high level of attention to detail in working methods
• Interest in the challenges of the Transport sector of the 21st century
Eligibility: Please check https://www.ucl.ac.uk/prospective-students/graduate/research/requirements
Stage 1 - Pre-application documents - (1) CV, (2) academic transcripts, and (3) 1-page personal statement outlining motivation, interest and eligibility for the post - should be emailed directly to Teresa Dawkins: [email protected]
Stage 2 - Following the interview, the successful candidate will be invited to make a formal application to the UCL Research Degree programme.
Informal enquiries on the content of the research topic should be emailed to Dr Maria Kamargianni, [email protected]
Deadline for application: 14 April 2019
Interviews week starting: 22 & 23 April 2019
Keywords - Transport, Transportation, Computer Science, Machine Learning