Origen-destination matrices estimation for simulating demand regional models
The International Centre for Numerical Methods in Engineering (CIMNE) is a research centre, created in 1987 by consortium between the Catalan Government and the Universitat Politècnica de Catalunya (UPC-BarcelonaTech), devoted to the development and application of numerical methods to a wide range of areas in engineering. CIMNE has been selected as a Severo Ochoa Centre of Excellence for the period 2019-2023. This is the highest level of recognition of excellence and leadership awarded to a research centre in Spain.
Sergi Saurí and Jordi Pons-Prats
Number of vacancies: 1
Category: PhD (PHD2)
Salary (gross): 17.563,14 EUR
Weekly working hours: Full time
Duration: 3 years
Starting date: No later than Sept 2021
CIMNE is looking for a PhD Researcher to be part of the Research and Technical Development (RTD) Group CENIT.
The functions assigned to the candidate will be:
Despite being a widely studied topic, the estimation of origin-destination matrices is still a source of concern for transport administrations looking for the most optimal way to estimate them, since it is the main variable in traffic simulation. Currently, despite the popularization of activity-based models and, more recently, agentive-based models, most administrations base their regional transportation models on the 4-stage model. This limits the ability to simulate the mobility of the territory, where the mobility required for work purposes is increasingly less marked and daily mobility for recreational reasons is gaining a significant amount, generating trips throughout the day outside the typical peak hours. Although the 4-stage models are based on the peak hour matrix (static model), which is a robust indicator of mobility, the estimation of hourly O-D matrices is a tool of interest for administrations since it not only enriches the model but also allows studying flows at a higher level of detail and the effects of certain transport policies on mobility over time.
The typical approach to the problem is through bi-level optimization (minimizing the distance between counts and traffic assignments). From here, one can opt for quasi-dynamic simulation that brings transport models closer to the advantages provided by activity and agent-based models. In practice, the main source of information are still mobility surveys combined with traffic data or license plate recognition cameras, which are a source of continuous information over time from which to derive the hourly matrices.
The result of the work would allow public administrations to have more accurate origin-destination models with which to improve mobility forecasts.
ARINBO- Nuevo sistema de gestión de gestión de movilidad urbana basado en matriz origen-destino y en herramientas de analítica avanzada. Nuclis Research Project with ALTRAN, CENIT-CIMNE and EURECAT. 2020-2021
Puignau Arrigain S.A., Pons-Prats J., Saurí Marchán S. (2020) New Data and Methods for Modelling Future Urban Travel Demand: A State of the Art Review. In: Diez P., Neittaanmäki P., Periaux J., Tuovinen T., Pons-Prats J. (eds) Computation and Big Data for Transport. Computational Methods in Applied Sciences, vol 54. Springer, Cham. Print ISBN 978-3-030-37751-9.
Motohiro Fujita, Shinji Yamada, Shintaro Murakami (2017) "Time Coefficient Estimation for Hourly Origin-Destination Demand from Observed Link Flow Based on Semidynamic Traffic Assignment", Journal of Advanced Transportation, vol. 2017, Article ID 6495861, 14 pages, https://doi.org/10.1155/2017/6495861
The requirements and merits will be evaluated with a maximum mark of 100 points. Such maximum mark will be obtained by adding up the points obtained in the following items:
Candidates must complete the "Application Form" form on our website, indicating the reference of the vacancy and attaching the following documents in English:
The deadline for registration to the offer ends on 31st May, 2021 at 12 noon.
The shortlisted candidates may be called for an interview. They may also be required to provide further supporting documentation.
CIMNE is an equal opportunity employer committed to diversity and inclusion. We are pleased to consider all qualified applicants for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, age, disability or any other basis protected by applicable state or local law. CIMNE has been awarded the HRS4R label.