DC8. Proactive risk mapping and infrastructure safety management

Júlia Alves Porto

Education
MSc in Transportation from University of Brasília
BSc in Civil Engineering from University of Brasília
About
Master of Science in Transportation and Civil Engineer. She has professional experience in land transportation regulation and pavement condition monitoring. In her master’s research, she applied computer vision techniques to identify traffic conflicts and developed coding skills in Python. Additionally, in her latest employment, she contributed to standardize the assessment of new rail transportation parameters used to supervise service quality in Brazil.

Hosts: NTUA & iRAP

Objectives:

To create an AI framework to process, harmonise, analyse and model an array of different available datasets and provide outputs in the form of risk mapping and network-level evaluations
To develop new AI algorithms for road attribute collection, along with methodologies to assess and quantify their accuracies, suitable for network applications and including hybrid, e.g., manually collected data
To use the AI-augmented dataset creation effort for a suitable working methodology for the generation of hybrid road attribute data and enhanced proactive risk mapping

Expected results:

A functional framework with the use of AI to exploit road risk information in a meaningful manner, transferable between networks
Assessment and quantification of the influence of each examined factor on the output of AI algorithms for safety management
A case study with actionable results, compatible with the mission and activities of iRAP – namely, a proactive risk mapping and evaluation of a network and comparison with existing risk-mapping results, and quantification of transferability to a second network

Planned secondment(s):

FREDEng-IT, Purpose: To review and exploit synergies of FRED engineering activities relevant to road safety data collection and the feasibility of proactive AI implementations to industrial practice