News - TRA Young Research Winners - Cross-modality Award Winners

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Forum of European National Highway Research Laboratories

TRA Young Research Winners - Cross-modality Award Winners

Cross-modality Award Winners


Let's have a look on Cross-modality Award Winners:

🏅 Nikolaou Paraskevas, from University of Cyprus

🏅 María J. Alonso González, from Delft University of Technology

🏅 Ioulia Markou, from Technical University of Denmark

Nikolaou Paraskevas from University of Cyprus presented a research project on “Controlling the Possible Spread of Infectious Diseases through the Air Transportation Network: A Dynamic Network Approach”.

Mobility of people is rapidly incrementing, but this may pose a threat to public health by facilitating infectious diseases spread through the transportation networks and mass transportation systems. In order to prevent this, it is important to investigate the mechanics of the spread of infectious diseases.

This project develops a dynamic systems approach, combining the integration of two modes of passenger transport (airway and roadway) with epidemic dynamics. The model is based on realistic global air traffic data, while the land transportation system relies on real information population densities. The project provides a novel approach for identifying specific locations or facilities that can spread infectious diseases over spatially connected regions, also providing an understanding of how the network topology, i.e. the air and road connection between countries, can affect this dynamic process.

María J. Alonso González from Delft University of Technology presented a research project on “Potential Uptake of Mobility-as-a-Service for Different Market Segments”.

Mobility-as-a-Service (MaaS) aims at improving urban transportation by providing integrated mobility "as a service". MaaS is fuelled by emerging on-demand transportation services and is expected to drive the upcoming mobility revolution, contributing to a shift from the current (privately-owned) car-centred model to increased public transport usage.

This project aims at investigating individuals' inclination to adopt MaaS. Going beyond the study of early adopters, it identifies drivers and barriers that can play a role in the adoption of MaaS for a large representative sample (>1,000 individuals) of the Dutch urban population. It also investigates whether public transport or other on-demand services are likely to become the backbone in MaaS.

Study results help evaluate future changes in urban mobility as a result of MaaS, and help design targeted strategies tailored to distinct users segments. Additionally, this project provides a framework to investigate differences in MaaS adoption potential among countries.

Ioulia Markou from the Technical University of Denmark proposed a research on the “Prediction of traffic anomalies due to special events”.

Traffic congestion greatly affects urban areas, deteriorating mobility, safety, and air quality. In Europe, even cities with low congestion levels show excessive network demand during peak hours, affecting the quality of life of their residents.

There is a need for frameworks that promote better management of city's road network and transport services.

While mature research exists for habitual behaviors (e.g. commuting cycles), current traffic management solutions typically fail under non-recurrent circumstances, such as incidents, special events, road-works, crisis scenarios, or inclement weather.

This project explores machine-learning architectures for combining time-series and textual data for mobility-demand predictions in eventful areas, where abnormal patterns are often observed but not easily explained. Our proposed models significantly reduce forecast errors by using cross-modal sources of information.

The results show that abnormal demand peaks can be detected accurately and in time, and competent authorities can subsequently take all the necessary measures to deal with them optimally.

Register here to attend the Remote Award Ceremony:
See you there September 29th at 9.30 AM (CEST)

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