NTUA presents TANGENT articles at the hEART Symposium in Leuven on 1-3 June 2022

The 10th symposium of the European Association for Research in Transportation (hEART) takes place in Leuven on June 1-3 2022. It is jointly organised by KU Leuven and the University of Luxembourg. The event brings together leading experts and promising young researchers.

Taking this opportunity, the National Technical University of Athens (NTUA) presents two papers prepared within TANGENT:

C. Gasparinatou, E. Mantouka, E. Vlahogianni, J. Golias, 2022, “Investigating the acceptance and willingness-to-pay of an urban pricing scheme: The case of Athens”

Urban pricing strategies have been widely implemented in order to reduce externalities generated by traffic. These strategies have been considered effective schemes that not only it is possible to relieve congestion from metropolitan areas that have heavy traffic, but can also reduce emissions from cars and promote public transport usage. The scope of this paper is to investigate the users’ perceptions towards the measure of urban tolls in the center of Athens. Applying discrete choice models to data from a stated preference survey, it was resulted that the majority of respondents would not accept such measure. Furthermore, it was found that drivers are willing to pay an extra 8-euro cents in order to save 1 minute on the travel time of their trip.

E. Karakitsou, P. Fafoutellis, E. I. Vlahogianni, 2022, “Efficient Traffic Demand Forecasting Using A Meaningful Representation With Social Multiplex Networks and Community Detection”

In this paper, a meaningful representation of the road network using Multiplex Networks, as well as a novel feature selection framework that enhance the predictability of future traffic conditions of an entire network are proposed. Using data of traffic volumes and tickets’ validation from the transportation network of Athens, we were able to develop prediction models that achieve very good performance but are also trained efficiently, do not introduce high complexity and, thus, are suitable for real-time operation. More specifically, the network’s nodes (loop detectors and subway/metro stations) are organized as a multilayer graph, each layer representing an hour of the day. Nodes with similar structural properties are then classified in communities and are exploited as features to predict the future demand values of nodes belonging to the same community. The results imply the potential of the method to provide reliable and valid predictions.

More information about the event is available here: https://heart2022.com



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