Traffic Forecasting - DSAIT NTUA

Projects

AΙ-Aided Decision Tool For Seamless Multimodal Network And Traffic Management

Enhanced Data Processing Techniques For Dynamic Management Of Multimodal Traffic

Publications

From Travel Times to Lane-Based Hourly Traffic Flow: An Explainable Machine Learning Framework

Charis Chalkiadakis & Eleni I. Vlahogianni

Data Science for Transportation 7, 18, 2025

Revisiting the Non-recurrent Traffic Incident Identification Problem for Real-Time Applications Using Theory-Aware Unsupervised Learning

Papadatou, K. Ν., Fafoutellis, P., & Vlahogianni, E. I.

Data Science for Transportation, 7(2), 12, 2025

Quantum neural networks with data re-uploading for urban traffic time series forecasting

Schetakis, N., Bonfini, P., Alisoltani, N., Blazakis, K., Tsintzos, S. I., Askitopoulos, A., Aghamalyan, D., Fafoutellis, P. & Vlahogianni, E. I.

Scientific Reports, 15(1), 19400, 2025

A theory-informed multivariate causal framework for trustworthy short-term urban traffic forecasting

P Fafoutellis, EI Vlahogianni

Transportation Research Part C: Emerging Technologies 170, 104945, 2025

Traffic demand prediction using a social multiplex networks representation on a multimodal and multisource dataset

P Fafoutellis, EI Vlahogianni

International Journal of Transportation Science and Technology 14, 171-185, 2024

Urban Arterial Traffic Volume and Travel Time Estimation with Use of Data Driven Models

C Konstantinidis, C Chalkiadakis, P Fafoutellis, EI Vlahogianni

IFAC-PapersOnLine 58 (10), 102-107, 2024

Unlocking the full potential of deep learning in traffic forecasting through road network representations: A critical review

P Fafoutellis, EI Vlahogianni

Data Science for Transportation 5 (3), 23, 2023

A Causal Deep Learning Framework for Traffic Forecasting

Fafoutellis, P., Laña, I., Del Ser, J., & Vlahogianni, E. I.

26th International Conference on Intelligent Transportation Systems (ITSC), pp. 5047-5053, IEEE, 2023

Efficient Traffic Demand Forecasting Using A Meaningful Representation With Social Multiplex Networks and Community Detection

Karakitsou, Eleftheria, Fafoutellis, Panagiotis, Vlahogianni, Eleni I.

, 2023

Methodological framework of creating the Levitate Policy Support Tool for Connected and Automated Transport Systems

A Ziakopoulos, J Roussou, A Chaudhry, H Boghani, B Hu, M Zach, ...

Proceedings of the 8th Road Safety and Simulation International Conference, 1-10, 2022

Dilated LSTM networks for short-term traffic forecasting using network-wide vehicle trajectory data

Fafoutellis, P., Vlahogianni, E. I., & Del Ser, J.

23rd International Conference on Intelligent Transportation Systems (ITSC), IEEE, 2020

Road traffic forecasting: Recent advances and new challenges

I Lana, J Del Ser, M Velez, EI Vlahogianni

IEEE Intelligent Transportation Systems Magazine 10 (2), 93-109

Optimization of traffic forecasting: Intelligent surrogate modeling

EI Vlahogianni

Transportation Research Part C: Emerging Technologies 55, 14-23

Improving short-term traffic forecasts: To combine models or not to combine?

Dimitrios Tselentis, Matthew G. Karlaftis, Eleni I Vlahogianni

IET Intelligent Transport Systems, 9(2), 193 - 201

Short-term traffic forecasting: Where we are and where we’re going

EI Vlahogianni, MG Karlaftis, JC Golias

Transportation Research Part C: Emerging Technologies 43, 3-19

Testing and comparing neural network and statistical approaches for predicting transportation time series

Eleni I. Vlahogianni, Matthew G. Karlaftis

Transportation Research Record Journal of the Transportation Research Board 2399(2399):9-22

Temporal aggregation in traffic data: implications for statistical characteristics and model choice

Eleni Vlahogianni, Matthew Karlaftis

Transportation Letters 3 (1), 37-49, 2011

Comparing traffic flow time-series under fine and adverse weather conditions using recurrence-based complexity measures

Eleni I Vlahogianni, Matthew G. Karlaftis

Does Information on Weather Affect the Performance of Short-Term Traffic Forecasting Models?

L Tsirigotis, EI Vlahogianni, MG Karlaftis

International Journal of Intelligent Transportation Systems Research 10 (1)

Enhancing predictions in signalized arterials with information on short-term traffic flow dynamics

EI Vlahogianni

Journal of Intelligent Transportation Systems 13 (2), 73-84

Temporal evolution of short‐term urban traffic flow: a nonlinear dynamics approach

EI Vlahogianni, MG Karlaftis, JC Golias

Computer‐Aided Civil and Infrastructure Engineering 23 (7), 536-548

Spatio‐temporal short‐term urban traffic volume forecasting using genetically optimized modular networks

Eleni I. Vlahogianni, Matthew G. Karlaftis, John C. Golias

Computer‐Aided Civil and Infrastructure Engineering 22 (5), 317-325

Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traffic volume

Eleni I. Vlahogianni, Matthew G. Karlaftis, John C. Golias

Transportation Research Part C: Emerging Technologies 14 (5), 351-367, 2006

Pattern-based short-term urban traffic predictor

E Vlahogianni, M Karlaftis, J Golias, N Kourbelis

2006 IEEE Intelligent Transportation Systems Conference, 389-393

Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach

Eleni I. Vlahogianni, Matthew G. Karlaftis, John C. Golias

Transportation Research Part C: Emerging Technologies 13 (3), 211-234

Short‐term traffic forecasting: Overview of objectives and methods

EI Vlahogianni, JC Golias, MG Karlaftis

Transport reviews 24 (5), 533-557

PHD Theses

Traffic Theory Inspired Spatiotemporal and Multimodal Deep Learning Models for Trustworthy Short-Term Urban Traffic Forecasting

Panagiotis Fafoutellis

2024

#Traffic Forecasting

Projects

Publications

From Travel Times to Lane-Based Hourly Traffic Flow: An Explainable Machine Learning Framework

Revisiting the Non-recurrent Traffic Incident Identification Problem for Real-Time Applications Using Theory-Aware Unsupervised Learning

Quantum neural networks with data re-uploading for urban traffic time series forecasting

A theory-informed multivariate causal framework for trustworthy short-term urban traffic forecasting

Traffic demand prediction using a social multiplex networks representation on a multimodal and multisource dataset

Urban Arterial Traffic Volume and Travel Time Estimation with Use of Data Driven Models

Unlocking the full potential of deep learning in traffic forecasting through road network representations: A critical review

A Causal Deep Learning Framework for Traffic Forecasting

Efficient Traffic Demand Forecasting Using A Meaningful Representation With Social Multiplex Networks and Community Detection

Methodological framework of creating the Levitate Policy Support Tool for Connected and Automated Transport Systems

Dilated LSTM networks for short-term traffic forecasting using network-wide vehicle trajectory data

Road traffic forecasting: Recent advances and new challenges

Optimization of traffic forecasting: Intelligent surrogate modeling

Improving short-term traffic forecasts: To combine models or not to combine?

Short-term traffic forecasting: Where we are and where we’re going

Testing and comparing neural network and statistical approaches for predicting transportation time series

Temporal aggregation in traffic data: implications for statistical characteristics and model choice

Comparing traffic flow time-series under fine and adverse weather conditions using recurrence-based complexity measures

Does Information on Weather Affect the Performance of Short-Term Traffic Forecasting Models?

Enhancing predictions in signalized arterials with information on short-term traffic flow dynamics

Temporal evolution of short‐term urban traffic flow: a nonlinear dynamics approach

Spatio‐temporal short‐term urban traffic volume forecasting using genetically optimized modular networks

Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traffic volume

Pattern-based short-term urban traffic predictor

Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach

Short‐term traffic forecasting: Overview of objectives and methods

PHD Theses

Traffic Theory Inspired Spatiotemporal and Multimodal Deep Learning Models for Trustworthy Short-Term Urban Traffic Forecasting

News

General Assembly of the ACUMEN EU project, Luxembourg 🇱🇺

The 9th IEEE Conference on MT-ITS, Luxembourg 🇱🇺

Kick-off of the FEDORA EU project, Brussels 🇧🇪

Artificial Intelligence for Road Safety and Mobility Workshop, May 2025, Athens 🇬🇷