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Cited by 78Traffic Prediction and Management TechniquesTraffic control and managementTransportation Planning and Optimization

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Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models

Fusco, Gaetano, Colombaroni, Chiara, Comelli, Luciano, Isaenko, Natalia·2015·2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)
DOI 10.1109/mtits.2015.7223242
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Abstract

The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied on a sub-area of the road network of Rome and validated on the same floating car data set.

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Research works printed but not linked: 20

  1. 10.1109/TITS.2012.2200474 · DOI
  2. 10.1016/S0925-2312(01)00702-0 · DOI
  3. 10.1061/(ASCE)0733-947X(2006)132:2(114) · DOI
  4. 10.1016/j.trc.2014.02.005 · DOI
  5. ahmed, 1979, Analysis of freeway traffic time-series data by using Box-Jenkins techniques, Transportation Research Board, 722, 1
  6. 10.1061/(ASCE)0733-947X(2003)129:6(664) · DOI
  7. 10.1016/S0968-090X(02)00009-8 · DOI
  8. 10.1023/A:1012831808926 · DOI
  9. fusco, 2013, A quasi-dynamic traffic assignment model for large congested urban road networks, Mathematical Models and Methods in Applied Sciences, 7, 341
  10. mahmassani, 2005, DYNASMART-X evaluation for real-time TMC application: CHART test bed. Maryland Transportation Initiative, University of Maryland, 1
  11. 10.1016/j.trc.2015.03.041 · DOI
  12. 10.1109/WONS.2014.6814732 · DOI
  13. 10.1016/j.trc.2014.01.005 · DOI · Paper page
  14. fusco, 1995, The Use of Artificial Neural Networks in Advanced Traveler Information and Traffic Management Systems, Applications of Advanced Technologies in Transportation Engineering ASCE, 341
  15. van hinsbergen, 2007, Short term traffic prediction models, ITS World Congress
  16. oh, 2014, Short-term Travel-time Prediction on Highway: A Review of the Data-driven Approach, Transport Reviews, 1
  17. fusco, 2013, An integrated method for short-term prediction of road traffic conditions for Intelligent Transportation Systems Applications, 7th WSEAS European Computing Conference
  18. 10.1109/ITSC.2006.1707364 · DOI
  19. 10.1109/TITS.2006.869623 · DOI
  20. fusco, 2015, A comparative analysis of implicit models for real-time short-term traffic predictions, Proc of Scientific Seminar SIDT Transport Systems Methods and Technologies

Cited by

Metadata

Authors
Fusco, G., Colombaroni, C., Comelli, L., & Isaenko, N.
Affiliations
Sapienza University of Rome (IT)
ORCID
Venue
2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)
Publisher
IEEE
Published
2015-06
Pages
93-101
DOI
10.1109/mtits.2015.7223242
OpenAlex
W1509749701
Cited by
78
References
0
Citations by year
2026: 3 · 2025: 4 · 2024: 6 · 2023: 4 · 2022: 9 · 2021: 19 · 2020: 10 · 2019: 5 · 2018: 10 · 2017: 5 · 2016: 3
FWCI
12.49
Type
conference paper
Language
en
Open access
closed
Locations
  • International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (publishedVersion)
  • IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
Topics
  • Social Sciences › Transportation › Traffic Prediction and Management Techniques
  • Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
  • Social Sciences › Transportation › Transportation Planning and Optimization
Keywords
short-term traffic forecasting, urban road networks, Bayesian networks, artificial neural networks, dynamic traffic assignment, floating car data

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