The DOI matches its Crossref record.
Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models
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.
Overview
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Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The results indicate that both deep learning algorithms and the Bayesian method offer similar results in short-term traffic forecasting.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [18]
In the citing paper
“The implemented FF-NN indicated a slight advantage at the 10 and 15 min prediction over the other NN algorithm but it seems to be less effective than the BN-SARIMA approach.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [18]
In the citing paper
“considering the NARX method they revealed that it gave reliable 5 min predictions, similar to the BN-SARIMA.However, NARX performance was poorer in the other time ranges predictions.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [18]
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 20
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- 10.1016/S0925-2312(01)00702-0 · DOI
- 10.1061/(ASCE)0733-947X(2006)132:2(114) · DOI
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- mahmassani, 2005, DYNASMART-X evaluation for real-time TMC application: CHART test bed. Maryland Transportation Initiative, University of Maryland, 1
- 10.1016/j.trc.2015.03.041 · DOI
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- 10.1016/j.trc.2014.01.005 · DOI · Paper page
- 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
- van hinsbergen, 2007, Short term traffic prediction models, ITS World Congress
- oh, 2014, Short-term Travel-time Prediction on Highway: A Review of the Data-driven Approach, Transport Reviews, 1
- fusco, 2013, An integrated method for short-term prediction of road traffic conditions for Intelligent Transportation Systems Applications, 7th WSEAS European Computing Conference
- 10.1109/ITSC.2006.1707364 · DOI
- 10.1109/TITS.2006.869623 · DOI
- 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
A Review of Machine Learning and IoT in Smart Transportation
Zantalis, F., Koulouras, G., Karabetsos, S., & Kandris, D. (2019) · Future Internet · doi:10.3390/fi11040094 · Cited by 625
Metadata
- Authors
- Fusco, G., Colombaroni, C., Comelli, L., & Isaenko, N.
- Affiliations
- Sapienza University of Rome (IT)
- Venue
- 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 93-101
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models. https://app.cochat.ai/papers/10.1109/mtits.2015.7223242
- The paper
- Fusco, G., Colombaroni, C., Comelli, L., & Isaenko, N. (2015). Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models. 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). https://doi.org/10.1109/mtits.2015.7223242
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
