Comparative analysis of implicit models for real‐time short‐term traffic predictions

Fusco, Gaetano; Colombaroni, Chiara; Isaenko, Natalia

doi:10.1049/iet-its.2015.0136

Cited by 22 publications

(12 citation statements)

References 24 publications

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“…Data were suitably preprocessed to fill missed data and eliminate abnormal values and filtered to get smoothed data. In a very recent paper (Fusco et al, 2016), we compared different network-based short-term forecasting models on a 10-month long series of aggregated measures obtained from FCD and we proposed a model structure conceived to perform forecasts on large networks exploiting speed estimates on all the links where they are available.…”

Section: Related Workmentioning

confidence: 99%

“…We introduce different architectures of machine learning models based on different levels of exploration of the road network in order to catch possible spatial correlations among traffic measures taken on different links of the network. In contrast to our previous study (Fusco et al, 2016), where we used the historical average speed as an a priori estimation, we are here closer to the Bayesian approach and we try to provide an as good as possible a priori estimation based on previous observations. Thus, we formulate a hybrid modeling framework where we integrate the best a priori estimation based on time correlation, which is provided by a consolidated Seasonal ARIMA model, with the spatial correlation estimated through a Bayesian network.…”

Section: Paper Contributionmentioning

confidence: 99%

See 1 more Smart Citation

Short-term speed predictions exploiting big data on large urban road networks

Fusco

Colombaroni

Isaenko

2016

Transportation Research Part C: Emerging Technologies

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131

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Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes

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Section: Related Workmentioning

confidence: 99%

Section: Paper Contributionmentioning

confidence: 99%

Short-term speed predictions exploiting big data on large urban road networks

Fusco

Colombaroni

Isaenko

2016

Transportation Research Part C: Emerging Technologies

Self Cite

131

View full text Add to dashboard Cite

show abstract

“…In [24], Fusco et al hybridized Bayesian networks and NN to create short-term prediction models using as data the link speeds recorded on the metropolitan area of Rome during seven months. Other example where Bayesian networks are applied to short-term traffic prediction was presented in [81].…”

Section: Probabilistic Reasoning In Traffic State Predictionmentioning

confidence: 99%

Applications of Soft Computing in Intelligent Transportation Systems

Masegosa

Onieva

Osaba

2017

Soft Computing Based Optimization and Decision Models

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Intelligent Transportation Systems emerged to meet the increasing demand for more efficient, reliable and safer transportation systems. These systems combine electronic, communication and information technologies with traffic engineering to respond to the former challenges. The benefits of Intelligent Transportation Systems have been extensively proved in many different facets of transport and Soft Computing has played a major role in achieving these successful results. This book chapter aims at gathering and discussing some of the most relevant and recent advances of the application of Soft Computing in four important areas of Intelligent Transportation Systems as autonomous driving, traffic state prediction, vehicle route planning and vehicular ad hoc networks.

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“…Fusco et al [19] investigated the time-spatial correlation of speed measures and developed a forecasting structure based on the forward and backward star links. Later, Fusco et al [20] proposed a supervisor mechanism to select the best performer model depending on the different traffic regime, either recurrent or nonrecurrent congestion. As the most common use of spatial-temporal information in time series models, Griffith [21] provided an overview of five basic statistical methods for modelling space-time data.…”

Section: Introductionmentioning

confidence: 99%