A procedure for urban route travel time forecast based on advanced traffic data: Case study of Rome

Cipriani, Ernesto; Gori, Stefano; Mannini, Livia; Brinchi, Stefano

doi:10.1109/itsc.2014.6957809

Cited by 10 publications

(9 citation statements)

References 21 publications

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“…The use of FCD for traffic modelling and travel demand estimation is widely discussed in literature [18][19][20][21][22][23][24][25]. For the scope of this study, FCD were used to estimate the Origin-Destination (O-D) matrices following the procedure intro-duced by Eisemann and List [26] and refined by Carrese et al [27] and Nigro et al [28].…”

Section: Traflc Simulation From Fcdmentioning

confidence: 99%

Analysing urban traffic volumes and mapping noise emissions in Rome (Italy) in the context of containment measures for the COVID-19 disease

et al. 2020

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This study presents the result of a traffic simulation analysis based on Floating Car Data and a noise emission assessment to show the impact of mobility restriction for COVID-19 containment on urban vehicular traffic and road noise pollution on the road network of Rome, Italy. The adoption of strong and severe measures to contain the spreading of Coronavirus during March-April 2020 generated a significant reduction in private vehicle trips in the city of Rome (-64.6% during the lockdown). Traffic volumes, obtained through a simulation approach, were used as input parameters for a noise emission assessment conducted using the CNOSSOS-EU method, and an overall noise emissions reduction on the entire road network was found, even if its extent varied between road types.

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Section: Traflc Simulation From Fcdmentioning

confidence: 99%

Analysing urban traffic volumes and mapping noise emissions in Rome (Italy) in the context of containment measures for the COVID-19 disease

et al. 2020

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“…eRCNN achieves the best traffic flow prediction accuracy on all I5 freeway sections. 10 eRLSTM and eRCNN+LSTM also achieve good accuracy when predicting the traffic flow. However, these two models can be outperformed by the LSTM under certain scenarios.…”

Section: Traffic Predictionmentioning

confidence: 93%

“…The results also show that LSTM outperforms all other models that do not utilize the error feedback. 10 It is interesting to observe that while LSTM outperformed CNN for both traffic variables, eRCNN outperforms eRLSTM. The error feedback has then a more positive effect on CNN models than on LSTM ones.…”

Section: Traffic Predictionmentioning

confidence: 97%

“…The study models the traffic variables like a dynamic system in the state-space. [10] also uses a Kalman filter for traffic prediction and fuses data provided by different traffic detectors. A disadvantage of parametric models is that they need to be readjusted every time we change the set of parameters that define the model.…”

Section: State Of the Art On Traffic Predictionmentioning

confidence: 99%

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A Comprehensive Evaluation of Deep Learning-Based Techniques for Traffic Prediction

Mena-Oreja

Gozálvez

2020

IEEE Access

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Deep learning-based techniques are the state of the art in road traffic prediction or forecasting. Several deep neural networks have been proposed to predict the traffic but they have not been evaluated under common datasets. Current studies analyze their capacity to predict road traffic in general but do not focus on their capacity to predict the formation of congestions. This is critical for avoiding congestions or mitigate their negative impact. This paper progresses the current state of the art by presenting a comprehensive comparison of the state-of-the-art deep neural networks for road traffic prediction. The comparison is conducted using the same real traffic datasets, and under normal and congested traffic conditions. The evaluation includes new deep neural networks and error recurrent models. Our study first demonstrates that accurately predicting the traffic overall does not imply that a deep neural network can accurately predict the traffic when congestions are being formed. This reinforces the idea that prediction techniques must also be evaluated under congestion conditions. Our analysis also shows that exploiting the spatiotemporal evolution of the traffic (and not just the temporal one) provides better prediction accuracy overall and in particular under congestion conditions. The study also demonstrates that error recurrent models outperform deep neural networks that do not utilize an error feedback both under normal and congested traffic conditions. In particular, our study shows that the error recurrent model eRCNN is the deep learning technique that achieves to date the best traffic prediction accuracy. It is also important emphasizing that error recurrent models achieve better prediction accuracy with shallower neural networks and therefore lower computational cost.

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“…neural networks [9][10][11][12] or Kalman modelling [13], fusing additional data sources e.g. automatic license plate recognition [14].…”

Section: Introductionmentioning

confidence: 99%

Real-time traffic monitoring by fusing floating car data with stationary detector data

Houbraken

Audenaert

Colle

et al. 2015

2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

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Abstract-Applying the current technological possibilities has led to a wide range of traffic monitoring systems. These heterogeneous data sources individually provide a view on the current traffic state, each source having its own properties and (dis)advantages. However, these different sources can be aggregated to create a single traffic state estimation. This paper presents a data fusion algorithm that combines data on the data sample level. The proposed system fuses floating car data with stationary detector data and was implemented on live traffic. Results show the fusion algorithm allows to eliminate individual source bias and alleviates source-specific limitations.

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A procedure for urban route travel time forecast based on advanced traffic data: Case study of Rome

Cited by 10 publications

References 21 publications

Analysing urban traffic volumes and mapping noise emissions in Rome (Italy) in the context of containment measures for the COVID-19 disease

Analysing urban traffic volumes and mapping noise emissions in Rome (Italy) in the context of containment measures for the COVID-19 disease

A Comprehensive Evaluation of Deep Learning-Based Techniques for Traffic Prediction

Real-time traffic monitoring by fusing floating car data with stationary detector data

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