Neural congestion prediction system for trip modelling in heterogeneous spatio-temporal patterns

Elleuch, W.; Wali, Ali; Alimi, Adel M.

doi:10.1080/00207721.2020.1760957

Cited by 13 publications

(5 citation statements)

References 28 publications

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“…The experimental environment was built on a Hadoop cluster with a MapReduce framework and ArcGIS platform, where the base hardware was an HP Z400 workstation with an Intel Xeon 3500 CPU and ECC and DDR3 8.0 GB RAM. We ran all experiments with Hadoop 3.1.1 using Java, Python on Ubuntu 18.64 OS [ 20 , 21 ].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…The MAPEs of the WND-LSTM model are significantly lower than those of the other models in most cases, especially for the 14-day dataset, where the MAPEs of the WND-LSTM are lower than those of the LSTM, W-LSTM, and ND-LSTM by 70.41%, 68.92%, and 46.77%, respectively. In particular, the MAPE value for WND-LSTM is 0.651%, indicating a high predictive power [ 20 ].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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Traffic Flow Prediction and Analysis in Smart Cities Based on the WND-LSTM Model

Zhao

2022

Computational Intelligence and Neuroscience

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Aiming at the problem that the road traffic flow in intelligent city is unevenly distributed in time and space, difficult to predict, and prone to traffic congestion, combined with pattern recognition and big data mining technology, this paper proposes a research method to analyze and mine the daily travel patterns of urban vehicles. This paper proposes a WND-LSTM model, which mainly includes data preprocessing, data modelling, and model implementation, to analyze the similarity of travel patterns in seasonal changes. Combining the data mining results with the data mining results, the daily travel model of road traffic vehicles in intelligent city is established. The results of the case study showed that the WND-LSTM model outperformed ARIMA (88.48%), LR (65.79%), SVR (70.46%), KNN (68.21%), SAEs (66.95%), GRU (68.43%), and LSTM (70.41%) in MAPE, respectively, with an average accuracy improvement of 71.25% (MAPE of 0.651%).

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Experimental Results and Analysismentioning

confidence: 99%

Traffic Flow Prediction and Analysis in Smart Cities Based on the WND-LSTM Model

Zhao

2022

Computational Intelligence and Neuroscience

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“…Additionally, they can help identify road segment connections where limited or no fixed sensors are deployed (Nagy and Simon, 2018;Hu et al, 2021). Floating Car Data (FCD) is another subset of trajectory which can be generated by smartphones or vehicles equipped with GNSS receivers (Elleuch et al, 2020). When the data is collected by cellular phones using the cellular network, it is generally referred to as Cellular Floating Car Data (CFCD).…”

Section: Spatio-temporal Sequence Datamentioning

confidence: 99%

Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities

Shaygan¹,

Meese²,

Li³

et al. 2022

Transportation Research Part C: Emerging Technologies

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“…Since this sensing technique only captures a fraction of the circulating vehicles, FCD is usually utilized to predict flow values of certain vehicles type, and is hence not suitable for general flow forecasting problems. Research contributions are more balanced towards traffic speed prediction, covering all data type and granularity combinations, except for FCD at freeways, where only one work has been found [154]. PeMS and Los Angeles County highway dataset (METR-LA) [206] are the preferred option when looking for freeway speed RCD.…”

Section: B Understanding Deep Learning Based Short-term Traffic Forec...mentioning

confidence: 99%

Deep Learning for Road Traffic Forecasting: Does it Make a Difference?

Manibardo¹,

Laña²,

Ser³

2020

Preprint

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Deep Learning methods have been proven to be flexible to model complex phenomena. This has also been the case of Intelligent Transportation Systems (ITS), in which several areas such as vehicular perception and traffic analysis have widely embraced Deep Learning as a core modeling technology. Particularly in short-term traffic forecasting, the capability of Deep Learning to deliver good results has generated a prevalent inertia towards using Deep Learning models, without examining in depth their benefits and downsides. This paper focuses on critically analyzing the state of the art in what refers to the use of Deep Learning for this particular ITS research area. To this end, we elaborate on the findings distilled from a review of publications from recent years, based on two taxonomic criteria. A posterior critical analysis is held to formulate questions and trigger a necessary debate about the issues of Deep Learning for traffic forecasting. The study is completed with a benchmark of diverse short-term traffic forecasting methods over traffic datasets of different nature, aimed to cover a wide spectrum of possible scenarios. Our experimentation reveals that Deep Learning could not be the best modeling technique for every case, which unveils some caveats unconsidered to date that should be addressed by the community in prospective studies. These insights reveal new challenges and research opportunities in road traffic forecasting, which are enumerated and discussed thoroughly, with the intention of inspiring and guiding future research efforts in this field.

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Neural congestion prediction system for trip modelling in heterogeneous spatio-temporal patterns

Cited by 13 publications

References 28 publications

Traffic Flow Prediction and Analysis in Smart Cities Based on the WND-LSTM Model

Traffic Flow Prediction and Analysis in Smart Cities Based on the WND-LSTM Model

Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities

Deep Learning for Road Traffic Forecasting: Does it Make a Difference?

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