QTIP: Quick simulation-based adaptation of traffic model per incident parameters

Peled, Inon; Kamalakar, Raghuveer; Azevedo, Carlos Lima; Pereira, Francisco C.

doi:10.1080/17477778.2020.1756702

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…The authors selected several gradient boosting regression trees models with different structures and parameters and used the prediction results of these models as the candidates in the Lasso ensemble framework to produce the final prediction. Peled et al [39] used an ordinary prediction model for ordinary traffic flow and another model under incident conditions by simulating real-time traffic incidents. Castro-Neto et al [40] showed that an online-SVR model outperforms Gaussian maximum likelihood, Holt exponential smoothing, and ANN under anomalous traffic conditions because the model can learn patterns from the most recently collected data, which can capture the irregular real-time trend of traffic.…”

Section: Other Traffic Prediction Problems Under Anomalous Conditionsmentioning

confidence: 99%

Hybrid model for predicting anomalous large passenger flow in urban metros

Zheng

Ling

Wang

et al. 2020

IET intell. transp. syst

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Machine learning models have been widely adopted for passenger flow prediction in urban metros; however, the authors find machine learning models may underperform under anomalous large passenger flow conditions. In this study, they develop a prediction framework that combines the advantage of complex network models in capturing the collective behaviour of passengers and the advantage of online learning algorithms in characterising rapid changes in real-time data. The proposed method considerably improves the accuracy of passenger flow prediction under anomalous conditions. This study can also serve as an exploration of interdisciplinary methods for transportation research.

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Section: Other Traffic Prediction Problems Under Anomalous Conditionsmentioning

confidence: 99%