Ensemble Learning for Crowd Flows Prediction on Campus

Wu, Chuting; Yin, Tianshu; Ge, Shuaijun; Yu, Ke

doi:10.1007/978-3-319-73830-7_11

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Wu et al [25] stack several models whose outputs are used as new features which will be sent as inputs of a new model.…”

Section: Reviewmentioning

confidence: 99%

“…The same cannot be reproduced for video data without requiring a huge calculation time. We opt for a compromise between a form of Stacking [25], without a meta-classifier because we combine the models at the evaluation phase, and a form of Bagging, because we perform an aggregation of models without applying Bootstrap sampling. Here the samples are the folds already obtained following the cross validation that we did for our previous work [6].…”

Section: Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble classification of video-recorded crowd movements

Bendali-Braham

Weber

Forestier

et al. 2021

2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)

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Ensemble learning methods often improve results in problems addressed by single Machine Learning models. In this work, we apply Ensemble Learning on video-recorded crowd movements. First, we build Ensembles of homogeneous Convolutional Neural Networks (CNN) to compare their performance on the Crowd-11 dataset and show the gain of performance demonstrated by Ensembles compared to single CNN models. Secondly, we evaluate all the possible combinations of these homogeneous Ensembles to build a global Ensemble of heterogeneous models, and we analyze the combination of Ensembles that achieves the best results. Our experiments reveal that Ensemble classification often obtains better results than single models and combining different Ensembles can make the predictions accuracy even better.

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“…Wu et al [25] stack several models whose outputs are used as new features which will be sent as inputs of a new model.…”

Section: Reviewmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

Ensemble classification of video-recorded crowd movements

Bendali-Braham

Weber

Forestier

et al. 2021

2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)

View full text Add to dashboard Cite

show abstract

“…Intelligent transportation system [1] is very important for the construction and development of modern cities. Tra c ow forecasting [2,3], as an indispensable part of the intelligent transportation system, can be used as an index to evaluate the road state. rough tra c ow forecasting, the government can better conduct urban management [4] as well as social security management.…”

Section: Introductionmentioning

confidence: 99%

CLSTAN: ConvLSTM-Based Spatiotemporal Attention Network for Traffic Flow Forecasting

Xiong

Ding

Huang

et al. 2022

Mathematical Problems in Engineering

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Traffic flow forecasting is the essential part of intelligent transportation sSystem (ITS), which can fully protect traffic safety and improve traffic system management capability. Nevertheless, it is still a challenging problem, which is influenced by many complex factors, including regional distribution and external factors (e.g., holidays and weather). To combine various factors to forecast traffic flow, we presented a novel neural network structure called ConvLSTM-based Spatiotemporal Attention Network (CLSTAN). Specifically, our proposed model is composed of four modules: a preliminary feature extraction module, a spatial attention module, a temporal attention module, and an information fusion module. The spatiotemporal attention module can efficiently learn the complex spatiotemporal patterns of traffic flow through the attention mechanism. The spatial attention module uses a series of initial traffic flow maps as input and obtains the weights of the various regions through a ConvLSTM. The temporal attention module uses the spatially weighted traffic flow map as input and acquires the complex spatiotemporal patterns of traffic flow by a ConvLSTM that introduces an attention mechanism. Finally, the information fusion module integrates spatiotemporal information from multiple time dimensions to forecast future traffic flow. Moreover, to confirm the validity of our method, our experiments were conducted extensively on the TaxiBJ and BikeNYC datasets, and ultimately, CLSTAN performed better than other baseline experiments.

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