Network-wide Crowd Flow Prediction of Sydney Trains via Customized Online Non-negative Matrix Factorization

Gong, Yongshun; Li, Zhibin; Zhang, Jian; Liu, Wei; Zheng, Yu; Kirsch, Christina

doi:10.1145/3269206.3271757

Cited by 49 publications

(27 citation statements)

References 24 publications

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“…Index Terms-Adversarial machine learning, physical world attack, traffic sign recognition, deep neural networks I. INTRODUCTION I NTERNET of Things (IoT) applications in smart traffic control and smart cities are highly dependent on intelligent autonomous vehicles [1][2][3] [4]. The traffic sign recognition is the core function of such IoT driver assistance systems [5][6] [7].…”

Section: Targeted Attention Attack On Deep Learningmentioning

confidence: 99%

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Targeted Attention Attack on Deep Learning Models in Road Sign Recognition

Yang

Liu

Zhang

et al. 2021

IEEE Internet Things J.

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Section: Targeted Attention Attack On Deep Learningmentioning

confidence: 99%

“…The code for RP 2 attack is available from Github 3 , and the parameters are set as default [19]. We implement the FGSM [11] based on the CleverHans v3.0.1 4 , and run the rest methods on Foolbox v1.9.0 5 . All parameters are set as the recommended value.…”

Section: B Experimental Settingsmentioning

confidence: 99%

Targeted Attention Attack on Deep Learning Models in Road Sign Recognition

Yang

Liu

Zhang

et al. 2021

IEEE Internet Things J.

Self Cite

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“…Several recent works [28][29][30][31][32][33][34][35] specifically paid attention to the metro scenario. [28] addressed the crowd flow distribution prediction problem across the entire train network. [29,30] estimated the route choices of passengers in complex metro networks.…”

Section: Related Workmentioning

confidence: 99%

STP-TrellisNets: Spatial-Temporal Parallel TrellisNets for Metro Station Passenger Flow Prediction

Sun

Zhu

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Recent years have witnessed a drastic increase in the number of urban metro passengers, which inevitably causes the overcrowdedness in the metro systems of many cities. Clearly, an accurate prediction of passenger flows at metro stations is critical for a variety of metro system management operations, such as line scheduling and staff preallocation, that help alleviate such overcrowdedness. Thus, in this paper, we aim to address the problem of accurately predicting metro station passenger (MSP) flows. Similar to other traffic data, such as road traffic volume and highway speed, MSP flows are also spatial-temporal in nature. However, existing methods for other traffic prediction tasks are usually suboptimal to predict MSP flows due to MSP flows' unique spatial-temporal characteristics. As a result, we propose a novel deep learning framework STP-TrellisNets, which for the first time augments the newly-emerged temporal convolutional framework TrellisNet for spatial-temporal prediction. The temporal module of STP-TrellisNets (named CP-TrellisNets) employs two TrellisNets in serial to jointly capture the short-and long-term temporal correlation of MSP flows. In parallel to CP-TrellisNets, its spatial module (named GC-TrellisNet) adopts a novel transfer flow-based metric to characterize the spatial correlation among MSP flows, and implements multiple diffusion graph convolutional networks (DGCNs) in time-series order with their outputs connected to a TrellisNet to capture the dynamics of such spatial correlation. Clearly, GC-TrellisNet essentially integrates TrellisNet with graph convolution, and empowers TrellisNet with the ability to capture dynamic graph-structured correlation. We conduct extensive experiments with two large-scale real-world automated fare collection datasets, which contain respectively about 1.5 billion records in Shenzhen, China and 70 million records in Hangzhou, China. The experimental results demonstrate that STP-TrellisNets outperforms the state-of-the-art baselines. CCS CONCEPTS • Information systems → Spatial-temporal systems; Data mining; • Computing methodologies → Neural networks.

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“…Non-negative matrix factorization (NMF) is a popular solution for network-wide issues, and online NMF showed better performance in capturing temporal changes [49]. Gong et al [50] proposed a model taking advantage of the NMF model to capture the dynamic mobility in Sydney train stations. Although their ONMF-OA model could predict the stable flow of people, it could not capture sudden changes in flow.…”

Section: Crowd Spatio-temporal Analysismentioning

confidence: 99%

Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis

Ebrahimpour

Wan

Cervantes

et al. 2019

IJGI

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Extracting features from crowd flow analysis has become an important research challenge due to its social cost and the impact of inadequate planning of high-quality services and security monitoring on the lives of citizens. This paper descriptively reviews and compares existing crowd analysis approaches based on different data sources. This survey provides the fundamentals of crowd analysis and considers three main approaches: crowd video analysis, crowd spatio-temporal analysis, and crowd social media analysis. The key research contributions in each approach are presented, and the most significant techniques and algorithms used to improve the precision of results that could be integrated into solutions to enhance the quality of services in a smart city are analyzed.

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Network-wide Crowd Flow Prediction of Sydney Trains via Customized Online Non-negative Matrix Factorization

Cited by 49 publications

References 24 publications

Targeted Attention Attack on Deep Learning Models in Road Sign Recognition

Targeted Attention Attack on Deep Learning Models in Road Sign Recognition

STP-TrellisNets: Spatial-Temporal Parallel TrellisNets for Metro Station Passenger Flow Prediction

Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis

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