Early Warning of City-Scale Unusual Social Event on Public Transportation Smartcard Data

Wang, Haiyang; Chen, Xiaming; Qiang, Siwei; Zhang, Honglun; Wang, Yongkun; Shi, Jianyong; Jin, Yaohui

doi:10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0048

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…al. [11] proposed a detection-based prediction framework for the early detection of unusual social crowd events. The aim of their model is to provide adequate information to the city administration team, in order to take necessary actions for traffic management, emergency management, and public safety.…”

Section: Computation and Analysisand Resultsmentioning

confidence: 99%

“…In a work proposed in [10], a large crowd information is detected in order to aid the transport managers and the travellers to plan transportation accordingly with the help of Singapore public transport system data set. In [11], an unusual social event prediction model is proposed which makes use of non-sensitive data of smart card users for the purpose of city administration. The model is evaluated by China's public transportation data.…”

Section: E Data Repository (Service Provider)mentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Transportation Cyber-Physical System: On Crowd through Event Detection for Public Urban Transportation

2019

IJRTE

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Intelligent Transportation System (ITS) has become the popular area of research since the last decade. Knowing the crowd density in every region of the city is of high importance for an ITS in delivering adequate transport facility to the public. Further, the occurrence of social events draw public crowd occasionally, in any specific region of the city. Thus, the combination of both the identification of crowd density and the social event detection lays a path to an interesting research in ITS. Having said, this paper proposes a methodology for the effective design of ITS with a primary focus on crowd sensing. The paper also presents a taxonomy of methods used to gather crowd density information through various sources. Furthermore, the research works that focused on event detection and crowd analysis are studied. Finally, the open challenges are identified and outlined which are promising research directions for ITS.

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Section: Computation and Analysisand Resultsmentioning

confidence: 99%

Section: E Data Repository (Service Provider)mentioning

confidence: 99%

Intelligent Transportation Cyber-Physical System: On Crowd through Event Detection for Public Urban Transportation

2019

IJRTE

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“…[6] used GPS trajectory data of vehicles to discover traffic jams. The research works [7] and [8] diagnosed traffic anomalies. The anomaly detection studied in this article focuses on discovering anomalous metro (traffic) states by detecting anomaly passenger (traffic) flow data.…”

Section: Related Workmentioning

confidence: 99%

Real‐time passenger flow anomaly detection in metro system

Wei

Zhang

et al. 2023

IET Intelligent Trans Sys

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Real‐time passenger‐flow anomaly detection at all metro stations is a very critical task for advanced Internet management. Robust principal component analysis (RPCA) based method has often been employed for anomaly detection task of multivariate time series data. However, it ignores the spatio‐temporal features of regular passenger‐flow patterns, resulting in a decrease in the accuracy of anomaly detection. In this paper, RT‐STRPCA model integrating temporal periodicity and spatial similarity is proposed to address the above issues. RT‐STRPCA model detects anomalies by decomposing the observation data into normal component and anomaly component. The spatio‐temporal constraints are taken into account to extract anomalies more accurately. The real‐time anomaly detection are realized by a sliding window. The performance of RT‐STRPCA model is evaluated on synthetic datasets and real‐world datasets. The experimental results on synthetic datasets demonstrate that the method achieves more accurate real‐time anomaly detection than baseline approaches and the result on real‐world datasets verify the utility and effectiveness of the proposed method.

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“…The records can be utilized to evaluate vehicle speeds and road conditions [91], [90], [87]. Besides, public transport card reading machines in bus and subway stations are another important user sensors that record the volume of people flows [65]. These various sensors can reflect the urban dynamics of one location from different perspectives.…”

Section: Urban Sensing Datamentioning

confidence: 99%

Urban Anomaly Analytics: Description, Detection, and Prediction

Zhang

et al. 2022

IEEE Trans. Big Data

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Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening are of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed.

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Early Warning of City-Scale Unusual Social Event on Public Transportation Smartcard Data

Cited by 5 publications

References 15 publications

Intelligent Transportation Cyber-Physical System: On Crowd through Event Detection for Public Urban Transportation

Intelligent Transportation Cyber-Physical System: On Crowd through Event Detection for Public Urban Transportation

Real‐time passenger flow anomaly detection in metro system

Urban Anomaly Analytics: Description, Detection, and Prediction

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