On detection of emerging anomalous traffic patterns using GPS data

Pang, Linsey; Chawla, Sanjay; Liu, Wei; Zheng, Yu

doi:10.1016/j.datak.2013.05.002

Cited by 130 publications

(60 citation statements)

References 8 publications

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“…Pang and Linsey Xiaolin [4,5] partition city into uniform grids and report anomalies if traffic volumes in neighboring cells are different, while Shekhar [9] focuses on detecting spatial outliers in graph structured datasets. Similarly, Liu [10] and Chawla [11] partition the city into disjoint regions linked by major roads and then find unexpected traffic flow between any two regions.…”

Section: Anomalies Detection Methods Of Passenger Flowmentioning

confidence: 99%

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An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger Flow

Wang

Zhang

Líu

et al. 2018

Journal of Advanced Transportation

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Subway is an important transportation means for residents, since it is always on schedule. However, some temporal management policies or unpredicted events may change passenger flow and then affect passengers requirement for punctuality. Thus, detecting anomaly event, mining its propagation law, and revealing its potential impact are important and helpful for improving management strategy; e.g., subway emergency management can predict flow change under the condition of knowing specific policy and estimate traffic impact brought by some big events such as vocal concerts and ball games. In this paper, we propose a novel anomalies detection method of subway passenger flow. In this method, an improved robust principal component analysis model is presented to detect anomalies; then ST-DBSCAN algorithm is used to group the station-level anomaly data on space-time dimensions to reveal the propagation law and potential impact of different anomaly events. The real flow data of Beijing subway are used for experiments. The experimental results show that the proposed method is effective for detecting anomalies of subway passenger flow in practices.

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Section: Anomalies Detection Methods Of Passenger Flowmentioning

confidence: 99%

“…These methods are mostly applied in freeway and urban roads, and they link the main regions of a city and try to find unexpected traffic flow between any two regions [4,5]. As the subway is a different traffic system from the traditional road transportation, the above methods are difficult to introduce to subway system.…”

Section: Introductionmentioning

confidence: 99%

An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger Flow

Wang

Zhang

Líu

et al. 2018

Journal of Advanced Transportation

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“…Hence monitoring citywide traffic conditions through this approach is mainly based on human effort. Lastly, [7] X. Pang et al explains floating car data that can be generated by vehicles traveling around a city with a GPS sensor. The trajectories of these vehicles will be sent to a central system and matched to a road network for deriving speeds on road segments.…”

Section: Data Available For Traffic Managementmentioning

confidence: 99%

Urban Computing: Key Challenges and Issues of Traffic Management System

Mahiya¹,

Corneille²

2018

IJCA

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Urban Computing collects heterogeneous data from diverse sources,integrates it and analyzes it to deal with the challenges faced by the Urban cities. Due to rapid urbanization it has emerged as an important research area. Urban computing connects unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models,and novel visualization methods, to create win-win-win solutions that improve urban environment, human life quality, and city operation systems. It has various areas of application include cultural archiving, energy consumption, health, social interaction, transportation and environment. In this paper we discuss various key challenges and issues related to traffic management in urban cities.

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“…Approaches coming from different areas than flight transport exhibit characteristics that are helpful to the purpose of automated flight diversion detection. Examples include techniques for video surveillance [29,30,31,32], transportation monitoring [33,34], travel time analysis [35], and road traffic anomaly trajectory detection [36,37,38,39,40,41]. These approaches often use GPS [34,35] and Automatic Identification System (AIS) data [33] for monitoring purposes, and SVMs [31] for the detection of anomalies.…”

Section: Related Workmentioning

confidence: 99%

Detecting flight trajectory anomalies and predicting diversions in freight transportation

Ciccio

Cabanillas

et al. 2016

Decision Support Systems

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Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation.When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties. The dependence on public data results in a challenge to detect anomalous behavior without knowing the planned flight trajectory. Our work addresses this challenge by introducing a prediction model that just requires information on an airplane's position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behavior. When an airplane displays anomalous behavior for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting airplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilizing the proposed prediction model, logistics companies gain a significant amount of response time for these cases.

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On detection of emerging anomalous traffic patterns using GPS data

Cited by 130 publications

References 8 publications

An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger Flow

An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger Flow

Urban Computing: Key Challenges and Issues of Traffic Management System

Detecting flight trajectory anomalies and predicting diversions in freight transportation

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