BTCI: A new framework for identifying congestion cascades using bus trajectory data

Chiang, Meng-Fen; Lim, Ee-Peng; Lee, Wang-Chien; Kwee, Agus Trisnajaya

doi:10.1109/bigdata.2017.8258039

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…The principle of detecting anomalies is that the density around a normal sample is similar to that around its neighbors. Chiang et al [44] designed a twostep congestion cascades identification strategy, where they used a non-parametric Kernel Density Estimation function to compute anomaly score for road segments in the first step. Congested cascades are then formed by unifying both attribute coherence and spatio-temporal closeness of detected congested segments.…”

Section: F Results and Discussion For Server Machine Datamentioning

confidence: 99%

Generative Anomaly Detection for Time Series Datasets

Kang¹,

Mukhopadhyay²,

Gokhale³

et al. 2022

Preprint

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Section: F Results and Discussion For Server Machine Datamentioning

confidence: 99%

Generative Anomaly Detection for Time Series Datasets

Kang¹,

Mukhopadhyay²,

Gokhale³

et al. 2022

Preprint

View full text Add to dashboard Cite

“…For example, the literature has explored various applications of data analysis techniques in transportation research. These include the detection of anomalous traffic patterns (Kong et al., 2018; Zhang, Zhang, et al., 2019), the identification of congestion cascades (Chiang et al., 2017), the optimization of bus route planning (Chien et al., 2003; Zimmermann et al., 2021), the analysis of regional mobility patterns of bus travelers (Qi et al., 2018), the examination of bus network structure (Badia et al., 2016), and so on.…”

Section: Related Workmentioning

confidence: 99%

Revealing the mobile sensing powers of semi‐random and deterministic “drive‐by” vehicle fleets

Jiang,

Wang,

Yao

et al. 2023

Transactions in GIS

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The utilization of mobile sensing through the deployment of sensors on third‐party vehicles that pass by an area offers notable benefits in terms of spatiotemporal coverage, precision, timeliness, and cost‐effectiveness for monitoring urban environments. However, the diverse travel behaviors exhibited by different types of vehicle fleets result in varying levels of sensing powers. Drawing inspiration from these phenomena, this study quantifies and compares the travel patterns and sensing powers of bus and taxi fleets. The experimental findings unveiled several noteworthy laws, such as a diminishing rate of increase in sensing power with the stable increasing vehicle number, the high sensing power exhibited by taxis, the comparable spatial coverage between buses, and the relatively low uncertainty associated with buses' sensing power. Furthermore, the impact of travel patterns (i.e., semi‐random behavior of taxis and deterministic behavior of buses) on the powers of mobile sensing was revealed. The aforementioned findings provide valuable insights for the development of efficient mobile sensing schemes and hold significant relevance for the implementation of smart city infrastructure.

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“…In other words, if the road network is regarded as a graph, a traffic anomaly is more likely an anomalous subgraph instead of an abnormal edge. Based on this consideration, some works detected a group of connected road segments as an abnormal group [51], [68]. In [21], [128], the authors further explored the causal interactions among road segments and identified the root cause of traffic anomalies.…”

Section: Traffic Anomalymentioning

confidence: 99%

“…4.1.1.1 Simple physical feature: Simple physical features are widely used because they are easy to access and have clear real world meanings. In [64], [68], the average vehicle speed is used to detect traffic accidents or congestion. They divided urban road networks into small segments and estimated the traffic flow speed based on trajectories in a small time slot.…”

Section: Spatiotemporal Feature Basedmentioning

confidence: 99%

“…Kernel function based model [135] is a nonparametric method that estimates probability density using kernel functions, which make no prior assumptions about the distribution. This method is used in [68], [60] to model the distribution of traffic speed data and sharing bike renting data. One-class SVM, as a classification based anomaly detection algorithm, learns the region that contains normal points in feature space using kernel function.…”

Section: Spatiotemporal Feature Basedmentioning

confidence: 99%

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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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BTCI: A new framework for identifying congestion cascades using bus trajectory data

Cited by 11 publications

References 19 publications

Generative Anomaly Detection for Time Series Datasets

Generative Anomaly Detection for Time Series Datasets

Revealing the mobile sensing powers of semi‐random and deterministic “drive‐by” vehicle fleets

Urban Anomaly Analytics: Description, Detection, and Prediction

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