Quantifying incident impacts and identifying influential features in urban traffic networks

Lee, Juyeong; JiIn, Kwak,; Oh, YongKyung; Kim, Sungil

doi:10.1080/21680566.2022.2063205

Cited by 3 publications

(1 citation statement)

References 45 publications

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“…A GPS trajectory consists of a series of points with latitude, longitude, and timestamp information generated when navigation service users travel. Based on GPS trajectory data, we aligned the sequence of observed user positions with the road network using a map-matching process [32] to obtain the average speed of each road. For all the incoming neighbor roads of the accident road, their average speed data were recorded at 5-min intervals from the previous 3 h to the subsequent 3 h based on the time when the accident occurred.…”

Section: F I G U R Ementioning

confidence: 99%

Spatially‐correlated time series clustering using location‐dependent Dirichlet process mixture model

Jung,

Kim,

Kim

2023

Statistical Analysis

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The Dirichlet process mixture (DPM) model has been widely used as a Bayesian nonparametric model for clustering. However, the exchangeability assumption of the Dirichlet process is not valid for clustering spatially correlated time series as these data are indexed spatially and temporally. While analyzing spatially correlated time series, correlations between observations at proximal times and locations must be appropriately considered. In this study, we propose a location‐dependent DPM model by extending the traditional DPM model for clustering spatially correlated time series. We model the temporal pattern as an infinite mixture of Gaussian processes while considering spatial dependency using a location‐dependent Dirichlet process prior over mixture components. This encourages the assignment of observations from proximal locations to the same cluster. By contrast, because mixture atoms for modeling temporal patterns are shared across space, observations with similar temporal patterns can be still grouped together even if they are located far apart. The proposed model also allows the number of clusters to be automatically determined in the clustering procedure. We validate the proposed model using simulated examples. Moreover, in a real case study, we cluster adjacent roads based on their traffic speed patterns that have changed as a result of a traffic accident occurred in Seoul, South Korea.

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Section: F I G U R Ementioning

confidence: 99%

Spatially‐correlated time series clustering using location‐dependent Dirichlet process mixture model

Jung,

Kim,

Kim

2023

Statistical Analysis

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RoadFormer: Road-Anchored Adversarial Dynamic Graph Transformer for Unlimited-Range Traffic Incident Impact Prediction

Sun,

Fu,

2023

2023 IEEE International Conference on Big Data (BigData)

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Time delay estimation of traffic congestion propagation due to accidents based on statistical causality

JiIn

Kim

2023

era

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<abstract><p>The accurate estimation of time delays is crucial in traffic congestion analysis, as this information can be used to address fundamental questions regarding the origin and propagation of traffic congestion. However, the exact measurement of time delays during congestion remains a challenge owing to the complex propagation process between roads and high uncertainty regarding future behavior. To overcome this challenge, we propose a novel time delay estimation method for the propagation of traffic congestion due to accidents using lag-specific transfer entropy (TE). The proposed method adopts Markov bootstrap techniques to quantify uncertainty in the time delay estimator. To the best of our knowledge, our proposed method is the first to estimate time delays based on causal relationships between adjacent roads. We validated the method's efficacy using simulated data, as well as real user trajectory data obtained from a major GPS navigation system in South Korea.</p></abstract>

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Quantifying incident impacts and identifying influential features in urban traffic networks

Cited by 3 publications

References 45 publications

Spatially‐correlated time series clustering using location‐dependent Dirichlet process mixture model

Spatially‐correlated time series clustering using location‐dependent Dirichlet process mixture model

RoadFormer: Road-Anchored Adversarial Dynamic Graph Transformer for Unlimited-Range Traffic Incident Impact Prediction

Time delay estimation of traffic congestion propagation due to accidents based on statistical causality

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