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Spatiotemporal filtering method for detecting kinematic waves in a connected environment.

ABSTRACT: Backward-moving kinematic waves (KWs) (e.g., stop-and-go traffic conditions and a shock wave) cause unsafe driving conditions, decreases in the capacities of freeways, and increased travel time. In this paper, a sequential filtering method is proposed to detect KWs using data collected in a connected environment, which can aid in developing a traffic control strategy for connected vehicles to stop or dampen the propagation of these KWs. The proposed method filters out random fluctuation in the data using ensemble empirical mode decomposition that considers the spectral features of KWs. Then, the spatial movements of KWs are considered using cross-correlation to identify potential candidate KWs. Asynchronous changes in the denoised flow and speed are used to evaluate candidate KWs using logistic regression to identify the KWs from localized reductions in speed that are not propagated upstream. The findings from an empirical evaluation of the proposed method showed strong promise for detecting KWs using data in a connected environment, even at 30% of the market penetration rates. This paper also addresses how data resolution of the connected environment affects the performance in detecting KWs.

SUBMITTER: Kim EJ

PROVIDER: S-EPMC7751863 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Spatiotemporal filtering method for detecting kinematic waves in a connected environment.

Kim Eui-Jin EJ Kim Dong-Kyu DK Kho Seung-Young SY Chung Koohong K

PloS one 20201221 12

Backward-moving kinematic waves (KWs) (e.g., stop-and-go traffic conditions and a shock wave) cause unsafe driving conditions, decreases in the capacities of freeways, and increased travel time. In this paper, a sequential filtering method is proposed to detect KWs using data collected in a connected environment, which can aid in developing a traffic control strategy for connected vehicles to stop or dampen the propagation of these KWs. The proposed method filters out random fluctuation in the d ...[more]

PMID: 33347491

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Project description:The sudden spread of COVID-19 infections in a region can catch its healthcare system by surprise. Can one anticipate such a spread and allow healthcare administrators to prepare for a surge a priori? We posit that the answer lies in distinguishing between two types of waves in epidemic dynamics. The first kind resembles a spatio-temporal diffusion pattern. Its gradual spread allows administrators to marshal resources to combat the epidemic. The second kind is caused by super-spreader events, which provide shocks to the disease propagation dynamics. Such shocks simultaneously affect a large geographical region and leave little time for the healthcare system to respond. We use time-series analysis and epidemiological model estimation to detect and react to such simultaneous waves using COVID-19 data from the time when the B.1.617.2 (Delta) variant of the SARS-CoV-2 virus dominated the spread. We first analyze India's second wave from April to May 2021 that overwhelmed the Indian healthcare system. Then, we analyze data of COVID-19 infections in the United States (US) and countries with a high and low Indian diaspora. We identify the Kumbh Mela festival as the likely super-spreader event, the exogenous shock, behind India's second wave. We show that a multi-area compartmental epidemiological model does not fit such shock-induced disease dynamics well, in contrast to its performance with diffusion-type spread. The insufficient fit to infection data can be detected in the early stages of a shock-wave propagation and can be used as an early warning sign, providing valuable time for a planned healthcare response. Our analysis of COVID-19 infections in the US reveals that simultaneous waves due to super-spreader events in one country (India) can lead to simultaneous waves in other places. The US wave in the summer of 2021 does not fit a diffusion pattern either. We postulate that international travels from India may have caused this wave. To support that hypothesis, we demonstrate that countries with a high Indian diaspora exhibit infection growth soon after India's second wave, compared to countries with a low Indian diaspora. Based on our data analysis, we provide concrete policy recommendations at various stages of a simultaneous wave, including how to avoid it, how to detect it quickly after a potential super-spreader event occurs, and how to proactively contain its spread.

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Spatiotemporal filtering method for detecting kinematic waves in a connected environment.

Publications

Spatiotemporal filtering method for detecting kinematic waves in a connected environment.

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