Wenyan Hu

University of Melbourne

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Jinkai Tan

Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)

Sheng Chen

Chinese Academy of Sciences

Rui Li

Kunming University of Science and Technology

Jun Wang

Tianjin University of Finance and Economics

Jun Wang

Central China Normal University

Guangtao Dong

Climate Central

Kourosh Khoshelham

University of Melbourne

Mei‐Po Kwan

Chinese University of Hong Kong

Chia‐Ying Lee

Institute of Linguistics, Academia Sinica

Stephan Winter

University of Melbourne

Cooperative Institutions

East China Normal University

Chinese Academy of Sciences

University of Melbourne

Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)

Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)

Northwest Institute of Eco-Environment and Resources

Sun Yat-sen University

University of California, Berkeley

Hohai University

Chinese Academy of Medical Sciences & Peking Union Medical College

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Decentralized management of ephemeral traffic incidents

Transactions in GIS (2022)

Wenyan Hu Bingkun Chen Stephan Winter Kourosh Khoshelham

Abstract Ephemeral traffic incidents, such as a fallen tree on a road, pose traffic safety hazards, and impact locally on traffic. While these incidents are neither predictable nor persistent, their existence is relevant for all vehicles planning to pass by while the impact continues. This article develops a novel communication strategy for vehicular ad hoc networks aiming to inform all the affected vehicles, while involving only the minimum number of non‐affected vehicles. This strategy exploits time geography as a spatial and temporal filter, ensuring also that the information broadcasting timely terminates when the incident is over. Agent‐based traffic simulations show that, when a road is temporarily blocked due to an ephemeral incident, the proposed decentralized information management model achieves significant improvement in travel efficiency and automatically updates outdated incident information in time.

Ephemeral key

10.1111/tgis.12940

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Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression

Journal of Transport Geography (2023)

Mengya Li Mei‐Po Kwan Wenyan Hu Rui Li Jun Wang

Variables

10.1016/j.jtrangeo.2023.103720

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Projected changes of typhoon intensity in a regional climate model: Development of a machine learning bias correction scheme

International Journal of Climatology (2020)

Jinkai Tan Sheng Chen Chia‐Ying Lee Guangtao Dong Wenyan Hu

Abstract A machine learning‐based bias correction scheme was developed to adjust the simulated Western North Pacific typhoon intensity in a 25‐km regional climate model (RCM). The bias correction scheme, MLERA, consists of a hybrid neural network, which takes modelled atmospheric and oceanic conditions near the storm centre as input. We use air temperature, specific humidity, and relative vorticity at 300 hPa, geopotential height at 700 hPa, wind speed at 850 hPa, sea‐level pressure, 10‐m wind speed, and total air‐sea fluxes. Those predictors are selected using least absolute shrinkage and selection operator (Lasso) algorithm and principal component analysis (PCA). Because there is no ‘ground truth’ for RCM simulated storms, we train and test MLERA using ERA‐Interim reanalysis and best‐track data. The intensity statistics estimated from MLERA match well with those from observations. MLERA, when applied to RCM history base period, increases the likelihood of simulated typhoons (with wind speed >33 m/s) from 40.8 to 73.9% in the direct simulation. Meanwhile, the RCM itself produces no C3+ storms (wind speed >50 m/s) in both RCP4.5 and RCP8.5 scenarios, while MLERA significantly increases those storms. MLERA suggests an increasing trend of the frequency of violent typhoons from near‐term (2031–2060) to long‐term (2069–2098). Such an increase is consistent with our understanding that a warming climate will increase the storm intensity. The present study shows the potential of the machine learning technique for bias correcting cyclone intensity in RCMs. Furthermore, the proposed algorithm could also be applied to the next generation of high‐resolution global climate models, which may have a spatial grid spacing close to today's RCMs.

Typhoon

Intensity

Geopotential height

10.1002/joc.6987

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Citations (6)