Associate Professor Jiwon Kim
Associate Professor

Jiwon Kim

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Phone: 
+61 7 334 63008

Background

Jiwon Kim is an Associate Professor in Transport Engineering and the Director of Higher Degree by Research in the School of Civil Engineering at the University of Queensland. She was a DECRA Fellow (2019-2022) sponsored by the Australian Research Council. She joined UQ in 2014 after completing her PhD research at Northwestern University. Prior to joining Northwestern, she worked at Samsung C&T (Engineering & Construction Group). She received Bachelor’s and Master’s degrees in civil engineering from Korea University.

Her research interests broadly encompass the application of Artificial Intelligence and Machine Learning (AI/ML) to enhance prediction, automation, and insight generation in transportation and urban mobility. She is passionate about developing intelligent autonomous systems that facilitate real-time traffic management and control, mobility service optimization, and traveller support. Her current research explores the potential of deep learning, reinforcement learning, and other cutting-edge AI/ML approaches to achieve these objectives.

Availability

Associate Professor Jiwon Kim is:
Available for supervision

Fields of research

Built Environment and Design Civil engineering Engineering Information and Computing Sciences Transport engineering Urban and regional planning

Qualifications

  • Doctor of Philosophy, Northwestern University

Research interests

  • Machine Learning and Artificial Intelligence (AI) applications in transport and logistics

    Data-driven modelling of traffic networks and mobility services; Data-driven traffic simulation; AI and multi-agent systems (reinforcement learning, imitation learning)

  • Predictive analytics for real-time traffic management and operations

    Real-time traffic estimation and prediction for Intelligent Transportation Systems (ITS); Traffic incident management; Decision support systems and automation

  • Urban Trajectory Data Analytics

    Urban vehicle trajectories in large-scale networks: data mining, pattern recognition, trajectory prediction and generation, and visualization; urban mobility insights and travel behavour

  • Traffic simulation and Traffic flow theory

    Traffic simulation (microsimulation/mesosimulation); Dynamic traffic assignment (DTA); Scenario generation and analysis

Search Professor Jiwon Kim’s works on UQ eSpace

111 works between 2010 and 2024
All (111) Journal Article (44) Conference Publication (66) Other Outputs (1)

1 - 20 of 111 works

2024

Journal Article

Eco-driving strategies using reinforcement learning for mixed traffic in the vicinity of signalized intersections

Yang, Zhiwei, Zheng, Zuduo, Kim, Jiwon and Rakha, Hesham (2024). Eco-driving strategies using reinforcement learning for mixed traffic in the vicinity of signalized intersections. Transportation Research Part C: Emerging Technologies, 165 104683, 104683. doi: 10.1016/j.trc.2024.104683

Eco-driving strategies using reinforcement learning for mixed traffic in the vicinity of signalized intersections

2023

Journal Article

Toward data-driven simulation of network-wide traffic: a multi-agent imitation learning approach using urban vehicle trajectory data

Sun, Jie and Kim, Jiwon (2023). Toward data-driven simulation of network-wide traffic: a multi-agent imitation learning approach using urban vehicle trajectory data. IEEE Transactions on Intelligent Transportation Systems, 25 (7), 6645-6657. doi: 10.1109/tits.2023.3343452

Toward data-driven simulation of network-wide traffic: a multi-agent imitation learning approach using urban vehicle trajectory data

2023

Journal Article

Variables affecting the risk of vehicle collisions in Australian road tunnels

Hidayat, Edwin, Lange, David, Karlovsek, Jurij and Kim, Jiwon (2023). Variables affecting the risk of vehicle collisions in Australian road tunnels. Journal of Road Safety, 34 (4), 20-30. doi: 10.33492/JACRS-D-22-00032

Variables affecting the risk of vehicle collisions in Australian road tunnels

2023

Journal Article

MSGNN: A Multi-structured Graph Neural Network model for real-time incident prediction in large traffic networks

Tran, Thanh, He, Dan, Kim, Jiwon and Hickman, Mark (2023). MSGNN: A Multi-structured Graph Neural Network model for real-time incident prediction in large traffic networks. Transportation Research Part C: Emerging Technologies, 156 104354. doi: 10.1016/j.trc.2023.104354

MSGNN: A Multi-structured Graph Neural Network model for real-time incident prediction in large traffic networks

2023

Journal Article

Multivariate time-varying Kalman filter approach for cycle-based maximum queue length estimation

Abewickrema, Wanuji, Yildirimoglu, Mehmet and Kim, Jiwon (2023). Multivariate time-varying Kalman filter approach for cycle-based maximum queue length estimation. Transportation Research Part C: Emerging Technologies, 154 104238, 1-19. doi: 10.1016/j.trc.2023.104238

Multivariate time-varying Kalman filter approach for cycle-based maximum queue length estimation

2023

Journal Article

A hierarchical multinomial logit model to examine the effects of signal strategies on right-turn crash injury severity at signalised intersections

Islam, Sheikh Manirul, Washington, Simon, Kim, Jiwon and Haque, Md Mazharul (2023). A hierarchical multinomial logit model to examine the effects of signal strategies on right-turn crash injury severity at signalised intersections. Accident Analysis and Prevention, 188 107091, 1-14. doi: 10.1016/j.aap.2023.107091

A hierarchical multinomial logit model to examine the effects of signal strategies on right-turn crash injury severity at signalised intersections

2023

Conference Publication

Map-matching on wireless traffic sensor data with a sequence-to-sequence model

Zhu, Zichun, He, Dan, Hua, Wen, Kim, Jiwon and Shi, Hua (2023). Map-matching on wireless traffic sensor data with a sequence-to-sequence model. 24th IEEE International Conference on Mobile Data Management (MDM), Singapore, Singapore, 3-6 July 2023. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/mdm58254.2023.00048

Map-matching on wireless traffic sensor data with a sequence-to-sequence model

2023

Conference Publication

A deep learning framework to generate synthetic mobility data

Arkangil, Eren, Yildirimoglu, Mehmet, Kim, Jiwon and Prato, Carlo (2023). A deep learning framework to generate synthetic mobility data. 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Nice, France, 14-16 June 2023. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/mt-its56129.2023.10241677

A deep learning framework to generate synthetic mobility data

2023

Journal Article

A Hierarchical Multinomial Logit model to examine the effects of signal strategies on right-turn crash risks by crash movement configuration

Islam, Sheikh Manirul, Washington, Simon, Kim, Jiwon and Haque, Md. Mazharul (2023). A Hierarchical Multinomial Logit model to examine the effects of signal strategies on right-turn crash risks by crash movement configuration. Accident Analysis and Prevention, 184 106993, 106993. doi: 10.1016/j.aap.2023.106993

A Hierarchical Multinomial Logit model to examine the effects of signal strategies on right-turn crash risks by crash movement configuration

2023

Journal Article

Autonomous anomaly detection on traffic flow time series with reinforcement learning

He, Dan, Kim, Jiwon, Shi, Hua and Ruan, Boyu (2023). Autonomous anomaly detection on traffic flow time series with reinforcement learning. Transportation Research Part C: Emerging Technologies, 150 104089, 1-21. doi: 10.1016/j.trc.2023.104089

Autonomous anomaly detection on traffic flow time series with reinforcement learning

2023

Conference Publication

MSGNN: a multi-structured graph neural network model for real-time incident prediction in large traffic networks

Tran, Thanh, He, Dan, Kim, Jiwon and Hickman, Mark (2023). MSGNN: a multi-structured graph neural network model for real-time incident prediction in large traffic networks. Transportation Research Board Annual Meeting, Washington, DC, United States, 8-12 January 2023.

MSGNN: a multi-structured graph neural network model for real-time incident prediction in large traffic networks

2023

Conference Publication

Multivariate time-varying Kalman filter approach for cycle-based maximum queue length estimation

Abewickrema, Wanuji, Yildirimoglu, Mehmet and Kim, Jiwon (2023). Multivariate time-varying Kalman filter approach for cycle-based maximum queue length estimation. Transportation Research Board Annual Meeting, Washington, DC United States, 8-12 January.

Multivariate time-varying Kalman filter approach for cycle-based maximum queue length estimation

2023

Conference Publication

Automatic traffic flow anomaly detection with reinforcement learning

He, Dan, Kim, Jiwon, Ruan, Boyu and Shi, Hua (2023). Automatic traffic flow anomaly detection with reinforcement learning. Transportation Research Board Annual Meeting, Washington, DC USA, 8-12 January 2023.

Automatic traffic flow anomaly detection with reinforcement learning

2023

Conference Publication

Interpretable data-driven car-following modelling with adversarial inverse reinforcement learning

Lin, Lin, Kim, Jiwon, Sun, Jie and Ahn, Sanghyung (2023). Interpretable data-driven car-following modelling with adversarial inverse reinforcement learning. Transportation Research Board Annual Meeting, Washington, DC, United States, 8-12 January 2023.

Interpretable data-driven car-following modelling with adversarial inverse reinforcement learning

2023

Conference Publication

An autonomous eco-driving strategy for mixed traffic in the vicinity of signalized intersection using deep reinforcement learning

Yang, Zhiwei, Zheng, Zuduo, Kim, Jiwon and Rakha, Hesham (2023). An autonomous eco-driving strategy for mixed traffic in the vicinity of signalized intersection using deep reinforcement learning. Transportation Research Board Annual Meeting, Washington, DC USA, 8-12 January 2023.

An autonomous eco-driving strategy for mixed traffic in the vicinity of signalized intersection using deep reinforcement learning

2023

Journal Article

Estimating link flows in road networks with synthetic trajectory data generation: inverse reinforcement learning approach

Zhong, Miner, Kim, Jiwon and Zheng, Zuduo (2023). Estimating link flows in road networks with synthetic trajectory data generation: inverse reinforcement learning approach. IEEE Open Journal of Intelligent Transportation Systems, 4, 14-29. doi: 10.1109/ojits.2022.3233904

Estimating link flows in road networks with synthetic trajectory data generation: inverse reinforcement learning approach

2022

Journal Article

An efficient algorithm for maximum trajectory coverage query with approximation guarantee

He, Dan, Zhou, Thomas, Zhou, Xiaofang and Kim, Jiwon (2022). An efficient algorithm for maximum trajectory coverage query with approximation guarantee. IEEE Transactions on Intelligent Transportation Systems, PP (99), 1-13. doi: 10.1109/tits.2022.3207499

An efficient algorithm for maximum trajectory coverage query with approximation guarantee

2022

Conference Publication

Online traffic incident prediction with hybrid graph-based neural network and continual learning

Tran, Thanh, He, Dan, Kim, Jiwon and Hickman, Mark (2022). Online traffic incident prediction with hybrid graph-based neural network and continual learning. Australasian Transport Research Forum, Adelaide, SA Australia, 28-30 September 2022.

Online traffic incident prediction with hybrid graph-based neural network and continual learning

2022

Conference Publication

A cooperative eco-driving system for mixed traffic on urban roads

Yang, Zhiwei, Zheng, Zuduo, Kim, Jiwon and Rakha, Hesham (2022). A cooperative eco-driving system for mixed traffic on urban roads. Australasian Transport Research Forum, Adelaide, 28-30 September 2022.

A cooperative eco-driving system for mixed traffic on urban roads

2022

Conference Publication

Threshold-free anomaly detection on traffic flow data with reinforcement learning

He, Dan, Ruan, Boyu, Kim, Jiwon and Shi, Hua (2022). Threshold-free anomaly detection on traffic flow data with reinforcement learning. Australasian Transport Research Forum, Adelaide, SA Australia, 28-30 September 2022.

Threshold-free anomaly detection on traffic flow data with reinforcement learning

Current funding

  • 2024 - 2026
    Safe and efficient eco-driving using connected and automated vehicles
    ARC Discovery Projects
    Open grant
  • 2020 - 2025
    Making Spatiotemporal Data More Useful: An Entity Linking Approach
    ARC Discovery Projects
    Open grant
  • 2019 - 2024
    Real-time Analytics on Urban Trajectory Data for Road Traffic Management
    ARC Linkage Projects
    Open grant

Past funding

  • 2022 - 2024
    Framework and Conceptual Solution for Integrating Road Network Operations Planning into Real-time Traffic Management
    iMove Cooperative Research Centre
    Open grant
  • 2022 - 2023
    UQ AWARE - Jiwon Kim
    UQ Amplify Women's Academic Research Equity
    Open grant
  • 2020 - 2021
    UQ AWARE - Dr Jiwon KIM
    UQ Amplify Women's Academic Research Equity
    Open grant
  • 2019 - 2022
    An Integrated Demographic and Transport Demand Modelling Framework (ID-TDM)
    iMove Cooperative Research Centre
    Open grant
  • 2019 - 2023
    Data-driven Simulation of Large Traffic Networks using Trajectory Data
    ARC Discovery Early Career Researcher Award
    Open grant
  • 2018 - 2019
    Urban mobility analytics-based crash risk prediction for real-time traffic incident management
    UQ Early Career Researcher
    Open grant

Availability

Associate Professor Jiwon Kim is:
Available for supervision

Before you email them, read our advice on how to contact a supervisor.

Available projects

  • We are currently hiring a research assistant / software developer.

    This Advanced Research Assistant will work as a part of Dr Jiwon Kim's research team to provide a technical support for developing and maintaining open-source software that is designed to analyse large-scale urban mobility data, specifically spatio-temporal trajectory data of vehicles and people travelling around a city (e.g., trajectories from GPS, Bluetooth, cellphones, and transit smart cards).

    Please contact jiwon.kim@uq.edu.au for more information.

  • Intelligent Transport Systems (ITS)

    - Big data analytics and artificial intelligence (AI) applications

    - Real-time traffic management and control

    - Spatio-temporal analysis of trajectory data in road networks

    - Data-driven approaches to traffic estimation and prediction

    - Congestion management and avoidance

    - Incident detection and traffic incident management

  • AI and Machine Learning for Urban Mobility

    - Learning human mobility behaviours from large-scale movement data

    - Imitation learning for human behaviour modelling in traffic networks

    - Multi-agent reinforcement learning for network traffic managmenet

  • Traffic flow theory and simulation

    - Advanced analysis techniques for micro- and meso-scopic traffic simulation models

    - Analysis of traffic flow breakdown phenomena

    - Modeling driver behavior and traffic flow characteristics under a connected and/or autonomous vehicle environment (e.g., V2V, V2I, and self-driving car)

    - Analysis of traffic flow variables using new sources of data (e.g., GPS devices, RFID tags, radar, and video)

  • Other topics

    - Use of video data (e.g., CCTV, drones) for traffic data collection and analysis

    - Resilient transport systems; vulnerability and risk assessment of road networks related to extreme weather events

    Dr. Kim is also happy to consider other topics related to transport planning and operations, traffic modeling and analysis, and urban traffic management.

Supervision history

Current supervision

  • Doctor Philosophy

    Model interpretation and data-centric modeling for advanced traffic prediction (MINDMAP)

    Principal Advisor

    Other advisors: Dr SangHyung Ahn, Dr Mehmet Yildirimoglu

  • Doctor Philosophy

    Zonal inference in congestion modelling

    Principal Advisor

    Other advisors: Honorary Professor Carlo Prato, Professor Zuduo Zheng

  • Doctor Philosophy

    Graph-based Learning Platform for Real-time Traffic Incident Prediction and Management

    Principal Advisor

    Other advisors: Professor Mark Hickman

  • Doctor Philosophy

    Data-driven Modelling of Urban Traffic Networks using Spatial Trajectory Data

    Principal Advisor

    Other advisors: Dr SangHyung Ahn

  • Doctor Philosophy

    Real-time Analytics on Urban Trajectory Data for Road Traffic Management

    Associate Advisor

    Other advisors: Dr Miao Xu

  • Doctor Philosophy

    Methods for Public Transport Operations Planning and Management

    Associate Advisor

    Other advisors: Professor Mark Hickman

  • Doctor Philosophy

    Operation Strategy of Shared Autonomous Vehicles with Various Passenger Capacity on the Basis of Ridesharing

    Associate Advisor

    Other advisors: Professor Zuduo Zheng

  • Doctor Philosophy

    Real-time Analytics on Urban Trajectory Data for Road Traffic Management

    Associate Advisor

    Other advisors: Dr Mehmet Yildirimoglu

  • Master Philosophy

    Visualisation of passenger and freight transport flows

    Associate Advisor

    Other advisors: Professor Mark Hickman

Completed supervision

Enquiries

For media enquiries about Associate Professor Jiwon Kim's areas of expertise, story ideas and help finding experts, contact our Media team:

communications@uq.edu.au