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

112 works between 2010 and 2024
All (112) Journal Article (45) Conference Publication (66) Other Outputs (1)

41 - 60 of 112 works

2020

Conference Publication

Investigating the impact of the connected environment on driver response time in car-following scenarios

Sharma, Anshuman, Zheng, Zuduo, Kim, Jiwon, Bhaskar, Ashish and Haque, Mazharul (2020). Investigating the impact of the connected environment on driver response time in car-following scenarios. Transportation Research Board Annual Meeting, Washington, DC, United States, 12-16 January 2020.

Investigating the impact of the connected environment on driver response time in car-following scenarios

2020

Conference Publication

Incorporating network traffic state for urban vehicle trajectory prediction

Choi, Seongjin, Kim, Jiwon and Yeo, Hwasoo (2020). Incorporating network traffic state for urban vehicle trajectory prediction. Transportation Research Board Annual Meeting, Washington, DC, United States, 12–16 January 2020.

Incorporating network traffic state for urban vehicle trajectory prediction

2020

Conference Publication

Drone-based vehicle identification: an empirical study of convolutional neural network performance

Hislop-Lynch, Samuel, Ahn, Sanghyung and Kim, Jiwon (2020). Drone-based vehicle identification: an empirical study of convolutional neural network performance. Transportation Research Board Annual Meeting, Washington, DC, United States, 12–16 January 2020.

Drone-based vehicle identification: an empirical study of convolutional neural network performance

2019

Journal Article

Deep-learning based urban vehicle trajectory prediction

Choi, Seongjin, Kim, Jiwon, Yu, Hwapyeong, Ka, Dongho and Yeo Hwasoo (2019). Deep-learning based urban vehicle trajectory prediction. Journal of Korean Society of Transportation, 37 (5), 422-429. doi: 10.7470/jkst.2019.37.5.422

Deep-learning based urban vehicle trajectory prediction

2019

Conference Publication

Real-time prediction of arterial vehicle trajectories: an application to predictive route guidance for an emergency vehicle

Choi, Seongjin, Kim, Jiwon, Yu, Hwapyeong and Yeo, Hwasoo (2019). Real-time prediction of arterial vehicle trajectories: an application to predictive route guidance for an emergency vehicle. 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27-30 October 2019. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ITSC.2019.8917122

Real-time prediction of arterial vehicle trajectories: an application to predictive route guidance for an emergency vehicle

2019

Journal Article

Behavioural advertising in the public transit network

Faroqi, Hamed, Mesbah, Mahmoud and Kim, Jiwon (2019). Behavioural advertising in the public transit network. Research in Transportation Business and Management, 32 100421, 100421. doi: 10.1016/j.rtbm.2019.100421

Behavioural advertising in the public transit network

2019

Journal Article

Comparing sequential with combined spatiotemporal clustering of passenger trips in the public transit network using smart card data

Faroqi, Hamed, Mesbah, Mahmoud and Kim, Jiwon (2019). Comparing sequential with combined spatiotemporal clustering of passenger trips in the public transit network using smart card data. Mathematical Problems in Engineering, 2019 (1) 5070794, 1-16. doi: 10.1155/2019/5070794

Comparing sequential with combined spatiotemporal clustering of passenger trips in the public transit network using smart card data

2019

Journal Article

Operational Ssenario definition in traffic simulation-based decision support systems: pattern recognition using a clustering algorithm

Chen, Ying, Kim, Jiwon and Mahmassani, Hani S. (2019). Operational Ssenario definition in traffic simulation-based decision support systems: pattern recognition using a clustering algorithm. Journal of Transportation Engineering Part A: Systems, 145 (4), 04019008. doi: 10.1061/JTEPBS.0000222

Operational Ssenario definition in traffic simulation-based decision support systems: pattern recognition using a clustering algorithm

2019

Journal Article

Estimating and comparing response times in traditional and connected environments

Sharma, Anshuman, Zheng, Zuduo, Kim, Jiwon, Bhaskar, Ashish and Haque, Md. Mazharul (2019). Estimating and comparing response times in traditional and connected environments. Transportation Research Record, 2673 (4), 036119811983796-684. doi: 10.1177/0361198119837964

Estimating and comparing response times in traditional and connected environments

2019

Journal Article

Forecasting pedestrian movements using recurrent neural networks: an application of crowd monitoring data

Duives, Dorine C., Wang, Guangxing and Kim, Jiwon (2019). Forecasting pedestrian movements using recurrent neural networks: an application of crowd monitoring data. Sensors, 19 (2) 382, 382. doi: 10.3390/s19020382

Forecasting pedestrian movements using recurrent neural networks: an application of crowd monitoring data

2019

Conference Publication

Estimating and comparing response times in traditional and connected environments

Sharma, Anshuman, Zheng, Zuduo, Kim, Jiwon, Bhaskar, Ashish and Haque, Mazharul (2019). Estimating and comparing response times in traditional and connected environments. Transportation Research Board Annual Meeting, Washington, DC, United States, 13 - 17 January 2019.

Estimating and comparing response times in traditional and connected environments

2019

Conference Publication

Attention-based recurrent neural network for urban vehicle trajectory prediction

Choi, Seongjin, Kim, Jiwon and Yeo, Hwasoo (2019). Attention-based recurrent neural network for urban vehicle trajectory prediction. 10th International Conference on Ambient Systems, Networks and Technologies (ANT 2019) / The 2nd International Conference on Emerging Data and Industry 4.0 (EDI40 2019), Leuven, Belgium, 29 April-2 May 2019. Amsterdam, Netherlands: Elsevier. doi: 10.1016/j.procs.2019.04.046

Attention-based recurrent neural network for urban vehicle trajectory prediction

2019

Conference Publication

Real-time prediction of inter-region traffic flow using urban vehicle trajectory data: a deep learning approach

Wang, Guangxing and Kim, Jiwon (2019). Real-time prediction of inter-region traffic flow using urban vehicle trajectory data: a deep learning approach. Transportation Research Board Annual Meeting, Washington, DC, United States, 13-17 January 2019.

Real-time prediction of inter-region traffic flow using urban vehicle trajectory data: a deep learning approach

2018

Journal Article

Network-wide vehicle trajectory prediction in urban traffic networks using deep learning

Choi, Seongjin, Yeo, Hwasoo and Kim, Jiwon (2018). Network-wide vehicle trajectory prediction in urban traffic networks using deep learning. Transportation Research Record, 2672 (45), 173-184. doi: 10.1177/0361198118794735

Network-wide vehicle trajectory prediction in urban traffic networks using deep learning

2018

Journal Article

Urban trajectory analytics: day-of-week movement pattern mining using tensor factorization

Naveh, Kianoosh Soltani and Kim, Jiwon (2018). Urban trajectory analytics: day-of-week movement pattern mining using tensor factorization. IEEE Transactions on Intelligent Transportation Systems, 20 (7) 8479365, 2540-2549. doi: 10.1109/TITS.2018.2868122

Urban trajectory analytics: day-of-week movement pattern mining using tensor factorization

2018

Journal Article

A model for measuring activity similarity between public transit passengers using smart card data

Faroqi, Hamed, Mesbah, Mahmoud, Kim, Jiwon and Tavassoli, Ahmad (2018). A model for measuring activity similarity between public transit passengers using smart card data. Travel Behaviour and Society, 13, 11-25. doi: 10.1016/j.tbs.2018.05.004

A model for measuring activity similarity between public transit passengers using smart card data

2018

Journal Article

Applications of transit smart cards beyond a fare collection tool: a literature review

Faroqi, H., Mesbah, M. and Kim, J. (2018). Applications of transit smart cards beyond a fare collection tool: a literature review. Advances in Transportation Studies, 45, 107-122. doi: 10.4399/978255166098

Applications of transit smart cards beyond a fare collection tool: a literature review

2018

Other Outputs

Too wet? Too cold? Too hot? This is how weather affects the trips we make

Corcoran, Jonathan, Pojani, Dorina, Rowe, Francisco, Zhou, Jiangping, Kim, Jiwon, Wei, Ming, Tao, Sui, Sigler, Thomas and Liu, Yan (2018, 04 09). Too wet? Too cold? Too hot? This is how weather affects the trips we make

Too wet? Too cold? Too hot? This is how weather affects the trips we make

2018

Journal Article

Identification of communities in urban mobility networks using multi-layer graphs of network traffic

Yildirimoglu, Mehmet and Kim, Jiwon (2018). Identification of communities in urban mobility networks using multi-layer graphs of network traffic. Transportation Research Part C: Emerging Technologies, 89, 254-267. doi: 10.1016/j.trc.2018.02.015

Identification of communities in urban mobility networks using multi-layer graphs of network traffic

2018

Journal Article

Time-dependent route scheduling on road networks

Li, Lei, Kim, Jiwon, Xu, Jiajie and Zhou, Xiaofang (2018). Time-dependent route scheduling on road networks. SIGSPATIAL Special, 10 (1), 10-14. doi: 10.1145/3231541.3231545

Time-dependent route scheduling on road networks

Current funding

  • 2024 - 2028
    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

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 - 2024
    Real-time Analytics on Urban Trajectory Data for Road Traffic Management
    ARC Linkage Projects
    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

    Interpretable Adversarial Inverse Reinforcement Learning for Driving Behaviour Learning

    Principal Advisor

    Other advisors: Dr SangHyung Ahn

  • Master Philosophy

    Deep Representation Learning of Spatio-Temporal Trajectory Data

    Principal Advisor

    Other advisors: Professor Mark Hickman

  • Doctor Philosophy

    Model interpretation and data-centric modelling for advanced traffic prediction

    Principal Advisor

  • Doctor Philosophy

    Zonal inference in congestion modelling

    Principal Advisor

    Other advisors: Honorary Professor Carlo Prato, Professor Zuduo Zheng

  • Master Philosophy

    Synthetic Travel Demand Generation using Data-Driven Methods

    Principal Advisor

    Other advisors: Honorary Professor Carlo Prato, Professor Zuduo Zheng

  • Doctor Philosophy

    Hybrid Deep Learning Platform for Real-time Traffic Incident Prediction and Management

    Principal Advisor

    Other advisors: Professor Mark Hickman

  • 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

    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

    An investigation on the allocation of fast-charging stations considering renewable energy sources

    Associate Advisor

    Other advisors: Dr Mehmet Yildirimoglu

  • Doctor Philosophy

    Methods for Public Transport Operations Planning and Management

    Associate Advisor

    Other advisors: Professor Mark Hickman

  • Doctor Philosophy

    Autonomous Eco-driving in the Vicinity of Signalized Intersection Using Deep Reinforcement Learning

    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

  • Doctor Philosophy

    Modelling Driving Behaviour of Mixed Autonomous and Human-Driven Vehicles Flow

    Associate Advisor

    Other advisors: Professor Zuduo Zheng

  • Doctor Philosophy

    Queue Length Estimation and Prediction at Isolated Signalized Intersections

    Associate Advisor

    Other advisors: Dr Mehmet Yildirimoglu

  • 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 Miao Xu

  • Master Philosophy

    Visualisation of passenger and freight transport flows

    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

Completed supervision

Enquiries

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communications@uq.edu.au