Dr Jiwon Kim

Lecturer

School of Civil Engineering
Faculty of Engineering, Architecture and Information Technology
jiwon.kim@uq.edu.au
+61 7 334 63008

Overview

Jiwon Kim is Lecturer in Transport Engineering in the School of Civil Engineering. She joined UQ in 2014 after completing her PhD research at Northwestern University. Her doctoral work was on the travel time reliability of transportation networks. Prior to joining Northwestern, she worked as an assistant manager at Samsung C&T (Engineering & Construction Group) from 2005 to 2008. She received Bachelor’s and Master’s degrees in civil engineering from Korea University in 2003 and 2005, respectively.

Dr. Kim’s research is broadly in the area of modeling and analysis of urban transport systems, with an emphasis on travel time reliability analysis, traffic flow theory, large-scale dynamic network modeling and traffic simulation, and decision support systems for real-time traffic management and operations. Her current research focuses on the application of probabilistic modeling and machine learning methods to study relationships among events, traffic, and control actions in urban networks to help address complex traffic control and management issues in the dynamic ITS environment.

Research Interests

  • Intelligent transportation systems (ITS)
  • Data mining and artificial intelligence applications in transportation
  • Travel time reliability, network uncertainty analysis
  • Transport network modeling and traffic simulation
  • Traffic flow theory
  • Decision support systems, expert systems
  • Urban traffic management and operations

Qualifications

  • Doctor of Philosophy, Northwestern University

Publications

View all Publications

Supervision

  • Doctor Philosophy

  • Doctor Philosophy

  • Doctor Philosophy

View all Supervision

Available Projects

  • - Big data analytics for intelligent transport

    - Data mining and artificial intelligence (AI) applications in traffic analysis

    - Spatio-temporal analysis of trajectory data in road networks

    - Data-driven approaches to traffic estimation and prediction

    - Decision support systems for real-time traffic management and control

    - Congestion management and avoidance

    - Incident detection and traffic incident management

  • - Characterizing travel time variability patterns (e.g., vehicle-to-vehicle, day-to-day, and within-day variability)

    - Analysis of network uncertainty sources and their impacts on travel time reliability

    - Estimating the probability distribution of route travel times using multi-source data

    - Development and use of travel time reliability performance indicators

    - Analysis of travel time reliability in mixed traffic

  • - Advanced analysis techniques for micro- and meso-scopic traffic simulation models (e.g., model calibration, input parameter sampling, sensitivity analysis, and uncertainty analysis)

    - 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)

View all Available Projects

Publications

Journal Article

Conference Publication

PhD and MPhil Supervision

Current Supervision

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Associate Advisor

    Other advisors:

  • Master Philosophy — Associate Advisor

    Other advisors:

  • Doctor Philosophy — Associate Advisor

    Other advisors:

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • - Big data analytics for intelligent transport

    - Data mining and artificial intelligence (AI) applications in traffic analysis

    - Spatio-temporal analysis of trajectory data in road networks

    - Data-driven approaches to traffic estimation and prediction

    - Decision support systems for real-time traffic management and control

    - Congestion management and avoidance

    - Incident detection and traffic incident management

  • - Characterizing travel time variability patterns (e.g., vehicle-to-vehicle, day-to-day, and within-day variability)

    - Analysis of network uncertainty sources and their impacts on travel time reliability

    - Estimating the probability distribution of route travel times using multi-source data

    - Development and use of travel time reliability performance indicators

    - Analysis of travel time reliability in mixed traffic

  • - Advanced analysis techniques for micro- and meso-scopic traffic simulation models (e.g., model calibration, input parameter sampling, sensitivity analysis, and uncertainty analysis)

    - 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)

  • - Use of video data (e.g., CCTV) for road traffic management and control

    - 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.