Kun Gao

Assistant Professor at Transportgruppen

Kun Gao is an Assistant Professor in the Division of Geology and Geotechnics, Urban Mobility Systems research group. His research interests contain smart transport systems with focuses on electrification, shared mobility, and connected automation, machine learning and big data for transportation optimization, Transport infrastructure planning and travel behavior analysis. Kun Gao has been working on establishing new methods for promoting the development of a safer and more sustainable transportation system. His research in above areas has been supported by Chalmers AoA Transport/Energy, Swedish Energy Agency, CHAIR, and Swedish Innovation Agency Vinnova.

Source: chalmers.se
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Showing 22 publications

2022

Inertia effects of past behavior in commuting modal shift behavior: interactions, variations and implications for demand estimation

Kun Gao, Minhua Shao, Kay W. Axhausen et al
Transportation. Vol. 49 (4), p. 1063 -1097
Journal article
2022

Data and Code Disclosure and Sharing Policy of communications in transportation research

Kun Gao, Hua Wang, Shuaian Wang et al
Communications in Transportation Research. Vol. 2
Other text in scientific journal
2022

Merging control strategies of connected and autonomous vehicles at freeway on-ramps: a comprehensive review

Jie Zhu, Said Easa, Kun Gao
Journal of Intelligent and Connected Vehicles. Vol. 5 (2), p. 99-111
Review article
2021

Examining nonlinear and interaction effects of multiple determinants on airline travel satisfaction

Kun Gao, Ying Yang, Xiaobo Qu
Transportation Research Part D: Transport and Environment. Vol. 97
Journal article
2021

Cumulative prospect theory coupled with multi-attribute decision making for modeling travel behavior

Kun Gao, Lijun Sun, Ying Yang et al
Transportation Research Part A: Policy and Practice. Vol. 148, p. 1-21
Journal article
2021

Optimal Design of Mixed Charging Station for Electric Transit with Joint Consideration of Normal Charging and Fast Charging

Le Zhang, Ziling Zeng, Kun Gao
Smart Innovation, Systems and Technologies. Vol. 231, p. 85-94
Paper in proceeding
2021

A Context-Aware Framework for Risky Driving Behavior Evaluation Based on Trajectory Data

Qingwen Xue, Ying Ying Xing, Kun Gao et al
IEEE Intelligent Transportation Systems Magazine. Vol. In Press
Journal article
2021

Extrapolation-enhanced model for travel decision making: An ensemble machine learning approach considering behavioral theory

Kun Gao, Ying Yang, Tianshu Zhang et al
Knowledge-Based Systems. Vol. 218
Journal article
2021

High-resolution assessment of environmental benefits of dockless bike-sharing systems based on transaction data

Aoyong Li, Kun Gao, Pengxiang Zhao et al
Journal of Cleaner Production. Vol. 296
Journal article
2021

Spatial heterogeneity in distance decay of using bike sharing: An empirical large-scale analysis in Shanghai

Kun Gao, Ying Yang, Aoyong Li et al
Transportation Research Part D: Transport and Environment. Vol. 94
Journal article
2021

Quantifying economic benefits from free-floating bike-sharing systems: A trip-level inference approach and city-scale analysis

Kun Gao, Ying Yang, Aoyong Li et al
Transportation Research Part A: Policy and Practice. Vol. 144, p. 89-103
Journal article
2021

Modeling Measurements Towards Effect of Past Behavior on Travel Behavior

Kun Gao, Tianshu Zhang, Zhihan Li
Smart Innovation, Systems and Technologies. Vol. 231, p. 141-157
Paper in proceeding
2021

Driving Style Recognition Incorporating Risk Surrogate by Support Vector Machine

Qingwen Xue, Jianjohn Lu, Kun Gao
Smart Innovation, Systems and Technologies. Vol. 231, p. 123-131
Paper in proceeding
2021

Diverging effects of subjective prospect values of uncertain time and money

Kun Gao, Ying Yang, Xiaobo Qu
Communications in Transportation Research. Vol. 1
Journal article
2021

Modeling Commercial Vehicle Drivers’ Acceptance of Forward Collision Warning System

Yueru Xu, Zhirui Ye, Chao Wang et al
Smart Innovation, Systems and Technologies. Vol. 231, p. 167-180
Paper in proceeding
2020

Spatio-Temporal Usage Patterns of Dockless Bike-Sharing Service Linking to a Metro Station: A Case Study in Shanghai, China

Qiang Yan, Kun Gao, Lijun Sun et al
Sustainability. Vol. 12 (3)
Journal article
2020

How to Model the Influence of In-vehicle Crowding on Travel Behavior: A Comparison Among Moderation, Independent Variable and Interaction

Kun Gao, Jieyu Fan, Ziling Zeng
Smart Innovation, Systems and Technologies. Vol. 185, p. 41-52
Paper in proceeding
2020

An empirical analysis of dockless bike-sharing utilization and its explanatory factors: Case study from Shanghai, China

Aoyong Li, Pengxiang Zhao, Yizhe Huang et al
Journal of Transport Geography. Vol. 88
Journal article
2020

Influential factors associated with consecutive crash severity: A two-level logistic modeling approach

Fanyu Meng, Pengpeng Xu, Cancan Song et al
International Journal of Environmental Research and Public Health. Vol. 17 (15), p. 1-16
Journal article
2020

Modelling the Relationships Between Headway and Speed in Saturation Flow of Signalised Intersections

Yang Teng, Jin Xu, Kun Gao et al
Smart Innovation, Systems and Technologies. Vol. 185, p. 179-188
Paper in proceeding
2020

Revealing psychological inertia in mode shift behavior and its quantitative influences on commuting trips

Kun Gao, Ying Yang, Lijun Sun et al
Transportation Research Part F: Traffic Psychology and Behaviour. Vol. 71, p. 272-287
Journal article
2020

Will Autonomous Vehicles Improve Traffic Efficiency and Safety in Urban Road Bottlenecks? The Penetration Rate Matters

Tianshu Zhang, Kun Gao
2020 IEEE 5th International Conference on Intelligent Transportation Engineering, ICITE 2020, p. 366-370
Paper in proceeding

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Showing 9 research projects

2022–2023

Analyzing and promoting micro-shared mobility system leveraging big data

Kun Gao GeoEngineering
Majid Astaneh Fluid Dynamics
Jiaming Wu GeoEngineering
Jelena Andric Vehicle Engineering and Autonomous Systems
Xiaobo Qu GeoEngineering
Ruo Jia Transportgruppen
AoA Transport Funds

2021–2023

Battery Ageing Prediction and Optimization in a Fleet of Electric Autonomous Vehicles

Kun Gao Transportgruppen
Xiaobo Qu Transportgruppen
Chalmers

2021–2023

AI-cloud-based Vehicle Management Strategies for Electrified Vehicles

Jiaming Wu GeoEngineering
Kun Gao Transportgruppen
Xiaobo Qu Transportgruppen
Changfu Zou Automatic Control
Chalmers AI Research Centre (CHAIR)

2020–2023

Speed Control of Intelligent Connected Vehicles in Open and Mixed Road Environment for Safer Traffic Operating

Jiaming Wu Automatic Control
Xiaobo Qu GeoEngineering
Kun Gao GeoEngineering
Jie Zhu GeoEngineering
Ziling Zeng GeoEngineering
VINNOVA

2020–2023

Simulation-based and field tests for evaluating multi-dimensional performances of intelligent connected vehicles

Kun Gao GeoEngineering
Jie Zhu GeoEngineering
Xiaobo Qu GeoEngineering
Ziling Zeng GeoEngineering
Jiaming Wu Automatic Control
Ruo Jia Transportgruppen
VINNOVA

2020–2021

Online Lithium-ion Battery State of Health Prognostics (LiBSoHP)

Kun Gao GeoEngineering
Le Zhang GeoEngineering
Xiaobo Qu GeoEngineering
Yongzhi Zhang GeoEngineering
Chalmers AI Research Centre (CHAIR)

2020–2023

ICV-Safe: Testing safety of intelligent connected vehicles in open and mixed road environment

Xiaobo Qu GeoEngineering
Jiaming Wu GeoEngineering
Kun Gao GeoEngineering
Yang Liu GeoEngineering
VINNOVA

2019–2022

System-Of-Systems for efficient Emergency Response and smart mobility (SoSER)

Xiaobo Qu GeoEngineering
Kun Gao GeoEngineering
Ziling Zeng GeoEngineering
Jie Zhu GeoEngineering
Jiaming Wu GeoEngineering
Ivana Tasic GeoEngineering
Changfu Zou Automatic Control
Christos Dimitrakakis Data Science
VINNOVA

2019–2022

Holistic and integrated emergency management technology & equipment in the event of traffic accidents

Xiaobo Qu GeoEngineering
Ziling Zeng GeoEngineering
Jie Zhu GeoEngineering
Ivana Tasic GeoEngineering
Kun Gao GeoEngineering
Changfu Zou Automatic Control
Jiaming Wu GeoEngineering
Chinese Ministry of Science and Technology
VINNOVA

There might be more projects where Kun Gao participates, but you have to be logged in as a Chalmers employee to see them.