This year, only one paper had been submitted from TUPA and it was accepted.

The topic is “Explore the Applicability of Shared Streets with Virtual Reality Technology” by Xiaojian(Vannesa) Hu, Taeho Oh, Inhi Kim and Xiaojian Hu(from SEU). 

This manuscript was prepared by my Master student, Vannesa. She proves we can always make it no matter which position you are now. 

TUPA will keep encourage master and even bachelor students to try such a prestigious conference like TRB in the future. 

Well done Vannesa and Taeho!!!

https://www.dropbox.com/s/ysl7ryl5fnvxxxd/TRB2021.pdf?dl=0

I had great time at Monash with my fantastic colleagues for 6 years. I cannot forgot what you have done to me. I have been formed by your great help. I am going to miss you all. Thanks and take care till we meet again.

Inhi Keeps closely working with Monash University as an adjunct senior lecturer until 2023. Kongju National University and Monash University will collaborate in teaching and research continuously. 

Congrats Bo! The mid review has been successfully completed. 

Title 

Short-term traffic state estimation and prediction based on spatiotemporal neural networks

Summary

Spatiotemporal neural networks (NN) models have recently achieved competitive results for short-term traffic prediction and achieved outstanding outcomes. However, two problems still require further study in terms of model performance and inner mechanism understanding: (1). The forecasting model affected by many aspects like model inputs, model structure, external factors, and optimisation function. How to design an appropriate framework for short-term traffic state prediction? (2). The existing related studies mainly use the knowledge and advantage from the neural network field, but how to incorporate transport domain kn owledge with the above framework? Therefore, th e aims of this study are: (1) Presenting an overall framework from data management to model training for traffic network state estimation and prediction, which provides better forecasting results and APIs for other applications. (2) Understanding the relations of components inside the framework and improving it by integrating transport domain knowledge. The outcomes of this study provide a general framework of the NN-based traffic forecasting model for practice and a better understanding, which will benefit the short-term traffic forecasting related research and industry.

https://www.youtube.com/watch?v=ptKB_hsGMEY&feature=youtu.be

Research Group

Transport

Research Theme

Monitoring, Prediction and Protection        

Summary

Traffic incident is one of the factors causing traffic congestion on the roads. The traffic management centres start accumulating incident data to manage the congestion. However, the incidents are not properly managed because many minor accidents happen to be unreported. Therefore, systematic ways for detecting and accumulating events data are necessary. This research aim is to introduce a novel way of obtaining incident-related data and to develop a robust detection algorithm to recognize incidents and its type simultaneously by machine learning.

Bio

I received the M.S degree in transportation engineer ing from Kongju National University, South Korea in 2017. I commenced Joint Ph.D. degree with Southeast University, China in 2018 under the supervision of Dr. Inhi Kim, Prof. Graham Currie and Prof. Zhibin Li. Research interests include intelligent transport systems, traffic simulation, virtual reality, driving simulator and deep learning analysis.

The presentation can be watched below;

https://youtu.be/I2CP-E2ZQvE

Research Group

Transport

Research Theme

Resilience, Infrastructure and Society

Summary

Although many strategies and policies have been advocated, either from the demand or supply aspect, to mitigate the traffic congestion, still, the problem remains to exist. A new perspective to visit the causes and to tackle this problem is urged. Extensive studies have revealed that the built environment can highly be associated with determining human spatial activities, especially automobile travel behaviors. Through travel behaviors, lead to the generation of traffic, which has an impact on traffic operation and control action. However, how the built environment directly influences the traffic performance (congestions and delays) have been rarely studied and lack of evidence.

In this context, this research aims to establish an in-depth understanding of the impacts of the built environment on traffic congestion at different spatial scales. Trying to answer the questions as 1) What built environment features are most relevant to the change of traffic congestion? 2) To what extent the change of the built environment features (by considering geographical scale) causes a difference in the traffic congestion level. 3) How can urban mobility and accessibility be optimized and enhanced via manipulating the design of the built environment? To address the above issues, the objectives of this research are 1) To synthesize the critical built environment indicators that hold accountable for the differences in traffic performance between areas; 2) To quantify the relationship of the built environment in raising the traffic congestion at different spatial scales; 3) To inform the built environment policy framework towards optimized transport planning and management .

By knowing this relationship, the outcome of this research can help guide the design of the urban/transportation planning policy for urban sprawl controls and mobility enhancement from the beginning design of the land use and the infrastructure implementation plaN.

For his presentation the video is below

https://youtu.be/BtofemdVzi0

Big congrats. Lilian has successfully passed the progress review with outstanding marks.

Please join to watch her seminar.

https://youtu.be/7y4TBdh8_6g

I am pleased that TUPA has made good outcomes at the 99th TRB conference.

• Inferring The Optimal Number Of Dockless Shared Bike In A New Area By Applying The Gradient Boosting Decision Tree Model by Dong Xiao, Tianqi Gu, Yuanqiu Bao, and Inhi Kim
• The  Use of Emerging  Virtual Reality Technology  in Road Safety Analysis: The Hook-Turn Case by Taeho Oh, Yanping Xu, Zhibin Li, and Inhi Kim
• Short-Term Traffic Prediction Using A  Spatial-Temporal  CNN Model With Transfer Learning by Wang Bo, Hai, Vu, and Inhi Kim