TOMMs와 TUPA의 합작으로 특허 출원!!

TUPA 3학년 정재은 “GPS TRAJECTORY DATA기반 통행비용함수 보정방안” 주제로 2020 교통학회 논문 발표하여 우수 논문상 수상

Active Transportation describes all human-powered forms of travel. Walking and cycling are among the most popular and can be combined with other modes, such as public transit. It is a key way for more people to be consistently active in their daily lives, improving the quality of life. Active transportation is one of the most cost-effective ways for an individual to become more physically active and remain healthy in the long-term.

Investments in active transportation infrastructure yield positive outcomes, for example, efficient transportation operation, improved air quality, reduced contributions to climate change, improved vibrancy and livability, vehicle operational cost saving, reduced congestion, and more.

Reasons that people give for not walking or cycling usually involve poor weather, safety concerns, a lack of sidewalks and cycling facilities, time pressures, and a lack of secure bicycle parking. Local governments have a crucial role and expertise with design and land use strategies to overcome these challenges.

In many recent years, various data sources detecting the movement of active transportation users and their activities have been implemented to understand travel behaviour. In a big data era, it became much feasible to estimate and predict travel behaviour to make a better transport plan. This special issue tries to compile high-quality research papers to contribute to the active transport area.

The objective of this Special Issue is to bring together state-of-the-art research contributions that address challenges in contemporary data pre and post processing, data management, data fusion, data driven AI applications, extensibility of data application in the active transport field. 

The special issue encourages the authors to contribute submissions from a broad range of research fields related to the recent active transport data to many practitioners and academics as followings

• Transportation planning and design
• Community land use and design
• Safety concerns for walking and biking
• First and last mile accessibility
• Active transport lifelong experience
• Active transport with health implications
• Shared mobility(dockless bike, stationary bikes, electric scooter) implementation
• Special treatments for active transport
• A relationship between public transport and active transportation
• Pedestrian travel behavior and walking pattern
• Elderly mobility

New TUPA has just started with 6 members today in KNU. Our team is small and not yet strong as we just started but will be getting stronger in research and team sprite. Thank you for trusting me in paving the ways we go together. Let’s bring it on and make something big happen together guys!!!

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