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        Developing a Solution to Improve Road Safety Using Multiple Deep Learning Techniques

        Humberto Villalta,Min gi Lee,Yoon Hee Jo,Kwang Sik Kim 한국인터넷방송통신학회 2023 International Journal of Internet, Broadcasting an Vol.15 No.1

        The number of traffic accidents caused by wet or icy road surface conditions is on the rise every year. Car crashes in such bad road conditions can increase fatalities and serious injuries. Historical data (from the year 2016 to the year 2020) on weather-related traffic accidents show that the fatality rates are fairly high in Korea. This requires accurate prediction and identification of hazardous road conditions. In this study, a forecasting model is developed to predict the chances of traffic accidents that can occur on roads affected by weather and road surface conditions. Multiple deep learning algorithms taking into account AlexNet and 2D-CNN are employed. Data on orthophoto images, automatic weather systems, automated synoptic observing systems, and road surfaces are used for training and testing purposes. The orthophotos images are pre-processed before using them as input data for the modeling process. The procedure involves image segmentation techniques as well as the Z-Curve index. Results indicate that there is an acceptable performance of prediction such as 65% for dry, 46% for moist, and 33% for wet road conditions. The overall accuracy of the model is 53%. The findings of the study may contribute to developing comprehensive measures for enhancing road safety.

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        Design of Public Transportation Route Guidance System for Wheelchair Users Utilizing Public Data of Seoul City

        Geumbi Lee,Humberto Villalta,Seunghyun Kim,Kisu Kim,Jaehyeong Go,Yongjoo Jun,Kwang Sik Kim 한국인터넷방송통신학회 2023 International Journal of Internet, Broadcasting an Vol.15 No.1

        The purpose of this study is to design and test a new way of public transportation route guidance system for persons with disabilities, including wheelchair users. The guidance system is smartphone app-based, using, routes that involve disabled-friendly facilities in the vicinity can be searched. A database that contains publicly available data on low-floor bus services, location and extent of disabled-friendly facilities, and suitable subways and stations, was developed for this purpose. The app uses the database to access and query the required information. A pilot study was conducted to test the effectiveness of the guidance system. It was found that the system was able to convey information about the disabled-friendly routes and related guidance information even inside subway stations, effectively. The performance of the system was compared with route guidance services that do not explicitly use data on disabled-friendly services. A notable difference was observed in the travel time estimated by this program and other guidance services. The difference was around 4 to 15 minutes. This is significant savings for persons with disabilities if they use the app and service. The study thus shows that exclusive use of disabled-friendly data in route guidance will bring more benefits for persons with disabilities.

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