• Proceedings of the Korean Society of Computer Information Conference
• /
• 2017.01a
• /
• pp.87-88
• /
• 2017

오늘날 도시의 규모가 커지고 도시의 기능이 점점 복잡해진다. 또한, 도시에는 사람들이 많이 살고 있으며, 계속해서 도시로 사람들이 모이게 된다. 그러므로 도시에서의 삶은 서로 간에 점점 더 가까워지고 많은 부분에서 이웃 사람들과 연결되고 공간과 시간을 공유하게 된다. 특히, 사람들이 대중교통을 이용하면서 원하든 원치 않았든 많은 소리에 노출 되며, 그 소리에 대한 영향으로 서로 피해를 보기도 한다. 서울은 세계에서 가장 혼잡한 도시 중 하나이며, 이런 서울의 대중교통 중 마을버스는 좁은 골목길을 포함해 도로를 다니며 시민들의 공공의 이동을 담담하고 있다. 이 마을버스를 사용하는 사람들은 일반적으로 차량 내에서 좋은 승차감, 높은 공기질 및 적은 소음에서 이용하기를 원한다. 본 논문에서는 마을버스의 실내 소음에 대한 성가심도를 소음도 및 혼잡도에 관하여 분석한다. 그리고 이러한 상황별로 어떠한 경우에 성가심도가 높은지를 판단하고, 저감하는 방법을 마련하도록 하고자 한다. 분석결과 마을버스 내부 소음은 새 차와 오래된 차에서 큰 차이를 보이지 않았고, 성가심도 또한 도로 상황에 따른 소음의 정도에 민감한 반응을 보임을 확인하였다. 그래서 저감대안으로는 차량의 소음이 적게 발생 시키는 운전과, 차량의 정비 등을 제안하고자 한다.

• Journal of Korean Society of Transportation
• /
• v.29 no.1
• /
• pp.7-15
• /
• 2011

Recently, airport competitiveness measure is not only passenger and cargo throughput but also value-added activities of their hinterland and airport city. That is, airport competitiveness comes from airport versus airport to airport with their own-supplied city and hinterland connected with airport to provide diversified functions. This study surveyed and analyzed how to form a cluster focused on Incheon International Airport and what are important factors to form of cluster in achieving competences. These clusters need government's political support. In this case, there was a shortage of specialized human resources in competent local suppliers, and limited informations sharing.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2016.04a
• /
• pp.762-765
• /
• 2016

사용자는 스마트폰의 대중화로 인해 다양하고 편리한 서비스를 쉽게 제공 받을 수 있다. 위치 정보 서비스를 사용하기 위해 GPS 모듈을 이용하는데 이는 전력 소모가 매우 크다. 다수의 GPS 모듈이 있는 그룹 상황에서 그룹의 헤더를 정하고 헤더의 위치 정보 데이터를 공유하는 방법을 이용하여 전력 소모 문제를 해결한다. 이를 위해 클라우드 기반 GPS 데이터 Sharing 시스템을 제안한다. 사전에 사회 관계 그룹을 등록하고 그룹원들의 위치 정보 데이터를 수신하고 거리/방위각/속도를 기준으로 그룹 상황을 감지한다. 그룹 상황 감지를 위해 Depth First Search(DFS) 알고리즘을 사용한다. 생성된 그룹에서 배터리 잔여량이 제일 많은 그룹원을 헤더로 정한다. 헤더의 배터리 잔여량에 따라 위치 정보 데이터 수집 횟수를 적응적으로 적용한다. 시스템을 적용한다면 그룹 상황에서의 그룹원의 전력 감소 효과와 더불어 대중 교통의 위치 데이터 공공화가 된다면 사용자의 위치 정보 데이터 대신 대중 교통의 데이터를 대신할 수 있고 사회 관계 그룹원들 간의 관계를 수치화 할 수 있을 것이다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.05a
• /
• pp.724-727
• /
• 2014

This paper is analyzing the situation of the Internet of Things Industry and draw the policy implications to promote Internet of Things industry. Major IT companies as Apple, Google, IBM, Sony, and Samsung have developed various smart glass and smart watch as a Iot products. In order to promote Iot Industry, we should take the build up of eco system between IT makers and the various contents provider, protection of personal information and data, development of killer applications and business models, and the conversion from IPv4 to IPv6 as a next internet address infra, build up of international standard platform on IoT.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.17 no.1
• /
• pp.55-70
• /
• 2018

Recently, as shared transportation services has expanded, integrated mobility services that link personal transportation and public transportation are paid attention. To do this, it is necessary to analyze trip mode chaining behavior. This study analyzed the characteristics of the trip mode chaining behavior using the 2010 travel diary survey in Seoul, and analyzed factors to affect mode choice of trip chaining through the multinomial logit model. The transportation means were classified into passenger cars, city buses, intercity buses, railways, taxis, and others, and 25 trip mode chaining types were identified. Among them, the trip share connected between city bus and railways was the highest. It was also found that the trip mode chaining occurred mainly at commuting and in the morning and afternoon peak. According to the model results, the mode choice of trip chaining is significantly influenced by individual attributes (sex and age), household attributes (car ownership and income), trip attributes (trip purpose, trip time and trip length), and arrival area attributes (number of subway lines and bus lines, ratio of commercial area, land use mix and central region).

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.1
• /
• pp.96-107
• /
• 2015

VANET technologies provide real-time traffic information for mitigating traffic jam and preventing traffic accidents, as well as in-vehicle infotainment service through Telematics/Intelligent Transportation System (ITS). Due to the rapid increasement of various requirements, the vehicle communication with a limited resource and the fixed frame architecture of the conventional techniques is limited to provide an efficient communication service. Therefore, a new flexible operation depending on the surrounding situation information is required that needs an adaptive design of the network architecture and protocol for efficiently predicting, distributing and sharing the context-aware information. In this paper, Vehicle-to-Infrastructure (V2I) based on communication between vehicle and a Road Side Units (RSU) and Vehicle-to-Vehicle (V2V) based on communication between vehicles are effectively combined in a new MAC architecture and V2I and V2V vehicles collaborate in management. As a result, many vehicles and RSU can use more efficiently the resource and send data rapidly. The simulation results show that the proposed method can achieve high resource utilization in accordance. Also we can find out the optimal transmission relay time and 2nd relay vehicle selection probability value to spread out V2V/V2I collaborative schedule message rapidly.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.6
• /
• pp.861-866
• /
• 2022

Seoul introduced the shared bicycle system, "Seoul Public Bike" in 2015 to help reduce traffic volume and air pollution. Hence, to solve various problems according to the supply and demand of the shared bicycle system, "Seoul Public Bike," several studies are being conducted. Most of the research is a strategic "Bicycle Rearrangement" in regard to the imbalance between supply and demand. Moreover, most of these studies predict demand by grouping features such as weather or season. In previous studies, demand was predicted by time-series-analysis. However, recently, studies that predict demand using deep learning or machine learning are emerging. In this paper, we can show that demand prediction can be made a little better by discovering new features or ordering the importance of various features based on well-known feature-patterns. In this study, by ordering the selection of new features or the importance of the features, a better coefficient of determination can be obtained even if the well-known deep learning or machine learning or time-series-analysis is exploited as it is. Therefore, we could be a better one for demand prediction.

• Journal of Internet Computing and Services
• /
• v.24 no.5
• /
• pp.107-120
• /
• 2023

The public bicycle of Seoul, Ttareungyi, was launched at October 2015 to reduce traffic and carbon emissions in downtown Seoul and now, 2023 Oct, the cumulative number of user is upto 4 million and the number of bike is about 43,000 with about 2700 stations. However, super growth of Ttareungyi has caused the several problems, especially demand/supply mismatch, and thus the Seoul citizen has been complained about out of stock. In this point, this study conducted a real time demand forecasting model to prevent stock out bike at stations. To develop the model, the research team gathered the rental·return transaction data of 20,000 bikes in whole 1600 stations for 2019 year and then analyzed bike usage, user behavior, bike stations, and so on. The forecasting model using machine learning is developed to predict the amount of rental/return on each bike station every hour through daily learning with the recent 90 days data with the weather information. The model is validated with MAE and RMSE of bike stations, and tested as a prototype service on the Seoul Bike Management System(Mobile App) for the relocation team of Seoul City.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.1
• /
• pp.55-69
• /
• 2017

Recently, the IoT (Internet of Things) environment is being developed rapidly through network which is linked to intellectual objects. Through the IoT, it is possible for human to intercommunicate with objects and objects to objects. Also, the IoT provides artificial intelligent service mixed with knowledge of situational awareness. One of the industries based on the IoT is a car industry. Nowadays, a self-driving vehicle which is not only fuel-efficient, smooth for traffic, but also puts top priority on eventual safety for humans became the most important conversation topic. Since several years ago, a research on the recognition of the surrounding environment for self-driving vehicles using sensors, lidar, camera, and radar techniques has been progressed actively. Currently, based on the WAVE (Wireless Access in Vehicular Environment), the research is being boosted by forming networking between vehicles, vehicle and infrastructures. In this paper, a research on the recognition of a traffic signs on highway was processed as a part of the awareness of the surrounding environment for self-driving vehicles. Through the traffic signs which have features of fixed standard and installation location, we provided a learning theory and a corresponding results of experiment about the way that a vehicle is aware of traffic signs and additional informations on it.

• Journal of Korean Society of Transportation
• /
• v.30 no.3
• /
• pp.119-127
• /
• 2012

For traffic safety, it is imperative for motorists to secure their clear view and to maintain a similar speed with others while driving in a lane. Large-sized vehicles at lower speeds, however, are likely to increase the risk of accident when they share a lane with cars. Although to overcome this complication the Korean Road Traffic Act established rules for the safe use of roads, the reality is that the rules are seldom observed strictly. In this light, this study was designed to analyze the severity of truck-involved accidents, thereby providing justification for the need of truck-designated lanes and thus contributing to measuring road safety more precisely. A binomial logistic regression model was applied to analyze the severity of truck-involved accidents. The analysis showed that several variables affect the severity of truck-involved accidents on freeways; i.e., violation against the rule of truck-designated lanes, weather, difference between daytime and nighttime, and parking on road shoulder. Moreover, the strong enforcement will be needed to make motorists observe the rule, because a Wald statistical test showed that the violation against the rule of truck-designated lanes has the largest influence on the severity.