논문 (학술지)
Road Speed Prediction Scheme by Analyzing Road Environment Data
등록번호 | - | SCI 구분
?
※구분 : SCI(SCIE포함), 비SCI |
SCI |
---|---|---|---|
저자명 (주·공동저자) | 임종태; 복경수; 박송희; 최도진; 유재수 ※ 과제 참여정보와 일치하는 연구자 상세정보로 정확하지 않을 수 있습니다. 동일저자 논문보기 |
||
논문구분 | 국외전문학술지 | 학술지명 | SENSORS |
ISSN | 1424-8220 | 학술지 출판일자 | 2022-04-12 |
학술지 볼륨번호 | 22(7) | 논문페이지 | 2606 ~ 2630 |
학술지 임팩트팩터 | 3.576 | 기여율 | 25 % |
DOI | 10.3390/s22072606 | ||
초록 | Road speed is an important indicator of traffic congestion. Therefore, the occurrence of traffic congestion can be reduced by predicting road speed because predicted road speed can be provided to users to distribute traffic. Traffic congestion prediction techniques can provide alternative routes to users in advance to help them avoid traffic jams. In this paper, we propose a machine-learning-based road speed prediction scheme using road environment data analysis. The proposed scheme uses not only the speed data of the target road, but also the speed data of neighboring roads that can affect the speed of the target road. Furthermore, the proposed scheme can accurately predict both the average road speed and rapidly changing road speeds. The proposed scheme uses historical average speed data from the target road organized by the day of the week and hour to reflect the average traffic flow on the road. Additionally, the proposed scheme analyzes speed changes in sections where the road speed changes rapidly to reflect traffic flows. Road speeds may change rapidly as a result of unexpected events such as accidents, disasters, and construction work. The proposed scheme predicts final road speeds by applying historical road speeds and events as weights for road speed prediction. It also considers weather conditions. The proposed scheme uses long short-term memory (LSTM), which is suitable for sequential data learning, as a machine learning algorithm for speed prediction. The proposed scheme can predict road speeds in 30 min by using weather data and speed data from the target and neighboring roads as input data. We demonstrate the capabilities of the proposed scheme through various performance evaluations. |
연구개발성과 등록 또는 활용에 대한 문의는 논문 연구개발성과 담당자를 통해 문의하시기 바랍니다.
[문의] 한국과학기술정보연구원 Tel : 042)716-7066, https://curation.kisti.re.kr/
- NTIS 관련 이용문의는 NTIS 콜센터(042-869-1115)로 문의하시기 바랍니다.
NTIS의 논문 정보는 국가연구개발사업 수행을 통해 발생된 성과로, 조사분석 등을 통해 입력된 정보를 수집 및 제공하고 있어, 출판사 또는 논문 정보 제공 사이트(Scienceon, RISS 등)에서 일괄 제공하는 논문 정보와 차이가 있을 수 있습니다.