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Passenger demand forecasting for bike sharing system based on graph neural networks

Passenger demand forecasting for bike sharing system based on graph neural networks

자료유형
학위논문
개인저자
김대한, 金大韓
서명 / 저자사항
Passenger demand forecasting for bike sharing system based on graph neural networks / Dae Han Kim
발행사항
Seoul :   Graduate School, Korea University,   2020  
형태사항
iv, 18장 : 도표 ; 26 cm
기타형태 저록
Passenger Demand Forecasting for Bike Sharing System based on Graph Neural Networks   (DCOLL211009)000000127342  
학위논문주기
학위논문(석사)-- 고려대학교 대학원: 컴퓨터·전파통신공학과, 2020. 2
학과코드
0510   6D36   1110  
일반주기
지도교수: 강재우  
서지주기
참고문헌: 장 17-18
이용가능한 다른형태자료
PDF 파일로도 이용가능;   Requires PDF file reader(application/pdf)  
비통제주제어
Graph Neural Networks , Time Series Demand prediction,,
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008 200103s2020 ulkd bmAC 000c eng
040 ▼a 211009 ▼c 211009 ▼d 211009
085 0 ▼a 0510 ▼2 KDCP
090 ▼a 0510 ▼b 6D36 ▼c 1110
100 1 ▼a 김대한, ▼g 金大韓
245 1 0 ▼a Passenger demand forecasting for bike sharing system based on graph neural networks / ▼d Dae Han Kim
260 ▼a Seoul : ▼b Graduate School, Korea University, ▼c 2020
300 ▼a iv, 18장 : ▼b 도표 ; ▼c 26 cm
500 ▼a 지도교수: 강재우
502 0 ▼a 학위논문(석사)-- ▼b 고려대학교 대학원: ▼c 컴퓨터·전파통신공학과, ▼d 2020. 2
504 ▼a 참고문헌: 장 17-18
530 ▼a PDF 파일로도 이용가능; ▼c Requires PDF file reader(application/pdf)
653 ▼a Graph Neural Networks ▼a Time Series Demand prediction
776 0 ▼t Passenger Demand Forecasting for Bike Sharing System based on Graph Neural Networks ▼w (DCOLL211009)000000127342
900 1 0 ▼a Kim, Dae Han, ▼e
900 1 0 ▼a 강재우, ▼g 姜在雨, ▼e 지도교수
945 ▼a KLPA

전자정보

No. 원문명 서비스
1
Passenger demand forecasting for bike sharing system based on graph neural networks (53회 열람)
PDF 초록 목차

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/학위논문서고/ 청구기호 0510 6D36 1110 등록번호 123063735 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 2 소장처 과학도서관/학위논문서고/ 청구기호 0510 6D36 1110 등록번호 123063736 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

초록

Bike Sharing System(BSS) is now becoming a prominent solution for traffic congestion and air pollution in many metropolitan areas. In BSS, passengers can rent a bike at one station and return it at another. One issue of BSS is station congestion. Stations with excessive rental rates are difficult to borrow a bike while stations with high return rates have no vacant spots. One solution for this imbalance between supply and demand is bike repositioning where bikes are transferred from congested stations to starving stations. Accurate estimate of demands for each bike station is necessary for a robust reposition- ing algorithm. However, current studies regarding demand prediction don’t account for useful features such as periodicity, limiting their modeling capacity. In this paper, we propose a method based on graph convolutional networks that enjoys strong predictive power of newly introduced periodicity features. On ‘Ddareungii’ benchmark dataset, pro- posed approach achieves a state-of-the-art result with 25% lower test mean squared error compared to previous top performer. We further inspect effects of each feature by abla- tion study and our analysis shows that weather and stations’ location do not necessarily impact predictions in an autoregressive setting, contradicting with common intuitions.

목차

1 Introduction 1
2 Backgrounds 3
 2.1 Graph Convolutional Network. 3
 2.2 Station-Level Demand Prediction. 4
 2.3 Grid-Level Demand Prediction  4
 2.4 Task Formulation 5
3 Methods 7
 3.1 Dataset Description  7
 3.2 Periodicity,Weather and Location  8
 3.3 Network Architecture  9
 3.4 Training and Evaluation 11
4 Analyses 13
 4.1 Ablation Study. 13
 4.2 Effects of Weather and Location in Non-autoregressive Setting 15
5 Conclusion 16
Bibliography 17