Bayesian mixture model for estimating travel time distribution based on low frequency probe vehicle data표본 프로브 차량 기반 베이즈 혼합 통행시간 분포 추정

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This dissertation aims to estimate travel time based on low and unknown sampling rates of probe vehicles and has two main objectives: i) enhance understanding of travel time properties and ii) develop a novel estimation model of travel time distribution. First, we explored the characteristics of travel time according to traffic state changes and congested traffic. The systematic relation between spot and section measurements is theoretically and empirically examined; as well, the characteristics of travel time variability are empirically observed. Then, we developed a novel estimation model of travel time distribution based on the Bayesian mixture model. To reduce the estimation error due to low and unknown sampling rate of probe vehicles, we used prior information in the Bayesian approach based on the characteristics of travel time regularity. To evaluate the accuracy of the methodology, we used empirical data from expressways and arterial roads. The Bayesian mixture model outperforms different estimation approaches.
Advisors
Jang, Kitaeresearcher장기태researcher
Description
한국과학기술원 :조천식녹색교통대학원,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 조천식녹색교통대학원, 2019.2,[vi, 121 p. :]

Keywords

Bayesian mixture model▼atravel time distribution▼alow frequency probe data▼aprior distribution▼atraffic oscillation; 베이즈 혼합 모형▼a통행시간 분포▼a표본 프로브 차량▼a사전분포▼a교통진동

URI
http://hdl.handle.net/10203/265383
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=842423&flag=dissertation
Appears in Collection
GT-Theses_Ph.D.(박사논문)
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