NUMBER OF VEHICLES AND TRAVEL TIME ESTIMATION ON URBAN TRAFFIC NETWORK USING BAYESIAN NETWORK MODEL AND PARTICLE FILTERING METHOD

<p align="justify">Travel time is one of the key variables that reflect the performance of a traffic system. The travel time is affected by the interaction between traffic demand (the number of incoming vehicles) and the characteristics of traffic supply (such as road capacity, traff...

Full description

Saved in:

Bibliographic Details
Main Author:	SAMUDRA KUSWANA NIM: 23815001, GILANG
Format:	Theses
Language:	Indonesia
Online Access:	https://digilib.itb.ac.id/gdl/view/27528
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Institut Teknologi Bandung
Language:	Indonesia

id	id-itb.:27528
spelling	id-itb.:275282018-10-01T15:02:10ZNUMBER OF VEHICLES AND TRAVEL TIME ESTIMATION ON URBAN TRAFFIC NETWORK USING BAYESIAN NETWORK MODEL AND PARTICLE FILTERING METHOD SAMUDRA KUSWANA NIM: 23815001, GILANG Indonesia Theses INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/27528 <p align="justify">Travel time is one of the key variables that reflect the performance of a traffic system. The travel time is affected by the interaction between traffic demand (the number of incoming vehicles) and the characteristics of traffic supply (such as road capacity, traffic signaling, and driving speed). Therefore, any traffic conditions will result in different travel times. The traffic conditions are determined by the complex interactions of drivers, vehicles, and road or site characteristics. <br /> <br /> <br /> The dynamics of traffic are modeled by taking a hydrodynamic theory approach, using standard assumptions commonly used in traffic engineering. Stochastic models of traffic evolution are derived and parameterized by turning rasio and the number of outgoing vehicles of each link. In addition, the travel time variability is modeled by statistical approach. The delay time experienced by a vehicle and its free flow speed are the two main sources of uncertainty that can be captured from the statistical model. <br /> <br /> <br /> The relationship between traffic dynamics and travel time is represented by the Dynamic Bayesian Network model. Using floating data (the position of the vehicle at each time interval) obtained from the Vissim simulator and probabilistic modeling framework, this study focuses on the method of estimating travel time and traffic state. The particle filter method is used to estimate the traffic state of each link on the network, which is the hidden variable of the Bayesian model in this study. The traffic process built in the Vissim simulator has been validated with the observed data collected directly on the real location of this study.<p align="justify"> <br /> text
institution	Institut Teknologi Bandung
building	Institut Teknologi Bandung Library
continent	Asia
country	Indonesia Indonesia
content_provider	Institut Teknologi Bandung
collection	Digital ITB
language	Indonesia
description	<p align="justify">Travel time is one of the key variables that reflect the performance of a traffic system. The travel time is affected by the interaction between traffic demand (the number of incoming vehicles) and the characteristics of traffic supply (such as road capacity, traffic signaling, and driving speed). Therefore, any traffic conditions will result in different travel times. The traffic conditions are determined by the complex interactions of drivers, vehicles, and road or site characteristics. <br /> <br /> <br /> The dynamics of traffic are modeled by taking a hydrodynamic theory approach, using standard assumptions commonly used in traffic engineering. Stochastic models of traffic evolution are derived and parameterized by turning rasio and the number of outgoing vehicles of each link. In addition, the travel time variability is modeled by statistical approach. The delay time experienced by a vehicle and its free flow speed are the two main sources of uncertainty that can be captured from the statistical model. <br /> <br /> <br /> The relationship between traffic dynamics and travel time is represented by the Dynamic Bayesian Network model. Using floating data (the position of the vehicle at each time interval) obtained from the Vissim simulator and probabilistic modeling framework, this study focuses on the method of estimating travel time and traffic state. The particle filter method is used to estimate the traffic state of each link on the network, which is the hidden variable of the Bayesian model in this study. The traffic process built in the Vissim simulator has been validated with the observed data collected directly on the real location of this study.<p align="justify"> <br />
format	Theses
author	SAMUDRA KUSWANA NIM: 23815001, GILANG
spellingShingle	SAMUDRA KUSWANA NIM: 23815001, GILANG NUMBER OF VEHICLES AND TRAVEL TIME ESTIMATION ON URBAN TRAFFIC NETWORK USING BAYESIAN NETWORK MODEL AND PARTICLE FILTERING METHOD
author_facet	SAMUDRA KUSWANA NIM: 23815001, GILANG
author_sort	SAMUDRA KUSWANA NIM: 23815001, GILANG
title	NUMBER OF VEHICLES AND TRAVEL TIME ESTIMATION ON URBAN TRAFFIC NETWORK USING BAYESIAN NETWORK MODEL AND PARTICLE FILTERING METHOD
title_short	NUMBER OF VEHICLES AND TRAVEL TIME ESTIMATION ON URBAN TRAFFIC NETWORK USING BAYESIAN NETWORK MODEL AND PARTICLE FILTERING METHOD
title_full	NUMBER OF VEHICLES AND TRAVEL TIME ESTIMATION ON URBAN TRAFFIC NETWORK USING BAYESIAN NETWORK MODEL AND PARTICLE FILTERING METHOD
title_fullStr	NUMBER OF VEHICLES AND TRAVEL TIME ESTIMATION ON URBAN TRAFFIC NETWORK USING BAYESIAN NETWORK MODEL AND PARTICLE FILTERING METHOD
title_full_unstemmed	NUMBER OF VEHICLES AND TRAVEL TIME ESTIMATION ON URBAN TRAFFIC NETWORK USING BAYESIAN NETWORK MODEL AND PARTICLE FILTERING METHOD
title_sort	number of vehicles and travel time estimation on urban traffic network using bayesian network model and particle filtering method
url	https://digilib.itb.ac.id/gdl/view/27528
_version_	1822922280643067904

NUMBER OF VEHICLES AND TRAVEL TIME ESTIMATION ON URBAN TRAFFIC NETWORK USING BAYESIAN NETWORK MODEL AND PARTICLE FILTERING METHOD

Similar Items