東華大學圖書館 |

Methods for Investigating Bicycle Traffic Flow Parameters and Facility Performance in Urban Environments.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Methods for Investigating Bicycle Traffic Flow Parameters and Facility Performance in Urban Environments./
作者:	Beitel, David.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	103 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
Contained By:	Dissertations Abstracts International83-06B.
標題:	Bicycles. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28731184
ISBN:	9798544224228

Methods for Investigating Bicycle Traffic Flow Parameters and Facility Performance in Urban Environments.
Beitel, David.

Methods for Investigating Bicycle Traffic Flow Parameters and Facility Performance in Urban Environments. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 103 p.

Source: Dissertations Abstracts International, Volume: 83-06, Section: B.

Thesis (Ph.D.)--McGill University (Canada), 2021.

This item must not be sold to any third party vendors.

Today's urban transportation networks accommodate large flows of both non-motorized and motorized traffic. The traffic dynamics between users is increasingly complex, thus the study of flows, performance and safety of facilities is critical. Current transportation networks rely on evidence-based planning and design, and traffic management and control systems. In this context, accurate monitoring of non-motorized transportation networks, in particular cycling, is crucial.The general objective of this thesis is to improve existing methods to automatically detect bicycle count anomalies and extrapolate volumes as well as to investigate the relationship between volumes, speed, and safety outcomes. The particular objectives of this thesis are to: i) develop a novel methodology for anomaly detection and interpolation of continuous bicycle count data, ii) improve existing methods to estimate average annual daily bicyclists with confidence intervals through extrapolation of short-term bicycle counts using reference site data, iii) investigate the relations between basic cycling traffic parameters on protected cycling infrastructure using empirical traffic flow data from bicycle facilities in Montreal, and iv) present a methodological framework for semi-automated video analysis of pedestrian-cyclist interactions at intersections in non-motorized shared space and estimate risk in a case study of the McGill Campus.Bicycle flow data is crucial for transportation agencies to evaluate and improve cycling infrastructure. Average annual daily bicyclists (AADB) is commonly used in research and practice as a metric for cycling studies such as ridership analysis, infrastructure planning and injury risk. AADB is estimated by averaging the daily cyclist totals measured throughout the year using a long-term automated bicycle counter, or by extrapolating data from a short-term counting site. Extrapolation of a short-term bicycle counting site requires an accurate and complete set of daily factors from a group of references: long-term bicycle counter data. In practice, validation of reference data is time-consuming but crucial as large error can be introduced into AADB extrapolations if reference data are not validated. Chapter 2 proposes an automated method to validate long-term bicycle count data and interpolate anomalous portions of data. The methods are tested using a large dataset; data anomalies are simulated by removing data or reducing counts to 25% or 40% of the measured bicycle counts. Anomalies were simulated for the anomaly detection process to identify and approximately 90% of the anomalies were flagged. The average absolute relative error (ARE) of the interpolated daily values was approximately 10%. Extrapolation of a short-term bicycle counting site can produce inaccurate AADB estimates due to several sources of error and the range of error is highly correlated to several characteristics of the short-term count. Chapter 3 proposes a simple method to estimate the quality of a short-term count through a single metric combining five factors associated with the count variation: duration, average demand, timeof-year, stability and correlation with the reference count. The quality measure, with a range from zero to ten, is negatively correlated with the ARE of the AADB estimation. The results show distinct ARE distributions for different quality measure classes. The average ARE for the lowest quality class is 13.5% compared to an average ARE of 3.0% for the highest quality class.

ISBN: 9798544224228Subjects--Topical Terms:

1313523
Bicycles.

Methods for Investigating Bicycle Traffic Flow Parameters and Facility Performance in Urban Environments.
LDR:07778nmm a2200337 4500 001 2346035
005 20220613064842.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798544224228
035 $a (MiAaPQ)AAI28731184
035 $a (MiAaPQ)McGill_ns064b412
035 $a AAI28731184
040 $a MiAaPQ $c MiAaPQ
100 1 $a Beitel, David. $3 3685068
245 1 0 $a Methods for Investigating Bicycle Traffic Flow Parameters and Facility Performance in Urban Environments.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 103 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
500 $a Advisor: Miranda-Moreno, Luis;Manaugh, Kevin.
502 $a Thesis (Ph.D.)--McGill University (Canada), 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Today's urban transportation networks accommodate large flows of both non-motorized and motorized traffic. The traffic dynamics between users is increasingly complex, thus the study of flows, performance and safety of facilities is critical. Current transportation networks rely on evidence-based planning and design, and traffic management and control systems. In this context, accurate monitoring of non-motorized transportation networks, in particular cycling, is crucial.The general objective of this thesis is to improve existing methods to automatically detect bicycle count anomalies and extrapolate volumes as well as to investigate the relationship between volumes, speed, and safety outcomes. The particular objectives of this thesis are to: i) develop a novel methodology for anomaly detection and interpolation of continuous bicycle count data, ii) improve existing methods to estimate average annual daily bicyclists with confidence intervals through extrapolation of short-term bicycle counts using reference site data, iii) investigate the relations between basic cycling traffic parameters on protected cycling infrastructure using empirical traffic flow data from bicycle facilities in Montreal, and iv) present a methodological framework for semi-automated video analysis of pedestrian-cyclist interactions at intersections in non-motorized shared space and estimate risk in a case study of the McGill Campus.Bicycle flow data is crucial for transportation agencies to evaluate and improve cycling infrastructure. Average annual daily bicyclists (AADB) is commonly used in research and practice as a metric for cycling studies such as ridership analysis, infrastructure planning and injury risk. AADB is estimated by averaging the daily cyclist totals measured throughout the year using a long-term automated bicycle counter, or by extrapolating data from a short-term counting site. Extrapolation of a short-term bicycle counting site requires an accurate and complete set of daily factors from a group of references: long-term bicycle counter data. In practice, validation of reference data is time-consuming but crucial as large error can be introduced into AADB extrapolations if reference data are not validated. Chapter 2 proposes an automated method to validate long-term bicycle count data and interpolate anomalous portions of data. The methods are tested using a large dataset; data anomalies are simulated by removing data or reducing counts to 25% or 40% of the measured bicycle counts. Anomalies were simulated for the anomaly detection process to identify and approximately 90% of the anomalies were flagged. The average absolute relative error (ARE) of the interpolated daily values was approximately 10%. Extrapolation of a short-term bicycle counting site can produce inaccurate AADB estimates due to several sources of error and the range of error is highly correlated to several characteristics of the short-term count. Chapter 3 proposes a simple method to estimate the quality of a short-term count through a single metric combining five factors associated with the count variation: duration, average demand, timeof-year, stability and correlation with the reference count. The quality measure, with a range from zero to ten, is negatively correlated with the ARE of the AADB estimation. The results show distinct ARE distributions for different quality measure classes. The average ARE for the lowest quality class is 13.5% compared to an average ARE of 3.0% for the highest quality class.
520 $a Les reseaux de transport urbain d'aujourd'hui accueillent d'importants flux de trafic motorise et non motorise. La dynamique du trafic entre les usagers est de plus en plus complexe, d'ou l'importance de l'etude des flux, des performances et de la securite des installations. Les reseaux de transport actuels reposent sur une planification et une conception fondees sur des donnees probantes, ainsi que sur des systemes de gestion et de controle du trafic. Dans ce contexte, une surveillance precise des reseaux de transport non motorises, en particulier du cyclisme, est cruciale.L'objectif general de cette these est d'ameliorer les methodes existantes pour detecter automatiquement les anomalies de comptage des velos et extrapoler les volumes ainsi que d'etudier la relation entre les volumes, la vitesse et les resultats en matiere de securite. Les objectifs particuliers de cette these sont les suivants : i) developper une nouvelle methodologie pour la detection des anomalies et l'interpolation des donnees de comptage de velos en continu, ii) ameliorer les methodes existantes pour estimer la moyenne annuelle de cyclistes par jour avec des intervalles de confiance par l'extrapolation de comptages de velos a court terme en utilisant des donnees de sites de reference, iii) etudier les relations entre les parametres de base du trafic cycliste sur les infrastructures cyclables protegees en utilisant des donnees empiriques sur le flux de trafic des installations cyclables a Montreal, et iv) presenter un cadre methodologique pour l'analyse video semi-automatisee des interactions pietons-cyclistes aux intersections dans l'espace partage non motorise et estimer le risque dans une etude de cas du campus de McGill.Les donnees sur les flux de velos sont essentielles pour que les organismes de transport puissent evaluer et ameliorer les infrastructures cyclables. La moyenne annuelle des cyclistes par jour (AADB) est couramment utilisee dans la recherche et la pratique comme mesure pour les etudes sur le cyclisme, telles que l'analyse de la frequentation, la planification des infrastructures et le risque de blessure. L'AADB est estimee en calculant la moyenne des totaux de cyclistes quotidiens mesures tout au long de l'annee a l'aide d'un compteur de velos automatise a long terme, ou en extrapolant les donnees d'un site de comptage a court terme. L'extrapolation d'un site de comptage de velos a court terme necessite un ensemble precis et complet de facteurs quotidiens provenant d'un groupe de references : les donnees d'un compteur de velos a long terme. En pratique, la validation des donnees de reference est longue mais cruciale car une erreur importante peut etre introduite dans les extrapolations de la AADB si les donnees de reference ne sont pas validees. Le chapitre 2 propose une methode automatisee pour valider les donnees de comptage de velos a long terme et interpoler les parties anormales des donnees. Les methodes sont testees en utilisant un large ensemble de donnees ; les anomalies sont simulees en supprimant des donnees ou en reduisant les comptages a 25 ou 40 % des comptages de velos mesures.
590 $a School code: 0781.
650 4 $a Bicycles. $3 1313523
650 4 $a Sensors. $3 3549539
650 4 $a Bicycling. $3 3683002
650 4 $a Statistics. $3 517247
650 4 $a Transportation. $3 555912
650 4 $a Recreation. $3 535376
690 $a 0463
690 $a 0709
690 $a 0814
710 2 $a McGill University (Canada). $3 1018122
773 0 $t Dissertations Abstracts International $g 83-06B.
790 $a 0781
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28731184