Full metadata record
DC FieldValueLanguage
dc.contributorDepartment of Civil and Environmental Engineeringen_US
dc.contributor.advisorSze, Nang-ngai (CEE)en_US
dc.contributor.advisorChen, Anthony (CEE)en_US
dc.creatorDing, Hongliang-
dc.publisherHong Kong Polytechnic Universityen_US
dc.rightsAll rights reserveden_US
dc.titleUnderstanding and analysis of bicycle travel and safetyen_US
dcterms.abstractCycling has received more and more attention in urban and transport planning in recent years. As an active transport mode, cycling does not only relieve traffic congestion and reduce vehicle emissions, but also improves the well-being of society. Despite the benefits for health and environment, bicyclists are vulnerable to injuries and mortalities in road crashes. It is crucial to identify the influencing factors that affect the bicycle crash risk. Therefore, effective countermeasures can be implemented to improve overall bicycle safety.en_US
dcterms.abstractIn this study, effects of policy interventions on bicycle travel and safety are examined, based on comprehensive traffic and crash data. For example, policy interventions including low emission zone, congestion charging scheme, and public bicycle rental scheme are considered. The propensity score matching method is applied to account for the effects of confounding factors like built environment and population socio-demographics. Results indicate that bicycle travel increases remarkably after the implementation of low emission zone, especially for short and intermediate bicycle trips. However, bicycle crash frequencies also increase after the introduction of congestion charging and public bicycle rental schemes.en_US
dcterms.abstractOn the other hand, association between built environment, population socio-demographics, road network configuration, traffic characteristics, and bicycle crash frequency at zonal level is measured, with which the bicycle crash exposure is accounted. For example, bicycle usage data from the public bicycle rental system is used to estimate the bicycle crash exposure. In addition, a weighted shortest path approach is proposed to estimate the bicycle distance travelled, with which the configuration of cycle lane network and safety perception of bicyclists are considered. Results indicate that bicycle crash frequency model that incorporates bicycle distance travelled as exposure is superior to those using bicycle time travelled and bicycle trip frequency as exposure. Furthermore, factors including land use, bicycle infrastructure, population density, gender, age, median household income, and weather condition are found to affect bicycle crash frequency, after controlling for the effects of unobserved heterogeneity and spatial correlation.en_US
dcterms.abstractLast but not least, advanced statistical and deep learning models are developed to resolve the prevalent problems in safety analysis. For example, a multivariate Poisson-lognormal regression model is developed to account for the correlation between the frequencies of different bicycle crash types. Furthermore, imbalanced crash data and boundary crash problems are resolved using the deep learning approaches including augmented variational autoencoder and crash feature-based allocation methods. Results indicate that crash frequency models developed using the aforementioned approaches have better prediction performances. More importantly, more influencing factors can be identified.en_US
dcterms.abstractTo sum up, findings of this study can enhance the understanding on the roles of environmental, physical, social, and political factors in bicycle travel and safety. This should shed light on the optimal urban planning, engineering design, and transport policy that can promote bicycle travel and improve bicycle safety in the long run.en_US
dcterms.extentxv, 202 pages : color illustrationsen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.educationalLevelAll Doctorateen_US
dcterms.LCSHBicycle commutingen_US
dcterms.LCSHCycling -- Safety measuresen_US
dcterms.LCSHTraffic safetyen_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsopen accessen_US

Files in This Item:
File Description SizeFormat 
6668.pdfFor All Users4.28 MBAdobe PDFView/Open

Copyright Undertaking

As a bona fide Library user, I declare that:

  1. I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
  2. I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
  3. I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.

By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.

Show simple item record

Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/12205