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postgraduate thesis: Quantitative assessment methods for taxi safety in Hong Kong

TitleQuantitative assessment methods for taxi safety in Hong Kong
Authors
Advisors
Advisor(s):Wong, SC
Issue Date2018
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Meng, F. [孟繁宇]. (2018). Quantitative assessment methods for taxi safety in Hong Kong. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.
AbstractAmong the public transport modes in Hong Kong, taxis play an essential role given the flexibility and comfort level of the services they provide and the increasing annual mileage they cover. However, the safety problems of taxis in Hong Kong have become rather severe in recent years, as suggested by the records of crashes involving taxis. This thesis applies advanced quantitative approaches to assess taxi safety in Hong Kong in three aspects: taxi crash frequency, taxi occupant injury severity, and taxi drivers’ driving performance. A linear projection approach was first applied to calculate the travel time exposures of taxis and total traffic in Hong Kong to provide a basis for the taxi crash frequency analysis. A gas dynamic analogous exposure measure was then derived and adopted in the multiple-vehicle crash frequency model, and the conventional taxi time exposure was used for the single-vehicle model. Influential factors for taxi crash frequency were identified via a random parameter modeling framework in which unobserved heterogeneities were addressed. The occupant-level injury severity in taxis was modeled based on a Bayesian hierarchical logistic modeling approach that accounted for space-time interaction. Taxi driver and passenger injuries were analyzed separately. The same models were established for injuries in private cars for the purpose of comparison. Fifty male taxi drivers’ driving performance was measured with a driving simulator at three different times during his working shift, and a brief fatigue inventory questionnaire survey was conducted during each visit. The temporal patterns of each driver’s driving performance and driving fatigue were modeled using a random effects modeling approach. Upon comparison of the patterns with the temporal distributions of taxi working intensity and taxi crash risk in Hong Kong, a “recovery effect” and a “lagging effect” were defined for taxi drivers’ driving fatigue. Suggestions for legislation and management policies regarding taxis in Hong Kong are made based on the results for the three studied aspects that could help manage the existing taxi safety problems and eliminate hidden taxi safety hazards.
DegreeDoctor of Philosophy
SubjectTaxicabs - China - Hong Kong
Traffic safetly - Research
Dept/ProgramCivil Engineering
Persistent Identifierhttp://hdl.handle.net/10722/273770

 

DC FieldValueLanguage
dc.contributor.advisorWong, SC-
dc.contributor.authorMeng, Fanyu-
dc.contributor.author孟繁宇-
dc.date.accessioned2019-08-14T03:29:50Z-
dc.date.available2019-08-14T03:29:50Z-
dc.date.issued2018-
dc.identifier.citationMeng, F. [孟繁宇]. (2018). Quantitative assessment methods for taxi safety in Hong Kong. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.-
dc.identifier.urihttp://hdl.handle.net/10722/273770-
dc.description.abstractAmong the public transport modes in Hong Kong, taxis play an essential role given the flexibility and comfort level of the services they provide and the increasing annual mileage they cover. However, the safety problems of taxis in Hong Kong have become rather severe in recent years, as suggested by the records of crashes involving taxis. This thesis applies advanced quantitative approaches to assess taxi safety in Hong Kong in three aspects: taxi crash frequency, taxi occupant injury severity, and taxi drivers’ driving performance. A linear projection approach was first applied to calculate the travel time exposures of taxis and total traffic in Hong Kong to provide a basis for the taxi crash frequency analysis. A gas dynamic analogous exposure measure was then derived and adopted in the multiple-vehicle crash frequency model, and the conventional taxi time exposure was used for the single-vehicle model. Influential factors for taxi crash frequency were identified via a random parameter modeling framework in which unobserved heterogeneities were addressed. The occupant-level injury severity in taxis was modeled based on a Bayesian hierarchical logistic modeling approach that accounted for space-time interaction. Taxi driver and passenger injuries were analyzed separately. The same models were established for injuries in private cars for the purpose of comparison. Fifty male taxi drivers’ driving performance was measured with a driving simulator at three different times during his working shift, and a brief fatigue inventory questionnaire survey was conducted during each visit. The temporal patterns of each driver’s driving performance and driving fatigue were modeled using a random effects modeling approach. Upon comparison of the patterns with the temporal distributions of taxi working intensity and taxi crash risk in Hong Kong, a “recovery effect” and a “lagging effect” were defined for taxi drivers’ driving fatigue. Suggestions for legislation and management policies regarding taxis in Hong Kong are made based on the results for the three studied aspects that could help manage the existing taxi safety problems and eliminate hidden taxi safety hazards.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subject.lcshTaxicabs - China - Hong Kong-
dc.subject.lcshTraffic safetly - Research-
dc.titleQuantitative assessment methods for taxi safety in Hong Kong-
dc.typePG_Thesis-
dc.description.thesisnameDoctor of Philosophy-
dc.description.thesislevelDoctoral-
dc.description.thesisdisciplineCivil Engineering-
dc.description.naturepublished_or_final_version-
dc.date.hkucongregation2018-
dc.identifier.mmsid991044046695303414-

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