Please use this identifier to cite or link to this item: http://hdl.handle.net/10266/6644
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dc.contributor.supervisorChowdhury, Ipshita-
dc.contributor.authorKaur, Kismatjeet-
dc.date.accessioned2023-10-25T07:48:33Z-
dc.date.available2023-10-25T07:48:33Z-
dc.date.issued2022-10-25-
dc.identifier.urihttp://hdl.handle.net/10266/6644-
dc.description.abstractA significant portion of the accidental deaths are caused by collisions at intersections between various types of road users. This study used an on-the-road study paradigm to analyse how drivers, riders of two-wheelers, and pedestrians behaved at intersections using ergonomics approaches and the Social Network Analysis framework. Data collected in the form of verbal protocols from an on-road study of cars, two-wheelers, and pedestrians was used to analyse behaviour at three signalised intersections using social networks. A total number of 36 participants (N = 39) were used in the study. The candidates were chosen randomly and conveniently from ages ranging from 25 to 45. The data collected included 13 car drivers (n = 13), 12 participants who drove a powered two-wheeler (n = 13) and 13 pedestrians (n=13). The analysis identifies occasions when these disparities in behaviour may be causing disputes amongst various road users and demonstrates how they differ among the various road user categories. It is discussed how intersection design contributes to these behavioural variations and the ensuing conflicts. It is concluded that intersections are currently not constructed in a way that encourages behaviour among the three types of road users examined. There is discussion about improvements that can be done to increase intersection safety and make the intersections easier to navigate.en_US
dc.language.isoenen_US
dc.subjectintersectionen_US
dc.subjectSocial networksen_US
dc.subjectDriversen_US
dc.subjectTwo-wheelersen_US
dc.subjectPedestriansen_US
dc.titleHow do Road Users Negotiate Signalised Intersections at Right Turns? An Analysis Using Social Networksen_US
dc.typeThesisen_US
Appears in Collections:Masters Theses@TSLAS

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