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ANALYSIS OF PEDESTRIAN-VEHICLE CONFLICTS AT SIGNALIZED INTERSECTIONS

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dc.contributor.author Sreereshmi, P M
dc.contributor.author Meenu, Tomson
dc.date.accessioned 2022-11-29T06:04:57Z
dc.date.available 2022-11-29T06:04:57Z
dc.date.issued 2022-07-07
dc.identifier.uri http://210.212.227.212:8080/xmlui/handle/123456789/284
dc.description.abstract Pedestrian safety is a growing concern, especially in developing nations with heterogeneous traffic conditions. One of the common safety issues at signalized intersections is pedestrian-vehicle conflicts, as they can occasionally result in serious collisions. The investigation was carried out at four signalized intersections in Kollam, Kerala. The study focused on conflicts caused by pedestrian signal violations and permissive left-turn movements. The study used surrogate safety measures such as post encroachment time (PET), time to vehicle (TTV), time to accident (TTA), and deceleration to safety time (DST) to analyze conflicts between pedestrians and automobiles fastly and proactively. The conflicts were divided into highly severe, severe and normal conflicts using k-means clustering. A support vector machine (SVM) model was developed to predict the severity level of conflicts. The study revealed that pedestrians' signal-violating behaviour caused 57% of the highly severe conflicts at signalized intersections. The study proposed threshold values of the conflict indicators for each severity level. The developed severity prediction model achieved 97% accuracy in predicting the conflicts' severity level. The study's findings aid in evaluating pedestrians' safety at signalized intersections en_US
dc.language.iso en en_US
dc.relation.ispartofseries ;TKM20CETE15
dc.subject Pedestrian-vehicle conflicts en_US
dc.subject violation behaviour en_US
dc.subject permissive left-turn traffic en_US
dc.subject Surrogate safety measures en_US
dc.subject Severity prediction model en_US
dc.title ANALYSIS OF PEDESTRIAN-VEHICLE CONFLICTS AT SIGNALIZED INTERSECTIONS en_US
dc.type Technical Report en_US


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