Driver types and their behaviors within a high level of pedestrian activity environment. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Zheng, Yinan; Chase, Richard Thomas; Elefteriadou, Lily; Sisiopiku, Virginia; Schroeder, Bastian
• Is Part Of: Transportation letters. Volume 9:Issue 1(2017)
• Page Start: 1
• Page End: 11
• Publication Date: 2017-01-01
• Subjects: Driver type -- Driver behavior -- Pedestrian -- Vehicle–pedestrian interaction -- Cluster analysis; Transportation -- Research -- Periodicals; Transportation -- Periodicals; Urban transportation -- Periodicals; 388.072
• DOI: 10.1080/19427867.2015.1131943
• Languages: English
• ISSNs: 1942-7867

Abstract

Abstract : Driver behavior analysis has recently been the focus of research to enhance traffic simulation modeling. Previous research has developed algorithms to model different driver types in both urban networks and freeways, and defined driver behavioral parameters based on pre-categorized driver groups. However, driver behaviors within the more confined, low speed, high pedestrian flow environment (such as CBD of a major city, urban downtown, campus) may be quite different and research concentrating on such locations is limited. The objective of this study is to assess and evaluate the relation between driver type and driver behavior within a relatively restrictive environment with high levels of pedestrian–vehicle interactions and develop suitable schemes to categorize driver types. To meet this objective, the University of Florida campus environment was used as the starting point of analyzing such driver types and behaviors, and a diverse pool of drivers was selected based on age, gender, driving experiences, etc. to participate in surveys and an instrumented vehicle study. The instrumented vehicle was used to collect vehicle trajectory and driver behavior data from the study subjects on the campus at the University of Florida. Two performance measures (namely, driver-desired speed and yield behavior) were applied to categorize driver types. These two categorizations provided consistent results and indicated that either one can be effectively applied in future studies for driver type classifications. Additional comparisons of driver background and personal characteristics obtained from questionnaires further confirmed the driver classification results.