DocumentCode
669034
Title
Vehicle interaction behaviors model based on drivers characteristics at expressway-ramp merging area
Author
Yunlong Tan ; Hongfei Jia
Author_Institution
Coll. of Transp., Jilin Univ., Changchun, China
Volume
3
fYear
2013
fDate
23-24 Nov. 2013
Firstpage
371
Lastpage
374
Abstract
The interaction behaviors between merging vehicles and mainline vehicles at expressway-ramp merging area are very complex, and the driver characteristic is an important factor that affects driver behaviors, however, the existing driver behavior models little consider the influence of driver own characteristic differences on the driver behaviors. In order to represent vehicles merging behaviors accurately, and overcome the shortcomings of existing models, firstly, a typical merging section is selected to investigate field data, the video processing software VEVID is used to get larger amounts of vehicle trajectory data, and a lot of factors that affect driver characteristics and vehicle merging behaviors are analyzed. Then, the driver type model is built by the fuzzy clustering method, and the driver type distribution data is as a key parameter to develop the cooperative probability model and the forced probability model. Finally, the microscopic traffic simulation system MTSS is taken as simulation platform to build merging simulation model, the real merging section traffic data in Guangzhou is used to validate the built model, the output results from the simulation system are compared with the field data, the satisfactory results indicate that the built merging model can be used to describe the complex vehicle interaction behaviors of merging sections.
Keywords
driver information systems; fuzzy set theory; pattern clustering; probability; road vehicles; video signal processing; Guangzhou; MTSS; complex vehicle interaction behaviors; cooperative probability model; driver behaviors; driver characteristics; driver type distribution data; driver type model; expressway-ramp merging area; forced probability model; fuzzy clustering method; mainline vehicles; merging section; merging simulation model; microscopic traffic simulation system; vehicle interaction behaviors model; vehicle trajectory data; vehicles merging behaviors; video processing software VEVID; Data models; Educational institutions; Merging; Microscopy; Traffic control; Vehicles; binary logit model; driver type; fuzzy clustering; merging area;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Management, Innovation Management and Industrial Engineering (ICIII), 2013 6th International Conference on
Conference_Location
Xi´an
Print_ISBN
978-1-4799-3985-5
Type
conf
DOI
10.1109/ICIII.2013.6703595
Filename
6703595
Link To Document