Identifying Riding Profiles Parameters from High Resolution Naturalistic Riding Data

A simple and flexible methodology is proposed in order to define Power-Two Wheelers (PTWs) riding profiles by distinguishing between regular and irregular PTW behaviors, by using high resolution naturalistic riding data. “Irregularities” in riding behavior are consistently expressed as outlying values in the multivariate consideration of a set of riding parameters. The detected irregularities are those that diverge from the centroid of the jointly considered riding variables and define critical riding situations that are further associated to typical riding events. Results indicate that the joint consideration of variables that are directly connected to the mechanical characteristics of the PTW, such as breaking, wheel speed, throttle and steering, are adequate to distinguish the regular from irregular riding behavior. Moreover, a regressor is constructed using neural networks and the influential determinants to the deviation from the rider’s regular actions are evaluated.

• Record URL:
  • http://onlinepubs.trb.org/onlinepubs/conferences/2011/RSS/3/Vlahogianni,E.pdf
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• Supplemental Notes:
  • Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the Transportation Research Board of the National Academy of Sciences. Unless otherwise indicated, all materials in this PDF are copyrighted by the National Academy of Sciences. Copyright © National Academy of Sciences. All rights reserved
• Corporate Authors:

  Transportation Research Board

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• Authors:
  • Vlahogianni, E I
  • Yannis, George
  • Golias, John C
  • Eliou, N
  • Lemonakis, P
• Conference:
  • 3rd International Conference on Road Safety and Simulation
  • Location: Indianapolis Indiana, United States
  • Date: 2011-9-14 to 2011-9-16
• Publication Date: 2011

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 15p
• Monograph Title: 3rd International Conference on Road Safety and Simulation

Subject/Index Terms

• TRT Terms: Automatic data collection systems; Behavior; Countermeasures; Incident detection; Moped drivers; Neural networks; Traffic safety; Two wheeled vehicles
• Subject Areas: Pedestrians and Bicyclists; Safety and Human Factors; I83: Accidents and the Human Factor;

Filing Info

• Accession Number: 01506362
• Record Type: Publication
• Files: TRIS, TRB, ATRI
• Created Date: Feb 3 2014 9:17AM