Understanding Bicycle Dynamics and Cyclist Behavior From Naturalistic Field Data (November 2012)

As technology advances, motorized vehicles employ newer, more intelligent systems to improve drivers' safety and mobility. The evolution of these systems is supported by increasingly accurate models for driver behavior and vehicle dynamics. Despite the significant role of nonmotorized vehicles such as bicycles in traffic accidents, the evolution of in-vehicle intelligent systems has no counterpart for bicycles. Part of the reason is that, to date, models for bicyclist behavior are absent and models for bicycle dynamics are limited. This paper presents a platform for collecting field data from bicycles and shows how such data can support the development of intelligent systems by offering novel insights into bicycle dynamics and bicyclist behavior.

[1]  James R. Sayer,et al.  Heavy-Truck Drivers’ Following Behavior With Intervention of an Integrated, In-Vehicle Crash Warning System , 2012, Hum. Factors.

[2]  Kenneth L Campbell The SHRP 2 Naturalistic Driving Study: Addressing Driver Performance and Behavior in Traffic Safety , 2012 .

[3]  Robert J Carroll,et al.  The development of a naturalistic data collection system to perform critical incident analysis: an investigation of safety and fatigue issues in long-haul trucking. , 2006, Accident; analysis and prevention.

[4]  Fan Zhang,et al.  How to make more cycling good for road safety? , 2012, Accident; analysis and prevention.

[5]  Richard Bishop,et al.  Intelligent Vehicle Technology and Trends , 2005 .

[6]  H Summala,et al.  Attention and expectation problems in bicycle-car collisions: an in-depth study. , 1998, Accident; analysis and prevention.

[7]  Angelo Cappello,et al.  Influence of a portable audio-biofeedback device on structural properties of postural sway , 2005, Journal of NeuroEngineering and Rehabilitation.

[8]  Ellen van Nunen,et al.  Cooperative Competition for Future Mobility , 2012, IEEE Transactions on Intelligent Transportation Systems.

[9]  Joshua D. Hoffman,et al.  A Dynamic Programming Algorithm for Scheduling In-Vehicle Messages , 2008, IEEE Transactions on Intelligent Transportation Systems.

[10]  Irene Isaksson-Hellman,et al.  A study of bicycle and passenger car collisions based on insurance claims data. , 2012, Annals of advances in automotive medicine. Association for the Advancement of Automotive Medicine. Annual Scientific Conference.

[11]  Richard J. Hanowski,et al.  Driver Distraction in Commercial Vehicle Operations , 2009 .

[12]  Marco Dozza,et al.  Platform enabling intelligent safety applications for vulnerable road users , 2014 .

[13]  Dot Hs,et al.  Review of Studies on Pedestrian and Bicyclist Safety, 1991-2007 , 2012 .

[14]  Emiliano Miluzzo,et al.  The BikeNet mobile sensing system for cyclist experience mapping , 2007, SenSys '07.

[15]  P. Carlo Cacciabue,et al.  Modelling Driver Behaviour in Automotive Environments: Critical Issues in Driver Interactions with Intelligent Transport Systems , 2007 .

[16]  Jonas Sjöberg,et al.  Model-Based Threat Assessment for Avoiding Arbitrary Vehicle Collisions , 2010, IEEE Transactions on Intelligent Transportation Systems.

[17]  Marco Dozza FOTware: a modular, customizable software for analysis of multiple-source fieldoperational- test data , 2010 .

[18]  K.J. Astrom,et al.  Bicycle dynamics and control: adapted bicycles for education and research , 2005, IEEE Control Systems.

[19]  Thomas A. Dingus,et al.  Evaluating the Relationship Between Near-Crashes and Crashes: Can Near-Crashes Serve as a Surrogate Safety Metric for Crashes? , 2010 .

[20]  Kazuya Takeda,et al.  A Study of Driver Behavior Under Potential Threats in Vehicle Traffic , 2009, IEEE Transactions on Intelligent Transportation Systems.

[21]  Benjamin J Chihak,et al.  Perceiving and acting on complex affordances: how children and adults bicycle across two lanes of opposing traffic. , 2013, Journal of experimental psychology. Human perception and performance.

[22]  Francesco Biral,et al.  Supporting Drivers in Keeping Safe Speed and Safe Distance: The SASPENCE Subproject Within the European Framework Programme 6 Integrating Project PReVENT , 2010, IEEE Transactions on Intelligent Transportation Systems.

[23]  Marco Dozza,et al.  Piloting the Naturalistic Methodology on Bicycles , 2012 .

[24]  Carolina Pinart,et al.  ECall-Compliant Early Crash Notification Service for Portable and Nomadic Devices , 2009, VTC Spring 2009 - IEEE 69th Vehicular Technology Conference.

[25]  James R. Sayer,et al.  Integrated vehicle-based safety systems field operational test final program report. , 2011 .

[26]  Louise Gustafsson,et al.  A naturalistic study of commuter cyclists in the greater Stockholm area. , 2013, Accident; analysis and prevention.

[27]  Richard J. Hanowski,et al.  An Assessment of Commercial Motor Vehicle Driver Distraction Using Naturalistic Driving Data , 2012, Traffic injury prevention.

[28]  Christopher J. Harris,et al.  An intelligent driver warning system for vehicle collision avoidance , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[29]  Linus Lindgren,et al.  BikeCOM – A cooperative safety application supporting cyclists and drivers at intersections , 2013 .

[30]  Hampton C. Gabler,et al.  Safety Benefits of Forward Collision Warning, Brake Assist, and Autonomous Braking Systems in Rear-End Collisions , 2012, IEEE Transactions on Intelligent Transportation Systems.

[31]  Stuart Newstead,et al.  Naturalistic cycling study: identifying risk factors for on-road commuter cyclists. , 2010, Annals of advances in automotive medicine. Association for the Advancement of Automotive Medicine. Annual Scientific Conference.

[32]  Liviu Iftode,et al.  The cyber-physical bike: a step towards safer green transportation , 2011, HotMobile '11.

[33]  Marco Dozza,et al.  What factors influence drivers' response time for evasive maneuvers in real traffic? , 2013, Accident; analysis and prevention.