Feasibility of Using In-Vehicle Video Data to Explore How to Modify Driver Behavior That Causes Nonrecurring Congestion

This research report—a product of the Reliability focus area of the second Strategic Highway Research Program (SHRP 2)—presents findings on the feasibility of using existing in-vehicle data sets, collected in naturalistic driving settings, to make inferences about the relationship between observed driver behavior and nonrecurring congestion. General guidance is provided on the protocols and procedures for conducting video data reduction analysis. In addition, the report includes technical guidance on the features, technologies, and complementary data sets that researchers should consider when designing future instrumented in-vehicle data collection studies. Finally, a new modeling approach is advanced for travel time reliability performance measurement across a variety of traffic congestion conditions.

[1]  Hesham Rakha,et al.  Trip Travel-Time Reliability: Issues and Proposed Solutions , 2010, J. Intell. Transp. Syst..

[2]  H. J. Van Zuylen,et al.  Monitoring and Predicting Freeway Travel Time Reliability: Using Width and Skew of Day-to-Day Travel Time Distribution , 2005 .

[3]  Thomas A. Dingus,et al.  The 100-Car Naturalistic Driving Study Phase II – Results of the 100-Car Field Experiment , 2006 .

[4]  Michael G.H. Bell,et al.  A game theory approach to measuring the performance reliability of transport networks , 2000 .

[5]  K Thiriez,et al.  LARGE TRUCK CRASH CAUSATION STUDY - INTERIM REPORT , 2002 .

[6]  Hesham Rakha,et al.  Calibration Issues for Multistate Model of Travel Time Reliability , 2010 .

[7]  Edward C. Sullivan,et al.  NEW MODEL FOR PREDICTING FREEWAY INCIDENTS AND INCIDENT DELAYS , 1997 .

[8]  Robert L. Bertini,et al.  I-880 Field Experiment: Analysis of Incident Data , 1997 .

[9]  Suzanne E. Lee,et al.  A COMPREHENSIVE EXAMINATION OF NATURALISTIC LANE-CHANGES , 2004 .

[10]  Robert J. Carroll,et al.  Light Vehicle-Heavy Vehicle Interactions: A Preliminary Assessment Using Critical Incident Analysis , 2002 .

[11]  Zachary R. Doerzaph,et al.  Live Stop-Controlled Intersection Data Collection , 2007 .

[12]  Jerry Holway,et al.  Types of vehicles , 2009 .

[13]  Haitham Al-Deek,et al.  Using Real-Life Dual-Loop Detector Data to Develop New Methodology for Estimating Freeway Travel Time Reliability , 2006 .

[14]  Pravin Varaiya,et al.  Travel-Time Reliability as a Measure of Service , 2003 .

[15]  Kenneth W. Gish,et al.  A pilot study to test multiple medication usage and driving functioning , 2008 .

[16]  Richard J. Hanowski,et al.  The Drowsy Driver Warning System Field Operational Test: Data Collection Methods: Final Report , 2008 .

[17]  Haitham Al-Deek,et al.  Estimating Magnitude and Duration of Incident Delays , 1997 .

[18]  Dot Hs,et al.  The 100 Car Naturalistic Driving Study , 2002 .

[19]  Josef F. Krems,et al.  The quality of behavioral and environmental indicators used to infer the intention to change lanes , 2007 .

[20]  Yinhai Wang,et al.  Quantifying Incident-Induced Travel Delays on Freeways Using Traffic Sensor Data: Phase II , 2008 .

[21]  John D Lee,et al.  Extending parental mentoring using an event-triggered video intervention in rural teen drivers. , 2007, Journal of safety research.

[22]  C. Nash International Transport Forum , 2010 .

[23]  Vincent K. Omachonu,et al.  Displays and controls , 1991 .

[24]  Hesham Rakha,et al.  Multistate Model for Travel Time Reliability , 2010 .

[25]  James R. Sayer,et al.  Automotive Collision Avoidance System Field Operational Test Report: Methodology and Results Appendices , 2005 .