rnings did not increase any additional work load, where 7 participants felt that it will take more time to get used to the warning messages. 80% participants appreciated the Safe Left-turn Maneuver Message more than the Advanced Warning Message. They also mentioned that the message content were clear and user-friendly. The survey results provided a positive indication of participants on the usage of in-vehicle audio messages for intersection safety and to recognize the traffic situation earlier especially in an obstructed or invisible traffic situation.
In this paper, a perceptual framework of in-vehicle collision warning message as a left-turn driving aid has been outlined and deployed through a driving simulator test to enhance safety at the urban intersections. From the comparative statistical analysis, values of mean speed of 30 participants showed a significant impact on driving behavior depending on driver’s reaction in the message scenarios. Moreover, the comparative analysis also showed that the ambience of night-time scenarios contribute to the effectiveness of performance measures, as most of the participants showed different driving pattern at night-time. In terms of mean acceleration, the baseline scenarios have more peaks in the curves especially after the delivery of Safe Left-turn Maneuver Message, which demonstrates that drivers accelerate and decelerate randomly as they were unsure on upcoming situations which cost more vehicular emissions.
With the impact of this particular warning message, most of them uniformly accelerate and make the turn safely. Most importantly, it had been observed that 80% of participants were able to make the turn in a short time than the baseline scenarios which contributed in reduction of travel time. In addition, the questionnaire survey and the collision analysis showed a promising possibility for this aid at left-turn movement. Considering the positive results and discussions of this experiment and utilizing the prospects and advantages of V2V technology, the designed audio warning would assist drivers to determine if a safe gap is available to execute the left-turn by providing messages depending on the availability of vehicles in opposite direction. To further investigate the potentiality of this type of technology, road test with a diverse demographic representation should be conducted to validate the operational impact of driving behavior with more performance measurement analysis in real environment.
The authors acknowledge that this research is supported in part by the United States Tier 1 University Transportation Center TranLIVE #DTRT12GUTC17/ KLK900-SB-003, and the National Science Foundation (NSF) under grants #1137732. The opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.
Conflicts of Interest
The authors declare no conflicts of interest.
|||Choi, E. (2010) Crash Factors in Intersection-Related Crashes: An On-Scene Perspective. Report No. DOT HS 811366, National Highway Traffic Safety Administration (NHTSA), Washington DC.|
|||Traffic Safety Facts Research Note (2013) Motor Vehicle Crashes: Overview. Report No. DOT HS 812101. National Highway Traffic Safety Administration NHTSA’s National Center for Statistics and Analysis.|
|||Traffic Safety Facts (2013) A Compilation of Motor Vehicle Crash Data from the Fatality Analysis Reporting System and the General Estimates System, DOT HS 812139, U.S. National Highway Traffic Safety Administration NHTSA’s National Center for Statistics and Analysis. U.S. Department of Transportation Washington DC.|
|||Misener, J. (2010) Cooperative Intersection Collision Avoidance System (CICAS): Signalized Left Turn Assist and Traffic Signal Adaptation. Department of Transportation Studies, University of California, California PATH Research Report UCB-ITS-PRR-2010-20.|
|||Brehmer, C.L, Kacir, K.C., Noyce, D.A. and Manser, M.P. (2003) Evaluation of Traffic Signal Displays for Protected/ Permissive Left-Turn Control. Report No. 493, National Cooperative Highway Research Program, Transportation Research Board, Washington DC.|
|||Davis, G.A. and Mudgal, A. (2013) Field Study of Driver Behavior at Permitted Left-Turn Indications. Intelligent Transportation Systems Institute, Center for Transportation Studies University of Minnesota, CTS Project #2012019, March 2013.|
Wang, X. and Abdel-Aty, M. (2008) Analysis of Left-Turn Crash Injury Severity by Conflicting Pattern Using Partial Proportional Odds Models. Journal of Accident Analysis and Prevention, 40, 1674-1682.
Davis, G.A. and Swenson, T. (2004) Field Study of Gap Acceptance by Left-Turning Drivers. Transportation Research Record: Journal of the Transportation Research Board, 1899, 71-75.
|||Davis, G.A. and Mudgal, A. (2015) Gap Selection Behavior at Permitted Left-Turn Indications on an Urban Arterial. 94th Transportation Research Board Annual Meeting, Washington DC, 11-15 January 2015, No. 15-5415.|
Caird, J. and Hancock, P. (1994) The Perception of Arrival Time for Different Oncoming Vehicles Arriving at an Intersection. Journal of Ecological Psychology, 6, 83-109.
|||Campbell, J.L. and Graham, J. (2012) Human Factors Guidelines for Road Systems, Second Edition. Project 17-47, NCHRP Report 600, Transportation Research Board, Washington DC.|
Zhou, H., Lownes, N.E., Ivan, J.N., Gårder, P.E. and Ravishanker, N. (2015) Left-Turn Gap Acceptance Behavior of Elderly Drivers at Unsignalized Intersections. Journal of Transportation Safety & Security, 7, 324-344.
|||Amjadi, R. (2009) Safety Evaluation of Offset Improvements for Left-Turn Lanes. Publication No. FHWA-HRT-09-036, The Federal Highway Administration (FHWA), US Department of Transportation, Washington DC.|
Troy, S. and Simpson, C. Offset Left-Turn Lanes. Report. North Carolina Department of Transportation.
Qiao, F., Jia, J., Yu, L., Li, Q. and Zhai, D. (2014) Drivers’ Smart Assistance System Based on Radio Frequency Identification Enhanced Safety and Reduced Emissions in Work Zones. Transportation Research Record: Journal of the Transportation Research Board, 2458, 37-46.
FARS-Transportation Injury Mapping System (TIMS). Fatality Analysis Reporting System Encyclopedia.
|||Li, Q., Qiao, F., Wang, X. and Yu, L. (2016) Drivers Smart Advisory System Improves Driving Performance at STOP Sign Intersections. Journal of Traffic and Transportation Engineering (English Edition), in Press.|
|||Li, Q. and Qiao, F. (2014) How Drivers’ Smart Advisory System Improves Driving Performance? A Simulator Imitation of Wireless Warning on Traffic Signal under Sun Glare. LAMBERT Academic Publishing, Saarbrücken.|
|||Qiao, F., Li, Q. and Yu, L. (2014) Testing Impacts of Work Zone X2V Communication System on Safety and Air Quality in Driving Simulator. Proceedings of the 21st ITS World Congress, Detroit, 7-11 September 2014, 1-11.|
|||Li, Q., Qiao, F., Wang, X. and Yu, L. (2013) Impacts of P2V Wireless Communication on Safety and Environment in Work Zones through Driving Simulator Tests. Proceedings of the 26th Annual Conference of the International Chinese Transportation Professionals Association (ICTPA), Tampa, 24-26 May 2013, Paper #26-179.|
|||Li, Q., Qiao, F. and Yu, L. (2015) Fuzzy Lane-Changing Models with Socio-Demographics and Vehicle-to-Infrastructure System Based on a Simulator Test. The Journal of Ergonomics, 5, 1000144.|
|||Qiao, F., Rahman, R., Li, Q. and Yu, L. (2016) Identifying Demographical Effects on Speed Patterns in Work Zones Using Smartphone Based Audio Warning Message System. Journal of Ergonomics. (in press)|
Li, Q., Qiao, F. and Yu, L. (2016) Vehicle Emission Implications of Drivers Smart Advisory System for Traffic Operations in Work Zones. Journal of Air & Waste Management. (in press)
|||Li, Q., Qiao, F., Qiao, Y. and Yu, L. (2016) Implications of Smartphone Messages on Driving Performance along Local Streets. Proceedings of the 11th Asia Pacific Transportation Development Conference and 29th ICTPA Annual Conference—Bridging the East and West: Theories and Practices of Transportation in the Asia Pacific, Hsinchu, 27-29 May 2016.|
Chen, H., Cao, L. and Logan, D.B. (2011) Investigation into the Effect of an Intersection Crash Warning System on Driving Performance in a Simulator. Journal of Traffic Injury Prevention, 12, 529-537.
|||Rahman, R., Qiao, F., Li, Q., Yu, L. and Kuo, P. (2015) Smart Phone Based Forward Collision Warning Messages in Work Zones to Enhance Safety and Reduce Emissions. Transportation Research Board 94th Annual Meeting, Washington DC, 11-15 January 2015, No. 15-0648.|
|||Derr, B.R. (2015) Evaluation of Change and Clearance Intervals Prior to the Flashing Yellow Arrow Permissive Left-Turn Indication, Project 03-125, NCHRP 03-125 [Anticipated] Federal Highway Administration, American Association of State Highway & Transportation Officials, National Cooperative Highway Research Program, Transportation Research Board, Washington DC. RiP Project 39946.|
Tran, C., Bark, K. and Ng-Thow-Hing, V. (2013) A Left-Turn Driving Aid Using Projected Oncoming Vehicle Paths with Augmented Reality. Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Eindhoven, 27-30 October 2013, 300-307.
|||Dabbour, E. and Easa, S.M. (2009) Perceptual Framework for a Modern Left-Turn Collision Warning System. International Journal of Applied Science, Engineering and Technology, 5, 8-14.|
|||Lee, S.E., Knipling, R.R., DeHart, M.C., Perez, M.A., Holbrook, G.T., Brown, S.B., Stone, S.R. and Olson, R.L. (2004) Vehicle-Based Countermeasures for Signal and Stop Sign Violations: Task 1. Intersection Control Violation Crash Analyses Task 2. Top-Level System and Human Factors Requirements. Report No. DOT HS 809 716. Virginia Tech Transportation Institute, National Highway Traffic Safety Administration, Department of Transportation, Washington DC.|
|||White, B. and Eccles, K.A. (2002) Inexpensive, Infrastructure-Based, Intersection Collision. Avoidance System to Prevent Left-Turn Crashes with Opposite-Direction Traffic. Transportation Research Record: Journal of the Transportation Research Board, (No. 1800), Paper No. 02-3846.|
|||Pierowicz, J., Jocoy, E., Lloyd, M., Bittner, A. and Pirson, B. (2000) Intersection Collision Avoidance Using ITS Countermeasures. DOT HS 809171, US DOT National Highway Traffic Safety Administration, Office of Advanced Safety Research Washington DC, Contract No. DTNH22-93-C-07024.|
|||Najm, W.G., Koopmann, J., Smith, J.D. and Brewer, J. (2010) Frequency of Target Crashes for IntelliDrive Safety Systems. Report No. DOT HS 811381, National Highway Traffic Safety Administration, US DOT National Highway Traffic Safety Administration, Washington DC.|
NHTSA Promotes Two Connected-Car Technologies to Prevent Crashes (2014) Automotive FLEET, the Car and Truck Fleet and Leasing Management Magazine.
United States Government Accountability Office (2013) Vehicle-to-Vehicle Technologies Expected to Offer Safety Benefits, but a Variety of Deployment Challenges Exist. GAO-14-13, Report to Congressional Requesters.
Woodhouse, K. (2012) Connected Vehicles: U-M Seeking 3,000 Ann Arbor Motorists for $18M Wireless Project to Prevent Collisions. The Ann Arbor News.
|||Rosenthal, T.J., Chrstos, J.P., Aponso, B.L. and Allen, R.W. (2004) A Driving Simulator for Testing The Visibility and Conspicuity Of Highway Designs And Traffic Control Device Placement. Submitted in 2004 Transportation Research Board Annual Meeting, Washington DC, 11-15 January 2004.|
Manual on Traffic Control Device Specifications (MUTCD) 2009 Edition.
Listen—Convert Text to Speech (MP3) Online.
|||Garcia, R. (2014) Roadway Design Manual, Texas Department of Transportation.|
Vehicle Stopping Sight Distance Calculator. CGSNetwork.com.
Friction and Co-Efficient of Friction. The Engineering Toolbox.
Copyright © 2020 by authors and Scientific Research Publishing Inc.
This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.