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Effects of different factors on drivers’ guidance compliance behaviors under road condition information shown on VMS

Listed author(s):
  • Zhong, Shiquan
  • Zhou, Lizhen
  • Ma, Shoufeng
  • Jia, Ning
Registered author(s):

    It is generally accepted that compliance behavior is affected by many factors. The purpose of this study is to investigate the effects of diverse factors on drivers’ guidance compliance behaviors under road condition information shown on graphic variable message sign (VMS), and based on this to find out a better information release mode. The involved data were obtained from questionnaire survey, and ordinal regression was used to analyze the casual relation between guidance compliance behavior and its influencing factors. Based on an overall analysis of conditions in driver’s route choice, an accurate method was proposed to calculate the compliance rate. The model testing information indicated that ordinal regression model with complementary log–log being the link function was appropriate to quantify the relation between the compliance rate and the factors. The estimation results showed that age, driving years, average annual mileage, monthly income, driving style, occupation, the degree of trust in VMS, the familiarity with road network and the route choice style were significant determinants of guidance compliance behavior. This paper also compared two different guidance modes which were ordinary guidance mode (M1) and predicted guidance mode (M2) through simulation. The average speed fluctuations and average travel time supported that M2 had better effect in improving traffic flow and balancing traffic load and resource. Some detailed suggestions of releasing guidance information were proposed with the explanation by flow-density curve and variation of traffic flows. These findings are the foundation to design and improve guidance systems by assessing guidance effect and modifying guidance algorithm.

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    Article provided by Elsevier in its journal Transportation Research Part A: Policy and Practice.

    Volume (Year): 46 (2012)
    Issue (Month): 9 ()
    Pages: 1490-1505

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    Handle: RePEc:eee:transa:v:46:y:2012:i:9:p:1490-1505
    DOI: 10.1016/j.tra.2012.05.022
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    1. Kanafani, A. & Al-Deek, H., 1991. "A simple model for route guidance benefits," Transportation Research Part B: Methodological, Elsevier, vol. 25(4), pages 191-201, August.
    2. Ozbay, Kaan & Bartin, Bekir, 2004. "Estimation Of Economic Impact Of Vms Route Guidance Using Microsimulation," Research in Transportation Economics, Elsevier, vol. 8(1), pages 215-241, January.
    3. Jou, Rong-Chang & Lam, Soi-Hoi & Liu, Yu-Hsin & Chen, Ke-Hong, 2005. "Route switching behavior on freeways with the provision of different types of real-time traffic information," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(5), pages 445-461, June.
    4. Yin, Yafeng & Yang, Hai, 2003. "Simultaneous determination of the equilibrium market penetration and compliance rate of advanced traveler information systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(2), pages 165-181, February.
    5. Yang, Hai & Kitamura, Ryuichi & Jovanis, Paul P. & Vaughn, Kenneth M. & Abdel-aty, Mohammed A. & Reddy, Prasuna Dvg, 1993. "Exploration Of Driver Route Choice With Advanced Traveler Information Using Neural Network Concepts," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt53d2t6df, Institute of Transportation Studies, UC Berkeley.
    6. Emmerink, Richard H. M. & Nijkamp, Peter & Rietveld, Piet & Van Ommeren, Jos N., 1996. "Variable message signs and radio traffic information: An integrated empirical analysis of drivers' route choice behaviour," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(2), pages 135-153, March.
    7. Papageorgiou, Markos, 1990. "Dynamic modeling, assignment, and route guidance in traffic networks," Transportation Research Part B: Methodological, Elsevier, vol. 24(6), pages 471-495, December.
    8. Fu, Liping, 2001. "An adaptive routing algorithm for in-vehicle route guidance systems with real-time information," Transportation Research Part B: Methodological, Elsevier, vol. 35(8), pages 749-765, September.
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