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Modeling user adoption of advanced traveler information systems: dynamic evolution and stationary equilibrium

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  • Yang, Hai
  • Meng, Qiang

Abstract

This paper models the growth rate and the saturation market penetration level for advanced traveler information system (ATIS) products/services with heterogeneous drivers. The price of using and the benefit gained from ATIS services are considered two key factors in explaining the growth of adoption of ATIS products. The information benefit is measured as the travel time saving between equipped and unequipped drivers and evaluated by a mixed stochastic and deterministic network equilibrium model. A modified logistic type growth model is adopted to describe the cumulative adoption of ATIS products over years. The final stationary equilibrium level of ATIS market penetration is so determined that the value of the information provided will decline to the point at which no new users will find it advantageous to purchase that service. The endogenous growth and stationary equilibrium model of market penetration of ATIS services is useful for forecasting the growth process and the impacts on the system performance of ATIS.

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  • Yang, Hai & Meng, Qiang, 2001. "Modeling user adoption of advanced traveler information systems: dynamic evolution and stationary equilibrium," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(10), pages 895-912, December.
    • Handle: RePEc:eee:transa:v:35:y:2001:i:10:p:895-912
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    Cited by:

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    2. Zhang, Rong & Verhoef, Erik T., 2006. "A monopolistic market for advanced traveller information systems and road use efficiency," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(5), pages 424-443, June.
    3. Lei Zhang & David Levinson, 2006. "Determinants of Route Choice and the Value of Traveler Information," Working Papers 200808, University of Minnesota: Nexus Research Group.
    4. Xingyuan Li & Jing Bai, 2021. "A Ridesharing Choice Behavioral Equilibrium Model with Users of Heterogeneous Values of Time," IJERPH, MDPI, vol. 18(3), pages 1-22, January.
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    7. Han, Linghui & Sun, Huijun & Wu, Jianjun & Zhu, Chengjuan, 2011. "Day-to-day evolution of the traffic network with Advanced Traveler Information System," Chaos, Solitons & Fractals, Elsevier, vol. 44(10), pages 914-919.
    8. Dillip Kumar Das, 2016. "Engendering Creative City Image by Using Information Communication Technology in Developing Countries," Urban Planning, Cogitatio Press, vol. 1(3), pages 1-12.
    9. Bifulco, Gennaro N. & Cantarella, Giulio E. & Simonelli, Fulvio & Velonà, Pietro, 2016. "Advanced traveller information systems under recurrent traffic conditions: Network equilibrium and stability," Transportation Research Part B: Methodological, Elsevier, vol. 92(PA), pages 73-87.
    10. Khan, Zaid & Amin, Saurabh, 2018. "Bottleneck model with heterogeneous information," Transportation Research Part B: Methodological, Elsevier, vol. 112(C), pages 157-190.
    11. Hiroaki Miyoshi, 2006. "Externalities and Taxation/Subsidization Policy of Vehicle Information and Communication System," ERSA conference papers ersa06p239, European Regional Science Association.
    12. 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.
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    15. Zhang, Fang & Lu, Jian & Hu, Xiaojian, 2022. "Integrated path controlling and subsidy scheme for mobility and environmental management in automated transportation networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).

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