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Spatial differentiation and network externality in pricing mechanism of online car hailing platform

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  • Wu, Tian
  • Zhang, Mengbo
  • Tian, Xin
  • Wang, Shouyang
  • Hua, Guowei

Abstract

In order to eliminate the information asymmetry of supply and demand in transport service market, online car hailing platforms appear recent years. Almost all the transport service markets have the property of spatial differentiation, i.e, passengers' demand and vehicle supply are subject to different distributions in different regions at different time. Under the framework of two-sided market theory, this paper develops an intermediary pricing model for transport service market to evaluate the impact of spatial differentiation and network externality on online car hailing platform's pricing mechanism, based on the analysis of transport services' supply-demand matching. The analytic solution shows that the optimal price of the online car hailing platform is decided by the degree of heterogeneity in customer valuation on demand side and driver cost on supply side. Given optimal price on demand side, supply price in a region directly determines the supply of transport service in the same region, has cross effect on the supply in other region, and has indirect effect on demand price through the aggregate supply. Based on the trip record data of New York City, the simulation shows that online car hailing platform try to customize higher prices to attract more drivers and alleviate the passengers' demand pressure in morning rush hour. Furthermore, the simulation also shows that the disparity of the flow density in adjacent areas may affect the price adjustment mechanism of the online car hailing platform, and if the regional disparity further expands, the optimal choice of the platform is to increase the price of supply side, rather than the demand side.

Suggested Citation

  • Wu, Tian & Zhang, Mengbo & Tian, Xin & Wang, Shouyang & Hua, Guowei, 2020. "Spatial differentiation and network externality in pricing mechanism of online car hailing platform," International Journal of Production Economics, Elsevier, vol. 219(C), pages 275-283.
    • Handle: RePEc:eee:proeco:v:219:y:2020:i:c:p:275-283
    DOI: 10.1016/j.ijpe.2019.05.007
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    References listed on IDEAS

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    Cited by:

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    6. Zhong, Yuanguang & Lan, Yibo & Chen, Zhi & Yang, Jiazi, 2023. "On-demand ride-hailing platforms with heterogeneous quality-sensitive customers: Dedicated system or pooling system?," Transportation Research Part B: Methodological, Elsevier, vol. 173(C), pages 247-266.
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    9. Wu, Tian & Wang, Shouyang & Wang, Lining & Tang, Xiao, 2022. "Contribution of China's online car-hailing services to its 2050 carbon target: Energy consumption assessment based on the GCAM-SE model," Energy Policy, Elsevier, vol. 160(C).
    10. Zhong, Yuanguang & Pan, Qi & Xie, Wei & Cheng, T.C.E. & Lin, Xiaogang, 2020. "Pricing and wage strategies for an on-demand service platform with heterogeneous congestion-sensitive customers," International Journal of Production Economics, Elsevier, vol. 230(C).
    11. Yan, Nina & Xu, Xun & Tong, Tingting & Huang, Liujia, 2021. "Examining consumer complaints from an on-demand service platform," International Journal of Production Economics, Elsevier, vol. 237(C).
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    13. Feng, Nan & Xu, Nan & Feng, Haiyang & Li, Minqiang, 2022. "Turn on instant booking or not? Decisions of rival hosts," Annals of Tourism Research, Elsevier, vol. 96(C).
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    15. Pazoki, Mostafa & Samarghandi, Hamed, 2021. "Word-Of-Mouth and estimating demand based on network structure and epidemic models," European Journal of Operational Research, Elsevier, vol. 291(1), pages 323-334.
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    17. Ding Wang & Kaan Ozbay & Zilin Bian, 2021. "Modeling and Analysis of Optimal Strategies for Leveraging Ride-Sourcing Services in Hurricane Evacuation," Sustainability, MDPI, vol. 13(8), pages 1-22, April.

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