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Model for Estimation Urban Transportation Supply-Demand Ratio

Author

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  • Chaoqun Wu
  • Yulong Pei
  • Jingpeng Gao

Abstract

The paper establishes an estimation model of urban transportation supply-demand ratio (TSDR) to quantitatively describe the conditions of an urban transport system and to support a theoretical basis for transport policy-making. This TSDR estimation model is supported by the system dynamic principle and the VENSIM (an application that simulates the real system). It was accomplished by long-term observation of eight cities’ transport conditions and by analyzing the estimated results of TSDR from fifteen sets of refined data. The estimated results indicate that an urban TSDR can be classified into four grades representing four transport conditions: “scarce supply,” “short supply,” “supply-demand balance,” and “excess supply.” These results imply that transport policies or measures can be quantified to facilitate the process of ordering and screening them.

Suggested Citation

  • Chaoqun Wu & Yulong Pei & Jingpeng Gao, 2015. "Model for Estimation Urban Transportation Supply-Demand Ratio," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-12, October.
    • Handle: RePEc:hin:jnlmpe:502739
    DOI: 10.1155/2015/502739
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    Cited by:

    1. Yunes Almansoub & Ming Zhong & Muhammad Safdar & Asif Raza & Abdelghani Dahou & Mohammed A. A. Al-qaness, 2023. "Modeling Impact of Transportation Infrastructure-Based Accessibility on the Development of Mixed Land Use Using Deep Neural Networks: Evidence from Jiang’an District, City of Wuhan, China," Sustainability, MDPI, vol. 15(21), pages 1-40, October.
    2. Xi Lu & Jiaqing Lu & Xinzheng Yang & Xumei Chen, 2022. "Assessment of Urban Mobility via a Pressure-State-Response (PSR) Model with the IVIF-AHP and FCE Methods: A Case Study of Beijing, China," Sustainability, MDPI, vol. 14(5), pages 1-23, March.
    3. Hussain, Etikaf & Bhaskar, Ashish & Chung, Edward, 2021. "A novel origin destination based transit supply index: Exploiting the opportunities with big transit data," Journal of Transport Geography, Elsevier, vol. 93(C).

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