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The Combined Distribution and Assignment Model: A New Solution Algorithm and Its Applications in Travel Demand Forecasting for Modern Urban Transportation

Author

Listed:
  • Heqing Tan

    (College of Civil and Transportation Engineering, Hohai University, 1 Xikang Road, Nanjing 210098, China)

  • Muqing Du

    (College of Civil and Transportation Engineering, Hohai University, 1 Xikang Road, Nanjing 210098, China)

  • Xiaowei Jiang

    (School of Business, Jinling Institute Technology, Nanjing 211100, China)

  • Zhaoming Chu

    (Road Traffic Safety Research Center of Ministry of Public Security, Beijing 100062, China)

Abstract

With the development of the advanced Intelligent Transportation System (ITS) in modern cities, it is of great significance to upgrade the forecasting methods for travel demand with the impact of ITS. The widespread use of ITS clearly changes the urban travelers’ behavior at present, in which case it is difficult for the conventional four-step travel demand forecasting model to have good performance. In this study, we apply the combined distribution and assignment (CDA) model to forecasting travel demand for modern urban transportation, in which travelers may choose the destination and path simultaneously. Furthermore, we present a new solution algorithm for solving the CDA model. With the network representation method that converts the CDA model into a standard traffic assignment problem (TAP), we develop a new path-based algorithm based on the gradient projection (GP) algorithm to solve the converted CDA model. The new solution algorithm is designed to find a more accurate solution compared with the widely used algorithm, the Evans’ two-stage algorithm. Two road networks, Sioux Falls and Chicago Sketch, are used to verify the performance of the new algorithm. Also, we conduct some experiments on the Sioux Falls network to illustrate several applications of the CDA model in consideration of the influences of ITS.

Suggested Citation

  • Heqing Tan & Muqing Du & Xiaowei Jiang & Zhaoming Chu, 2019. "The Combined Distribution and Assignment Model: A New Solution Algorithm and Its Applications in Travel Demand Forecasting for Modern Urban Transportation," Sustainability, MDPI, vol. 11(7), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:7:p:2167-:d:221872
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    References listed on IDEAS

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    6. Chen, Anthony & Kasikitwiwat, Panatda, 2011. "Modeling capacity flexibility of transportation networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 105-117, February.
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    Cited by:

    1. Jinjun Tang & Fan Gao & Fang Liu & Wenhui Zhang & Yong Qi, 2019. "Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM," Sustainability, MDPI, vol. 11(19), pages 1-19, October.
    2. Du, Muqing & Tan, Heqing & Chen, Anthony, 2021. "A faster path-based algorithm with Barzilai-Borwein step size for solving stochastic traffic equilibrium models," European Journal of Operational Research, Elsevier, vol. 290(3), pages 982-999.
    3. Seungkyu Ryu, 2021. "Mode Choice Change under Environmental Constraints in the Combined Modal Split and Traffic Assignment Model," Sustainability, MDPI, vol. 13(7), pages 1-16, March.
    4. Marta Rojo, 2020. "Evaluation of Traffic Assignment Models through Simulation," Sustainability, MDPI, vol. 12(14), pages 1-19, July.

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