Macroscopic Fundamental Diagram Based Discrete Transportation Network Design




Hu, Guojing
Lu, Weike
Wang, Feng
Whalin, Robert

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The presence of demand uncertainty brings challenges to network design problems (NDP), because fluctuations in origin-destination (OD) demand have a prominent effect on the corresponding total travel time, which is usually adopted as an index to evaluate the network design problem. Fortunately, the macroscopic fundamental diagram (MFD) has been proved to be a property of the road network itself, independent of the origin-destination demand. Such characteristics of an MFD provide a new theoretical basis to assess the traffic network performance and further appraise the quality of network design strategies. Focusing on improving network capacity under the NDP framework, this paper formulates a bi-level programming model, where at the lower level, flows are assigned to the newly extended network subject to user equilibrium theory, and the upper level determines which links should be added to achieve the maximum network capacity. To solve the proposed model, we design an algorithm framework, where traffic flow distribution of each building strategy is calculated under the dynamic user equilibrium (DUE), and updated through the VISSIM-COM-Python interaction. Then, the output data are obtained to shape MFDs, and k-means clustering algorithm is employed to quantify the MFD-based network capacity. Finally, the methodology is implemented in a test network, and the results show the benefits of using the MFD-based method to solve the network design problem under stochastic OD demands. Specifically, the capacity paradox is also presented in the test results.



k-means clustering, stochastic systems, travel time, algorithm framework, traffic control, bilevel programming models, Ingram School of Engineering


Hu, G., Lu, W., Wang, F., & Whalin, R. W. (2020). Macroscopic fundamental diagram based discrete transportation network design. Journal of Advanced Transportation, 2020, pp. 1-14.


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© 2020 Guojing Hu et al.

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This work is licensed under a Creative Commons Attribution 3.0 United States License.

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