Macroscopic Fundamental Diagram Based Discrete Transportation Network Design
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...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/4951953 |
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| author | Guojing Hu Weike Lu Feng Wang Robert W. Whalin |
| author_facet | Guojing Hu Weike Lu Feng Wang Robert W. Whalin |
| author_sort | Guojing Hu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-22e7981bad754afa996718dc9b5c7289 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-22e7981bad754afa996718dc9b5c72892025-02-03T05:49:29ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/49519534951953Macroscopic Fundamental Diagram Based Discrete Transportation Network DesignGuojing Hu0Weike Lu1Feng Wang2Robert W. Whalin3Department of Mathematics and Statistical Sciences, Jackson State University, Jackson, MS 39217, USAAlabama Transportation Institute, The University of Alabama, Tuscaloosa, AL 35487, USAIngram School of Engineering, Texas State University, San Marcos, TX 78666, USADepartment of Civil and Environmental Engineering, Jackson State University, Jackson, MS 39217, USAThe 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.http://dx.doi.org/10.1155/2020/4951953 |
| spellingShingle | Guojing Hu Weike Lu Feng Wang Robert W. Whalin Macroscopic Fundamental Diagram Based Discrete Transportation Network Design Journal of Advanced Transportation |
| title | Macroscopic Fundamental Diagram Based Discrete Transportation Network Design |
| title_full | Macroscopic Fundamental Diagram Based Discrete Transportation Network Design |
| title_fullStr | Macroscopic Fundamental Diagram Based Discrete Transportation Network Design |
| title_full_unstemmed | Macroscopic Fundamental Diagram Based Discrete Transportation Network Design |
| title_short | Macroscopic Fundamental Diagram Based Discrete Transportation Network Design |
| title_sort | macroscopic fundamental diagram based discrete transportation network design |
| url | http://dx.doi.org/10.1155/2020/4951953 |
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