Multi-attribute decision-making method for sustainable traffic network design

A multi-attribute decision-making, transportation network technology, applied in the field of multi-attribute decision-making, can solve problems such as failure to fully consider the conflict of sustainability

Pending Publication Date: 2019-11-15
SOUTHEAST UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

In previous research work, the main goal of traffic network design was to alleviate traffic congestion, i.e. minimize total travel t

Method used

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  • Multi-attribute decision-making method for sustainable traffic network design
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  • Multi-attribute decision-making method for sustainable traffic network design

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Experimental program
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Embodiment Construction

[0073] Such as image 3 The Nguyen-Dupuis traffic network shown is widely used in traffic studies to validate various methods. Link parameters include free flow travel time, link capacity and link length, as shown in Table 1

[0074] Table 1. Link parameters of the Nguyen-Dupuis network

[0075]

[0076]

[0077] There are two origin areas 1 and 4 and two destination areas 2 and 3 in the Nguyen-Dupuis network. Assume that the traffic of origins 1 and 4 during the peak time is 1800pcu / h and 1200pcu / h respectively. That is to say, O 1 =1800pcu / h, O 4 =1200pcu / h. Candidate sections for improving traffic capacity are 3, 4, 5, 9, 13, 16, and 19. The problem is to determine the optimal link capacity improvement mode according to four attributes in order to make the best use of the investment budget. M=500 investment allocation patterns are randomly generated using the Dirichlet distribution Dir(α) from α=(1,1,1,1,1,1,1), where its dimension is the number of candidate ro...

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Abstract

The invention discloses potential sustainable traffic network design integrated with societal, economic, environmental and safe dimensions. Therefore, a multi-target double-layer model is developed, and the leader-follower essential relationship between a planner and a traveler is clearly expressed. On an upper layer, a planner designs a sustainable traffic network by optimizing investment budgetallocation. At the lower layer, the traveler makes a series of behavior responses to the traffic network design at the upper layer through a four-stage model with feedback. The feedback process can converge to traffic system equalization, and an equalization result is further fed back to the upper layer to measure the sustainability of the traffic network. In order to solve the proposed multi-target double-layer model, the invention designs a multi-attribute decision-making method on the basis of Dirichlet distribution, a continuous average method, a Frank-Wolfe algorithm and a Dijkstra algorithm. An example of an Nguyen-Dupuis network is used for verifying the effectiveness of the method disclosed by the invention. Research finds that a designed method is effective for searching satisfactory solutions.

Description

Technical field: [0001] The invention proposes a multi-attribute decision-making method for sustainable traffic network design, which belongs to the technical field of traffic engineering. Background technique: [0002] Transport systems play a key role in sustainable development, and as such, sustainable urban transport has recently become an important research area. A sustainable transport system is usually characterized by three dimensions: economic, environmental and social. However, existing transportation research only focuses on one or at most two dimensions [1] . Only a small number of studies consider all three dimensions simultaneously. It should be noted that there may be more than one measure in each of the three dimensions. For example, in addition to the total travel time of the system, the economic dimension can also include consumer surplus, network reserve capacity, total investment cost, total toll revenue, etc. In addition, safety is also an important...

Claims

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Application Information

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IPC IPC(8): G06F17/50G08G1/01
CPCG08G1/0104
Inventor 林宏志赵宇轩
Owner SOUTHEAST UNIV
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