Urban layout optimization in a city network under an extended quadratic assignment problem framework

Abstract

For urban planning, transportation system in a region is essentially a result of the activity participation among geographically distributed locations. Facing congestion, pollution and other serious challenges, Metropolitan Planning Organizations (MPOs) need to offer effective decision-making supports to urban planning, especially urban layout optimization solutions. By mathematically defining the urban layout problem as a joint optimization of (a) activity space (AS)-to-location assignment, (b) demand distribution and (c) traffic flow assignment, this paper constructs an urban layout model by extending the quadratic assignment problem (QAP). In comparison with the standard model, our proposed framework considers possible flow distribution between different types of urban amenities, subject to the region-level demand control total constraint for certain activity types. A Maximal AS-to-location flow (MaxASLF) based heuristic algorithm and a Lagrangian relaxation-based approach are designed to solve the proposed models. Various examples and discussions are provided to examine the effectiveness of the proposed methods.

KEYWORDS:

• Urban layout
• travel demand distribution
• traffic assignment
• quadratic assignment problem

Acknowledgments

The authors would like to thank Dr. Xuesong Zhou at Arizona State University for his valuable comments. The authors confirm contribution to the paper as follows. Problem statement and model formulation: Jialu Fu and Lu (Carol) Tong; model implementation and algorithm design: Jialu Fu and Lu (Carol) Tong; data pre-processing: Jialu Fu; numerical experiments: Jialu Fu and Lu (Carol) Tong; draft manuscript preparation: Jialu Fu, Lu (Carol) Tong, Xianting Huang. All authors reviewed the results and approved the final version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The corresponding author is supported by National Key R&D Program of China (grant number 2019YFF0301400) and National Natural Science Foundation of China (grant numbers 71801006, 61961146005). The first author is supported by National Natural Science Foundation of China (grant number 71734004).