Analytical Optimized Traffic Flow Recovery for Large-scale Urban Transportation Network

作     者：Fu, Sicheng Shi, Haotian Liang, Shixiao Wang, Xin Ran, Bin 

作者机构：Department of Civil and Environmental Engineering University of Wisconsin-Madison MadisonWI53706 United States Department of Industrial and Systems Engineering University of Wisconsin-Madison MadisonWI53706 United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Constrained optimization 

摘      要：The implementation of intelligent transportation systems (ITS) has enhanced data collection in urban transportation through advanced traffic sensing devices. However, the high costs associated with installation and maintenance result in sparse traffic data coverage. To obtain complete, accurate, and high-resolution network-wide traffic flow data, this study introduces the Analytical Optimized Recovery (AOR) approach that leverages abundant GPS speed data alongside sparse flow data to estimate traffic flow in large-scale urban networks. The method formulates a constrained optimization framework that utilizes a quadratic objective function with l2 norm regularization terms to address the traffic flow recovery problem effectively and incorporates a Lagrangian relaxation technique to maintain non-negativity constraints. The effectiveness of this approach was validated in a large urban network in Shenzhen s Futian District using the Simulation of Urban MObility (SUMO) platform. Analytical results indicate that the method achieves low estimation errors, affirming its suitability for comprehensive traffic analysis in urban settings with limited sensor deployment. Copyright © 2024, The Authors. All rights reserved.