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

Sicheng Fu, Haotian Shi, Shixiao Liang, Xin Wang, Bin Ran

TL;DR

The paper tackles the challenge of recovering high-resolution network-wide traffic flows in large urban networks with sparse sensor coverage by fusing GPS link-speed data and limited observed flows. It introduces the Analytical Optimized Recovery (AOR) framework, a constrained quadratic optimization that leverages a dynamic assignment matrix $\mathbf{A}=\boldsymbol{\rho}\boldsymbol{\theta}$ and $l_2$ regularization, with Stochastic Gradient Descent for hyperparameter tuning and Lagrange Relaxation to enforce non-negativity. The approach is validated on Shenzhen's Futian District using SUMO simulations and a real-world subnetwork, demonstrating robust recovery performance across road types and reasonable computation times, with WRME around 0.16–0.19 and MAE distributions favoring high-demand links. Findings indicate that AOR can enable city-scale traffic analysis and planning with limited sensor deployment, offering a scalable, interpretable tool for ITS data fusion and traffic management.

Abstract

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.

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

TL;DR

The paper tackles the challenge of recovering high-resolution network-wide traffic flows in large urban networks with sparse sensor coverage by fusing GPS link-speed data and limited observed flows. It introduces the Analytical Optimized Recovery (AOR) framework, a constrained quadratic optimization that leverages a dynamic assignment matrix and regularization, with Stochastic Gradient Descent for hyperparameter tuning and Lagrange Relaxation to enforce non-negativity. The approach is validated on Shenzhen's Futian District using SUMO simulations and a real-world subnetwork, demonstrating robust recovery performance across road types and reasonable computation times, with WRME around 0.16–0.19 and MAE distributions favoring high-demand links. Findings indicate that AOR can enable city-scale traffic analysis and planning with limited sensor deployment, offering a scalable, interpretable tool for ITS data fusion and traffic management.

Abstract

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.
Paper Structure (19 sections, 34 equations, 16 figures, 6 tables, 2 algorithms)

This paper contains 19 sections, 34 equations, 16 figures, 6 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Computer Vision Based
  4. Data Driven Based
  5. Optimization Framework Based
  6. Methodology
  7. Problem Statement
  8. Notations
  9. Dynamic Assignment Matrix
  10. Analytical Formulation
  11. SGD for Adjusting Hyperparameters
  12. Lagrange Relaxation
  13. Numerical Experiments
  14. Dataset Description
  15. Evaluation Metrics
  16. ...and 4 more sections

Figures (16)

  • Figure 1: Flowchart of the proposed Analytical Optimized Recovery (AOR) approach
  • Figure 2: GIS representation of the road network in Futian District
  • Figure 3: Spatial distribution of OD points
  • Figure 4: Traffic surveillance camera locations in Futian District
  • Figure 5: Evolution of hyperparameters in the Stochastic Gradient Descent (SGD) process
  • ...and 11 more figures