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Extending Dynamic Origin-Destination Estimation to Understand Traffic Patterns During COVID-19

Han Yu, Suyanpeng Zhang, Sze-chuan Suen, Maged Dessouky, Fernando Ordonez

TL;DR

This paper proposes a data-driven approach to estimate OD traffic flow using sensor data on highways and local roads, and extends prior DODE models to improve accuracy and realism in order to estimate how policies affect OD traffic flow in large urban networks.

Abstract

Estimating dynamic Origin-Destination (OD) traffic flow is crucial for understanding traffic patterns and the traffic network. While dynamic origin-destination estimation (DODE) has been studied for decades as a useful tool for estimating traffic flow, few existing models have considered its potential in evaluating the influence of policy on travel activity. This paper proposes a data-driven approach to estimate OD traffic flow using sensor data on highways and local roads. We extend prior DODE models to improve accuracy and realism in order to estimate how policies affect OD traffic flow in large urban networks. We applied our approach to a case study in Los Angeles County, where we developed a traffic network, estimated OD traffic flow between health districts during COVID-19, and analyzed the relationship between OD traffic flow and demographic characteristics such as income. Our findings demonstrate that the proposed approach provides valuable insights into traffic flow patterns and their underlying demographic factors for a large-scale traffic network. Specifically, our approach allows for evaluating the impact of policy changes on travel activity. The approach has practical applications for transportation planning and traffic management, enabling a better understanding of traffic flow patterns and the impact of policy changes on travel activity.

Extending Dynamic Origin-Destination Estimation to Understand Traffic Patterns During COVID-19

TL;DR

This paper proposes a data-driven approach to estimate OD traffic flow using sensor data on highways and local roads, and extends prior DODE models to improve accuracy and realism in order to estimate how policies affect OD traffic flow in large urban networks.

Abstract

Estimating dynamic Origin-Destination (OD) traffic flow is crucial for understanding traffic patterns and the traffic network. While dynamic origin-destination estimation (DODE) has been studied for decades as a useful tool for estimating traffic flow, few existing models have considered its potential in evaluating the influence of policy on travel activity. This paper proposes a data-driven approach to estimate OD traffic flow using sensor data on highways and local roads. We extend prior DODE models to improve accuracy and realism in order to estimate how policies affect OD traffic flow in large urban networks. We applied our approach to a case study in Los Angeles County, where we developed a traffic network, estimated OD traffic flow between health districts during COVID-19, and analyzed the relationship between OD traffic flow and demographic characteristics such as income. Our findings demonstrate that the proposed approach provides valuable insights into traffic flow patterns and their underlying demographic factors for a large-scale traffic network. Specifically, our approach allows for evaluating the impact of policy changes on travel activity. The approach has practical applications for transportation planning and traffic management, enabling a better understanding of traffic flow patterns and the impact of policy changes on travel activity.
Paper Structure (27 sections, 30 equations, 9 figures, 3 tables)

This paper contains 27 sections, 30 equations, 9 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Literature
  3. DODE estimation
  4. Covid-19 Policy and traffic
  5. Contributions
  6. The Model
  7. Network Definition
  8. General Framework
  9. Module 1: Base DODE Model
  10. Module 2: Extended DODE Model
  11. Incorporating local road traffic information
  12. Incorporating Daily Traffic Pattern Information: Symmetry Constraints
  13. Imposing fidelity to total traffic flow
  14. Numerical Analysis: Los Angeles Traffic During the 2020 COVID Stay-at-home Order
  15. Data Acquisition and Model Parameterization
  16. ...and 12 more sections

Figures (9)

  • Figure 1: General Framework.
  • Figure 2: Example graph $G$ with OD $rs$ and links.
  • Figure 3: Health districts and traffic sensors in LA County. Health district boundaries are shown in red. Blue dots indicate sensor locations on highways.
  • Figure 4: Different levels of aggregation. Each dot represents both an origin and a destination. The dots are connected by links that follow the highway network in LA County.
  • Figure 5: Traffic flow comparison in 2019 and 2020
  • ...and 4 more figures