[Paper Review] Traffic Performance Score for Measuring the Impact of COVID-19 on Urban Mobility

Zhiyong Cui, Meixin Zhu+6 more|arXiv (Cornell University)|Jul 1, 2020

Traffic Prediction and Management Techniques7 references19 citations

TL;DR

This paper proposes a Traffic Performance Score (TPS) to measure network-wide urban traffic performance by integrating multiple traffic parameters, enabling real-time and historical spatial-temporal analysis. The TPS platform, developed for the Greater Seattle area, reveals that COVID-19 drastically reduced traffic volume (VMT) and speeding rates, reshaping urban mobility through both reduced travel demand and altered driving behavior.

ABSTRACT

Measuring traffic performance is critical for public agencies who manage traffic and individuals who plan trips, especially when special events happen. The COVID-19 pandemic has significantly influenced almost every aspect of daily life, including urban traffic patterns. Thus, it is important to measure the impact of COVID-19 on transportation to further guide agencies and residents to properly respond to changes in traffic patterns. However, most existing traffic performance metrics incorporate only a single traffic parameter and measure only the performance of individual corridors. To overcome these challenges, in this study, a Traffic Performance Score (TPS) is proposed that incorporates multiple parameters for measuring network-wide traffic performance. An interactive web-based TPS platform that provides real-time and historical spatial-temporal traffic performance analysis is developed by the STAR Lab at the University of Washington. Based on data from this platform, this study analyzes the impact of COVID-19 on different road segments and the traffic network as a whole. Considering this pandemic has greatly reshaped social and economic operations, this study also evaluates how COVID-19 is changing the urban mobility from both travel demand and driving behavior perspectives.

Motivation & Objective

• To address the lack of comprehensive, network-wide traffic performance metrics that integrate multiple traffic parameters.
• To develop a real-time, interactive web-based platform for monitoring and analyzing urban traffic performance at both segment and network levels.
• To quantify the impact of COVID-19 on urban mobility by analyzing changes in travel demand and driving behavior across different road segments.
• To evaluate how pandemic-related restrictions reshaped urban traffic patterns, particularly in terms of volume, speed, and congestion at scale.
• To provide a scalable framework for public agencies to monitor and respond to long-term shifts in urban mobility post-pandemic.

Proposed method

• The Traffic Performance Score (TPS) is calculated using a weighted combination of key traffic parameters: speed, volume, travel time, and congestion level across the network.
• The TPS platform integrates real-time loop detector data from the Washington State Department of Transportation (WSDOT) with historical traffic data for spatial-temporal analysis.
• The platform uses a normalized scoring system (0–12) to represent traffic performance, where higher scores indicate better performance and lower congestion.
• Speeding rates are computed using lane-level loop detector data, with the formula: SpeedingRate = Σ(Qi for Vi > VTH) / Σ(Qi), where VTH is the speed threshold.
• The study compares pre- and during-COVID-19 periods (Jan 19 to May 10, 2020) using weekly time-series analysis of TPS and VMT.
• The platform is designed to be scalable and extendable to other cities and regions, supporting future large-scale mobility analysis.

Experimental results

Research questions

• RQ1 How does the proposed Traffic Performance Score (TPS) effectively capture network-wide traffic performance compared to single-parameter metrics?
• RQ2 What were the changes in network-wide traffic performance and vehicle miles traveled (VMT) in the Greater Seattle area before and during the COVID-19 pandemic?
• RQ3 How did travel demand vary across different road segments during the pandemic, and what factors influenced these changes?
• RQ4 How did driving behavior, particularly speeding, change in response to pandemic-related restrictions and reduced traffic volumes?
• RQ5 To what extent did the pandemic induce long-term shifts in urban mobility patterns, and how can these be quantified using data-driven metrics?

Key findings

• The TPS and VMT both showed a significant decline in traffic performance and volume following the Washington State stay-at-home order on March 23, 2020.
• Traffic volume dropped sharply four weeks before the stay-at-home order, with VMT decreasing by approximately 50% during peak pandemic periods.
• Speeding rates on major freeways (I-5, I-405, SR-520) decreased significantly during the pandemic, with SR-520 showing a near-zero speeding rate after the order.
• The reduction in speeding was most pronounced on I-5 and I-405, where rates dropped to their lowest in late March and remained below pre-pandemic levels.
• The TPS platform successfully visualized real-time and historical traffic patterns, enabling both segment-level and network-wide analysis of mobility changes.
• The study confirms that the pandemic induced measurable, large-scale shifts in both travel demand and driving behavior, suggesting potential long-term changes in urban mobility patterns.