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Enhancing traffic safety analysis with digital twin technology: integrating vehicle dynamics and environmental factors into microscopic traffic simulation

  • Guanhao Xu
  • , Jianfei Chen
  • , Zejiang Wang
  • , Anye Zhou
  • , Max Schrader
  • , Joshua Bittle
  • , Yunli Shao

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Traffic safety is a critical concern in transportation engineering and urban planning. Traditional traffic safety analysis requires trained observers to collect data in the field, which is time-consuming, labor-intensive, and sometimes inaccurate. In recent years, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, have been utilized to study traffic safety. However, microscopic traffic simulation only focuses on traffic-related factors, such as traffic volume, traffic signals, and lane configurations, neglecting vehicle dynamics and environment-related factors like weather and lighting conditions, which can significantly impact traffic safety. In light of this, this paper explores the application of digital twin technology in traffic safety analysis, integrating vehicle simulators, which consider vehicle dynamics and environmental factors, and microscopic traffic simulators, which simulate the operations of traffic flow, for enhanced safety evaluations. Various scenarios, including different weather conditions and visibility levels, are simulated using a digital twin of a road segment in Tuscaloosa, Alabama. The simulations employ Surrogate Safety Measures (SSMs) like Time to Collision (TTC) and Deceleration Rate to Avoid a Crash (DRAC) to assess safety under varying conditions. The results demonstrate that traffic digital twin can identify potential safety issues that traditional microscopic simulation cannot, providing insights for improving traffic control strategies and transportation infrastructure to enhance traffic safety.

Original languageEnglish
Article number44404
JournalScientific Reports
Volume15
Issue number1
DOIs
StatePublished - Dec 2025

Funding

This material is based upon work supported by the US Department of Energy, Vehicle Technologies Office, Energy Efficient Mobility Systems (EEMS) program, under project Improving Network-Wide Fuel Economy and Enabling Traffic Signal Optimization Using Infrastructure and Vehicle-Based Sensing and Connectivity (EEMS107).

Keywords

  • Digital twin
  • IPG CarMaker
  • SUMO
  • Surrogate safety measures
  • Traffic safety
  • X-in-the-loop

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