Trajectory Data-based Model Predictive Control of Adjacent Intersection Signal Control Under Traffic Accident Scenarios

Strategic Insights -

In a critical study, researchers at the University of Shanghai for Science and Technology have developed a highly scalable model predictive control strategy for traffic accident scenarios. The proposed method, which utilizes vehicle trajectory data generated during an accident, offers technical support for maintaining traffic flow stability and smoothness when autonomous and human-driven vehicles coexist. The study was funded by the National Natural Science Foundation of China (NSFC), Science & Technology Commission of Shanghai Municipality (STCSM), and Shanghai Pujiang Program.

Key Takeaways:

  • The proposed model predictive control strategy takes vehicle trajectory data generated during an accident as input, quantifies the impact of the accident, and outputs an optimal signal control scheme to reduce delays and prevent congestion diffusion.
  • The model was validated by performing simulations in Python and accessing the component object model interface of VISSIM, with comparisons made with the Decentralized-MPC method and the fixed-time control scheme.
  • The proposed MPC-Incident method reduced the average vehicle delay by 7.8% compared with the Decentralized-MPC method.
  • The study highlighted the applicability of the Decentralized-MPC method in traffic accident scenarios with different traffic volumes and proposed an optimal signal control scheme to mitigate the effects of accidents on traffic flow.
  • The researchers successfully demonstrated the effectiveness of the proposed method in a wide range of realistic high-density urban road networks.

Statistics:

  • The proposed MPC-Incident method reduced the average vehicle delay by 7.8% compared with the Decentralized-MPC method.
  • The study was funded by the National Natural Science Foundation of China (NSFC), Science & Technology Commission of Shanghai Municipality (STCSM), and Shanghai Pujiang Program.
  • The model was validated using Python and VISSIM, with a total of 12 simulation scenarios used for the base case and the comparison.

Sources:

  • Trajectory Data-based Model Predictive Control of Adjacent Intersection Signal Control Under Traffic Accident Scenarios. Transportation Research Record: Journal of the Transportation Research Board, 2025.
  • NewsRx. Study Findings from University of Shanghai for Science and Technology Broaden Understanding of Information Technology (Trajectory Data-based Model Predictive Control of Adjacent Intersection Signal Control Under Traffic Accident Scenarios). Information Technology Newsweekly. November 4, 2025; p 930.
  • University of Shanghai for Science and Technology. Smart Urban Mobil Inst, Shanghai, People's Republic of China.
  • Sage Publications Inc, 2455 Teller Rd, Thousand Oaks, CA 91320, USA