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Jam-absorption driving with data assimilation

Siyu Li, Ryosuke Nishi, Daichi Yanagisawa, Katsuhiro Nishinari

TL;DR

The findings suggest that the proposed DA framework can reduce control failures and prevent significant declines and deteriorations in JAD performance caused by changes in traffic characteristics, e.g., weather conditions or traffic composition.

Abstract

This paper introduces a data assimilation (DA) framework based on the extended Kalman filter-cell transmission model, designed to assist jam-absorption driving (JAD) operation to alleviate sag traffic congestion. To ascertain and demonstrate the effectiveness of the DA framework for JAD operation, in this paper, we initially investigated its impact on the motion and control performance of a single absorbing vehicle. Numerical results show that the DA framework effectively mitigated underestimated or overestimated control failures of JAD caused by misestimation of key parameters (e.g., free flow speed and critical density) of the traffic flow fundamental diagram. The findings suggest that the proposed DA framework can reduce control failures and prevent significant declines and deteriorations in JAD performance caused by changes in traffic characteristics, e.g., weather conditions or traffic composition.

Jam-absorption driving with data assimilation

TL;DR

The findings suggest that the proposed DA framework can reduce control failures and prevent significant declines and deteriorations in JAD performance caused by changes in traffic characteristics, e.g., weather conditions or traffic composition.

Abstract

This paper introduces a data assimilation (DA) framework based on the extended Kalman filter-cell transmission model, designed to assist jam-absorption driving (JAD) operation to alleviate sag traffic congestion. To ascertain and demonstrate the effectiveness of the DA framework for JAD operation, in this paper, we initially investigated its impact on the motion and control performance of a single absorbing vehicle. Numerical results show that the DA framework effectively mitigated underestimated or overestimated control failures of JAD caused by misestimation of key parameters (e.g., free flow speed and critical density) of the traffic flow fundamental diagram. The findings suggest that the proposed DA framework can reduce control failures and prevent significant declines and deteriorations in JAD performance caused by changes in traffic characteristics, e.g., weather conditions or traffic composition.
Paper Structure (12 sections, 21 equations, 7 figures)

This paper contains 12 sections, 21 equations, 7 figures.

Table of Contents

  1. INTRODUCTION
  2. VIRTUAL TRUE TRAJECTORY GENERATION
  3. A synthetic single-lane freeway section with a sag
  4. Car-following model for generating true trajectories
  5. DA FRAMEWORK AND JAD MODEL
  6. EKF-CTM for traffic state estimation
  7. Prediction of absorbing end point
  8. JAD
  9. NUMERICAL EXPERIMENTS
  10. Experiment setup
  11. Results
  12. CONCLUSIONS

Figures (7)

  • Figure 1: Three cases of JAD: (a) Proper control, (b) Underestimated control, and (c) Overestimated control. Point A denotes an absorbing end point. The white parts imply vacant time space (i.e., no traffic). The purple lines are the trajectories of the absorbing vehicle (AbV).
  • Figure 2: A hypothetical freeway section with a sag.
  • Figure 3: Integrated system framework for JAD with DA.
  • Figure 4: Traffic demand over time.
  • Figure 5: Time-space diagrams (loop detectors are not displayed except for in (a)-i for clarity).
  • ...and 2 more figures