A Time-varying Shockwave Speed Model for Trajectory Reconstruction using Lagrangian and Eulerian Observations

TL;DR

The paper tackles the challenge of reconstructing detailed vehicle trajectories under mixed traffic by combining Eulerian loop-detector data with Lagrangian connected-vehicle data. It introduces a calibration algorithm for time-varying shockwave speeds within a shockwave-theory framework, enabling more accurate trajectory reconstruction for non-connected vehicles, and adds a microsimulation-based dynamics model (MFC) to improve energy consumption estimation. Key contributions include (1) a Monte Carlo-based calibration of $w_j^{\tau_k}$, (2) reference-point reconstruction for NCVs anchored to calibrated CVs, (3) energy-aware trajectory reconstruction via MFC, and (4) evaluation on HighD/NGSIM showing improved trajectory accuracy, oscillation reproduction, and energy estimates, especially at low CV penetration. The work advances practical trajectory reconstruction with data-efficient calibration and provides insights into CV contributions through the ST area metric, with potential extensions to signals/intersections and deep-learning integrations for CV-free calibration. The approach offers meaningful implications for traffic management and energy optimization in mixed-traffic environments.

Abstract

Inference of detailed vehicle trajectories is crucial for applications such as traffic flow modeling, energy consumption estimation, and traffic flow optimization. Static sensors can provide only aggregated information, posing challenges in reconstructing individual vehicle trajectories. Shockwave theory is used to reproduce oscillations that occur between sensors. However, as the emerging of connected vehicles grows, probe data offers significant opportunities for more precise trajectory reconstruction. Existing methods rely on Eulerian observations (e.g., data from static sensors) and Lagrangian observations (e.g., data from probe vehicles) incorporating shockwave theory and car-following modeling. Despite these advancements, a prevalent issue lies in the static assignment of shockwave speed, which may not be able to reflect the traffic oscillations in a short time period caused by varying response times and vehicle dynamics. Moreover, energy consumption estimation is largely ignored. In response, this paper proposes a novel framework that integrates Eulerian and Lagrangian observations for trajectory reconstruction. The approach introduces a calibration algorithm for time-varying shockwave speed. The calibrated shockwave speed of the CV is then utilized for trajectory reconstruction of other non-connected vehicles based on shockwave theory. Additionaly, vehicle and driver dynamics are introduced to optimize the trajectory and estimate energy consumption. The proposed method is evaluated using real-world datasets, demonstrating superior performance in terms of trajectory accuracy, reproducing traffic oscillations, and estimating energy consumption.

Figures (11)

• Figure 1: Illustration of the loop detector's information and connected vehicles' information.
• Figure 2: Trajectory reconstruction based on shockwave theory.
• Figure 3: Process of shockwave speed calibration.
• Figure 4: Adjust the speed collected by the loop detector.
• Figure 5: Trajectory reconstruction of other non-connected vehicles.