Estimating Acceleration and Lane-Changing Dynamics Based on NGSIM Trajectory Data

TL;DR

This paper introduces a smoothing algorithm for NGSIM trajectory data to accurately estimate vehicle acceleration and lane-changing dynamics, enabling improved analysis of traffic flow and safety metrics.

Contribution

A novel smoothing method for trajectory data that enhances velocity and acceleration estimation, and a quantitative criterion for lane-change duration based on trajectory density.

Findings

Improved estimation of vehicle acceleration from noisy positional data.

Accurate calculation of time gaps and times-to-collision distributions.

Identification of velocity advantages and anticipatory behaviors during lane changes.

Abstract

The NGSIM trajectory data sets provide longitudinal and lateral positional information for all vehicles in certain spatiotemporal regions. Velocity and acceleration information cannot be extracted directly since the noise in the NGSIM positional information is greatly increased by the necessary numerical differentiations. We propose a smoothing algorithm for positions, velocities and accelerations that can also be applied near the boundaries. The smoothing time interval is estimated based on velocity time series and the variance of the processed acceleration time series. The velocity information obtained in this way is then applied to calculate the density function of the two-dimensional distribution of velocity and inverse distance, and the density of the distribution corresponding to the ``microscopic'' fundamental diagram. Furthermore, it is used to calculate the distributions of time gaps and times-to-collision, conditioned to several ranges of velocities and velocity differences. By simulating virtual stationary detectors we show that the probability for critical values of the times-to-collision is greatly underestimated when estimated from single-vehicle data of stationary detectors. Finally, we investigate the lane-changing process and formulate a quantitative criterion for the duration of lane changes that is based on the trajectory density in normalized coordinates. Remarkably, there is a very noisy but significant velocity advantage in favor of the targeted lane that decreases immediately before the change due to anticipatory accelerations.