Nonstationary Kalman Filter for Estimation of Accurate and Consistent Car-Following Data

Abstract

Difficulty in obtaining accurate car-following data has traditionally been regarded as a considerable drawback in understanding real phenomena and has affected the development and validation of traffic microsimulation models. Recent advancements in digital technology have opened up new horizons in the conduct of research in this field. Despite the high degrees of precision of these techniques, estimation of time series data of speeds and accelerations from positions with the required accuracy is still a demanding task. The core of the problem is filtering the noisy trajectory data for each vehicle without altering platoon data consistency; i.e., the speeds and accelerations of following vehicles must be estimated so that the resulting intervehicle spacings are equal to the real one. Otherwise, negative spacings can also easily occur. The task was achieved in this study by considering vehicles of a platoon as a sole dynamic system and reducing several estimation problems to a single consistent one. This process was accomplished by means of a nonstationary Kalman filter that used measurements and time-varying error information from differential Global Positioning System devices. The Kalman filter was fruitfully applied here to estimation of the speed of the whole platoon by including intervehicle spacings as additional measurements (assumed to be reference measurements). The closed solution of an optimization problem that ensures strict observation of the true intervehicle spacings concludes the estimation process. The stationary counterpart of the devised filter is suitable for application to position data, regardless of the data collection technique used, e.g., video cameras.

Characterizing Heterogeneity in Drivers’ Merging Maneuvers Using Two-Step Cluster Analysis

“…Thus, it is easy for us to collect the trajectories of 399 merging vehicles. However, the previous study [31,44] shows that the original trajectory data seem to contain some noise and errors. The velocities and acceleration provided by the NGSIM cannot be directly used.…”

“…In CORSIM, ten different driver types can be defined with variable gap acceptance values [28] and each gap acceptance decision is independent considering the current available gap and the personal gap acceptance value. In gap acceptance models using critical gaps, the most basic assumption is that a driver will accept the adjacent gap only if both the lead and lag gaps are larger than the critical gap [31] However, this assumption is often criticized as it is often inconsistent with the real world observations that vehicles still take lane changes when only the lead or lag gap or even none of them are larger than the critical gap [2,32,33]. To overcome this deficiency, a binary logit model was built by Kita (1993).…”

Situation assessment and decision making for lane change assistance using ensemble learning methods

“…Data smoothing techniques such as moving average (Ossen & Hoogendoorn, 2008), Kalman filtering (Punzo, Formisano, & Torrieri, 2005) and Kalman smoothing (Ma & Andreasson, 2007) have been used to improve speed data quality. In this study, the moving average method was adopted to smooth the speed measurements.…”

Simulation Optimization of Car-Following Models Using Flexible Techniques

“…A series of data-collection experiments were carried out on roads surrounding the city of Naples, in Italy [62]. All data were collected under real traffic conditions in In this research, data used are readily available observations from the field.…”

“…Six data series were used, one for calibration and five for validation. A detailed description of the data could be found in [62], who kindly provided the data for this research.…”