A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

Abstract

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions-particularly in urban contexts-due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss-and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation.

A Mesoscopic Traffic Data Assimilation Framework for Vehicle Density Estimation on Urban Traffic Networks Based on Particle Filters

“…It has been proven that the variable dimensions of both the system state and the discrete event state trajectory have no tangible effect on the updating of particles and their weights in particle filters by previous studies [24,30,33]. Therefore, we can safely apply the particle filter to estimate vehicle densities in our study.…”

“…Then, a match procedure [24] is employed to define missed detections and false detections based on the measurement…”

“…While most data assimilation algorithms are limited by the assumptions of either linear/continuously differentiable models (e.g., KF, EKF) or Gaussian errors (e.g., EnKF), the particle filter (PF) imposes no restriction on the dynamics and the errors of the models and can converge to the true state distribution. It is superior for the estimation of nonlinear and non-Gaussian system theoretically [22] and has been employed in many related research works [23,24]. Xie et al [24] develop a generic particle filter-based data assimilation framework for reconstructing vehicle trajectories on signalized urban arterials, in which noisy vehicle passages and sparse travel time observations are assimilated into a microscopic traffic simulation model.…”

“…It is superior for the estimation of nonlinear and non-Gaussian system theoretically [22] and has been employed in many related research works [23,24]. Xie et al [24] develop a generic particle filter-based data assimilation framework for reconstructing vehicle trajectories on signalized urban arterials, in which noisy vehicle passages and sparse travel time observations are assimilated into a microscopic traffic simulation model.…”

“…Resampling step: we resample the newly generated particles by replicating particles in proportion to their weights (lines [18][19][20][21][22][23][24][25][26][27].…”