A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters. (July 2018)

• Record Type: Journal Article
• Authors: Xie, Xu; van Lint, Hans; Verbraeck, Alexander
• Is Part Of: Transportation research. Volume 92(2018)
• Page Start: 364
• Page End: 391
• Publication Date: 2018-07
• Subjects: Vehicle trajectory reconstruction -- Noisy sensor data -- Vehicle accumulation estimation -- Microscopic traffic simulation -- Data assimilation -- Particle filters; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2018.05.009
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Generic particle filter framework to estimate plausible vehicle trajectories. This framework uses vehicle passages, traffic control data, and travel time data. Any microscopic traffic flow model can be used as system model. We explicitly address counting & accumulation errors in all data sources. We estimate counts, departures and trajectories well within a 10% error. 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.