Curvedness feature constrained map matching for low-frequency probe vehicle data

Abstract

Map matching method is a fundamental preprocessing technique for massive probe vehicle data. Various transportation applications need map matching methods to provide highly accurate and stable results. However, most current map matching approaches employ elementary geometric or topological measures, which may not be sufficient to encode the characteristic of realistic driving paths, leading to inefficiency and inaccuracy, especially in complex road networks. To address these issues, this article presents a novel map matching method, based on the measure of curvedness of Global Positioning System (GPS) trajectories. The curvature integral, which measures the curvedness feature of GPS trajectories, is considered to be one of the major matching characteristics that constrain pairwise matching between the two adjacent GPS track points. In this article, we propose the definition of the curvature integral in the context of map matching, and develop a novel accurate map matching algorithm based on the curvedness feature. Using real-world probe vehicles data, we show that the curvedness feature (CURF) constrained map matching method outperforms two classical methods for accuracy and stability under complicated road environments.

Uncertainty-Based Map Matching: The Space-Time Prism and k-Shortest Path Algorithm

“…Other works worth being mentioned are the ones by Wang et al [25], Morikawa et al [26], Rahmani et al [27], Hunter et al [28] and Zeng et al [29]. For the sake of space, we do not further comment on them.…”

UAV attitude estimation based on the dual filtering methods

“…Therefore, it is the research hotspot to make use of vehicle running characteristics in map matching, for example, those literatures proposed a novel method to solve map matching (Liu et al, 2017;Rohani et al, 2016;Zeng et al, 2016). Besides, hidden Markov model (HMM) and heuristic algorithms are some competitive algorithms in those methods, for example, the literature (Mohamed et al, 2017) presents a new method named SnapNet, which provides accurate real-time map matching for a cellular-based trajectory trace and employs a novel incremental HMM algorithm to solve the problem.…”

Longitudinal error improvement by visual odometry trajectory trail and road segment matching

“…With the value of the factor in (17) and (18), these are multiplied with the current particle weight w k − 1 (i)…”

“…Zeng et al in [17] also presented a curve matching method whereby they performed curve comparison from GPS data with map roads. However, since GPS data typically contains noiseunless a high precision GPS device is used -it needs to be filtered to obtain a functional curve for comparison.…”