TY - JOUR

T1 - Estimating road segments using natural point correspondences of GPS trajectories

AU - Leichter, Artem

AU - Werner, Martin

N1 - Funding information: The publication of this article was funded by the Open Access fund of Leibniz Universität Hannover. This work was partially funded by the Federal Ministry of Education and Research, Germany (Bundesministerium für Bildung und Forschung, Förderkennzeichen 01IS17076). We gratefully acknowledge this support.

PY - 2019/10/11

Y1 - 2019/10/11

N2 - This work proposes a fast and straightforward method, called natural point correspondences (NaPoCo), for the extraction of road segment shapes from trajectories of vehicles. The algorithm can be expressed with 20 lines of code in Python and can be used as a baseline for further extensions or as a heuristic initialization for more complex algorithms. In this paper, we evaluate the performance of the proposed method. We show that (1) the order of the points in a trajectory can be used to cluster points among the trajectories for road segment shape extraction and (2) that preprocessing using polygonal approximation improves the results of the approach. Furthermore, we show based on "averaging GPS segments" competition results, that the algorithm despite its simplicity and low computational complexity achieves state-of-the-art performance on the challenge dataset, which is composed of data from several cities and countries.

AB - This work proposes a fast and straightforward method, called natural point correspondences (NaPoCo), for the extraction of road segment shapes from trajectories of vehicles. The algorithm can be expressed with 20 lines of code in Python and can be used as a baseline for further extensions or as a heuristic initialization for more complex algorithms. In this paper, we evaluate the performance of the proposed method. We show that (1) the order of the points in a trajectory can be used to cluster points among the trajectories for road segment shape extraction and (2) that preprocessing using polygonal approximation improves the results of the approach. Furthermore, we show based on "averaging GPS segments" competition results, that the algorithm despite its simplicity and low computational complexity achieves state-of-the-art performance on the challenge dataset, which is composed of data from several cities and countries.

KW - Averaging

KW - GPS

KW - Road network

KW - Segments

KW - Trajectory

UR - http://www.scopus.com/inward/record.url?scp=85074212626&partnerID=8YFLogxK

U2 - 10.15488/9273

DO - 10.15488/9273

M3 - Article

AN - SCOPUS:85074212626

VL - 9

JO - Applied Sciences (Switzerland)

JF - Applied Sciences (Switzerland)

SN - 2076-3417

IS - 20

M1 - 4255

ER -