Network-Based Continuous Space Representation for Describing Pedestrian Movement in High Resolution
DOI:
https://doi.org/10.17815/CD.2020.107Keywords:
road network, offset, continuous space, general state space model, map matchingAbstract
A concept of network-based continuous space representation is proposed and applied to the sequential map matching problem with simulation data assuming pedestrian movement. The concept allows for dealing with situations that the resolution of network representation is not high enough to describe the pedestrian movement considering the observation accuracy. The experiment showed that the proposed concept worked well in the example of pedestrian movement along with the sidewalk by estimation of accurate positions.References
M. A. Quddus, W. Y. Ochieng, and R. B. Noland, “Current map-matching algorithms for transport
applications: State-of-the art and future research directions,” Transp. Res. Part C, Vol.15, no.5,
pp.312-328, 2007.
W. Kim, G. Jee and J. Lee, “Efficient use of digital road map in various positioning for ITS,” Proc. of
IEEE Symposium on Position Location and Navigation, 2000.
T. Hunter, R. Herring, P. Abbeel and A. Bayen, “Path and travel time inference from GPS probe
vehicle data,” Proc. of the Neural Information Processing Systems Foundation (NIPS), 2009.
N. Gordon, D. Salmond, and A. Smith, “Novel approach to nonlinear / non-Gaussian Bayesian state
estimation,” Radar and Signal Processing, IEE Proc. F, Vol.140, no.2, pp.107-113, 1993.
G. Kitagawa, “Monte Carlo filter and smoother for non-Gaussian nonlinear state space models,”
Journal of Computational and Graphical Statistics, Vol.5, no.1, pp.1-25, 1996.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Wataru Nakanishi, Takashi Fuse
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to Collective Dynamics agree to publish their articles under the Creative Commons Attribution 4.0 license.
This license allows:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms.
Authors retain copyright of their work. They are permitted and encouraged to post items submitted to Collective Dynamics on personal or institutional websites and repositories, prior to and after publication (while providing the bibliographic details of that publication).