Details
| Original language | English |
|---|---|
| Pages (from-to) | 1012-1039 |
| Number of pages | 28 |
| Journal | International Journal of Geographical Information Science |
| Volume | 30 |
| Issue number | 5 |
| Publication status | Published - 24 Sept 2015 |
Abstract
This work aims at the inference of traffic networks from GPS trajectories. We perform geometry and topology reconstruction of the network in a multistep process. Our main contributions are the formulation of an explicit intersection model with a score function that accounts for consistency with the raw tracking data, as well as for a topology prior and the search for the best model by maximization of this score function using a Markov chain Monte Carlo sampler. We demonstrate the viability of our model-based approach with experiments on GPS data sets of varying size and data quality, followed by a comparison with results achieved by alternative, heuristic approaches.
Keywords
- algorithms, generative modeling, Map construction, tracking data
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
- Social Sciences(all)
- Library and Information Sciences
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In: International Journal of Geographical Information Science, Vol. 30, No. 5, 24.09.2015, p. 1012-1039.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Generative models for road network reconstruction
AU - Kuntzsch, Colin
AU - Sester, Monika
AU - Brenner, Claus
PY - 2015/9/24
Y1 - 2015/9/24
N2 - This work aims at the inference of traffic networks from GPS trajectories. We perform geometry and topology reconstruction of the network in a multistep process. Our main contributions are the formulation of an explicit intersection model with a score function that accounts for consistency with the raw tracking data, as well as for a topology prior and the search for the best model by maximization of this score function using a Markov chain Monte Carlo sampler. We demonstrate the viability of our model-based approach with experiments on GPS data sets of varying size and data quality, followed by a comparison with results achieved by alternative, heuristic approaches.
AB - This work aims at the inference of traffic networks from GPS trajectories. We perform geometry and topology reconstruction of the network in a multistep process. Our main contributions are the formulation of an explicit intersection model with a score function that accounts for consistency with the raw tracking data, as well as for a topology prior and the search for the best model by maximization of this score function using a Markov chain Monte Carlo sampler. We demonstrate the viability of our model-based approach with experiments on GPS data sets of varying size and data quality, followed by a comparison with results achieved by alternative, heuristic approaches.
KW - algorithms
KW - generative modeling
KW - Map construction
KW - tracking data
UR - http://www.scopus.com/inward/record.url?scp=84961204989&partnerID=8YFLogxK
U2 - 10.1080/13658816.2015.1092151
DO - 10.1080/13658816.2015.1092151
M3 - Article
AN - SCOPUS:84961204989
VL - 30
SP - 1012
EP - 1039
JO - International Journal of Geographical Information Science
JF - International Journal of Geographical Information Science
SN - 1365-8816
IS - 5
ER -