An iterative framework with active learning to match segments in road networks

ABSTRACT

Road network matching that detects arc-to-arc relations is a crucial prerequisite for the update of road data. The increasing complexity of multi-source and multi-scale road network data challenges the existing methods on accuracy and efficiency. This paper focuses on the interactive-based probabilistic relaxation approach. It is difficult to obtain satisfactory results by using completely automatic matching algorithm in some complicated road networks such as multi-lane carriageways. We try to improve the matching accuracy by combining optimization matching model with manual interaction. The method uses the module of active learning to construct unlabeled sample pool from preliminary matching of probabilistic relaxation, and then selects the arcs with the highest uncertainty by query function. The selected road is then handed over to humans to determine its arc-to-arc relations in the other road network. Finally, the matching parameters are automatically adjusted according to the user’s feedback information, so as to realize the dynamic optimization of the model. Our interaction method is efficient as it only needs to specify few arc-to-arc mappings and others can be amended automatically. Our experimental results reveal that active learning can substantially improve the performance of standard probabilistic relaxation algorithms in road network matching.

KEYWORDS:

• Data fusion
• probabilistic relaxation
• road matching
• active learning
• crowdsourcing data

Acknowledgments

The authors are grateful to the editor and the anonymous referees for their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data and codes that support the findings of this study are available in [figshare.com] with the identifier (https://figshare.com/s/875d6dc8b13edac154ba).

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [42071442]; Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) [CUG170640].