Fuzzifying Geospatial Data to Identify Critical Traffic Areas

DOI

10.2991/asum.k.210827.061How to use a DOI?

Keywords

Type-2 fuzzy sets, traffic analysis, spatio-temporal data, smart logistics, geohash

Abstract

This manuscript proposes a framework to design an artifact that combines traffic data of different sources, addresses their low-penetration rate and imprecision, and enables their analysis. The implemented artifact uses probe data of en-route operations of delivery vehicles and Traffic Message Channel-based records. Both datasets are fuzzified and a type-2 fuzzy logic system is then implemented, to determine the traffic criticality of geographical zones. The output of the system is displayed on a map to serve as an analysis tool. With the practical implementation, it is shown that such insights can be obtained, without large amounts of precise information. However, comprehensive evaluation methods are to be developed to verify the validity of the results.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jhonny Pincay
AU  - Edy Portmann
AU  - Luis Terán
PY  - 2021
DA  - 2021/08/30
TI  - Fuzzifying Geospatial Data to Identify Critical Traffic Areas
BT  - Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP)
PB  - Atlantis Press
SP  - 463
EP  - 470
SN  - 2589-6644
UR  - https://doi.org/10.2991/asum.k.210827.061
DO  - 10.2991/asum.k.210827.061
ID  - Pincay2021
ER  -