On a clustering-based approach for traffic sub-area division

Document Type

Conference Proceeding

Publication Date

1-1-2019

Abstract

Traffic sub-area division is an important problem in traffic management and control. This paper proposes a clustering-based approach to this problem that takes into account both temporal and spatial information of vehicle trajectories. Considering different orders of magnitude in time and space, we employ a z-score scheme for uniformity and design an improved density peak clustering method based on a new density definition and similarity measure to extract hot regions. We design a distribution-based partitioning method that employs k-means algorithm to split hot regions into a set of traffic sub-areas. For performance evaluation, we develop a traffic sub-area division criterium based on the SDbw indicator and the classical Davies-Bouldin index in the literature. Experimental results illustrate that the proposed approach improves traffic sub-area division quality over existing methods.

Identifier

85068616154 (Scopus)

ISBN

[9783030229986]

Publication Title

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

External Full Text Location

https://doi.org/10.1007/978-3-030-22999-3_45

e-ISSN

16113349

ISSN

03029743

First Page

516

Last Page

529

Volume

11606 LNAI

Grant

SGSCXT00XGJS1800219

Fund Ref

Sichuan Province Science and Technology Support Program

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