Selecting the most relevant variables towards clustering bus priority corridors
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Selecting the most relevant variables towards clustering bus priority corridors Miriam Rocha1,2 · Cristina Albuquerque Moreira Silva3 · Reinaldo Germano Santos Junior3 · Michel Anzanello1 · Gabrielli Harumi Yamashita1 · Luis Antonio Lindau3 Accepted: 1 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This paper proposes a novel framework to identify the most informative variables for clustering bus priority corridors according to their similarities regarding system and operational aspects. Although each bus corridor has its peculiarities, understanding the similarities (e.g., system, physical and operational aspects) between corridors of different regions of the world can help researchers and transit specialists to draw up strategies tailored to improving the traffic in the cities. For that matter, we integrate a novel metric for measuring clustering quality to the omit-one-variable-out-at-a-time selection procedure. The proposed method relies on three steps: (i) collect and preprocess data describing bus corridors; (ii) define the number of clusters to be generated based on a hierarchical approach; and (iii) iteratively group bus corridors, and eliminate less relevant clustering variables. When applied to a dataset comprised of 296 bus priority corridors from 45 countries and described by 44 variables, the proposed framework retained only four variables (i.e., brand and/or logo, station spacing, enhanced stations, and operating speed) responsible for the best stratification of corridors. Four clusters were formed and qualitatively assessed regarding their similarities in terms of system, physical and operational aspects. Corridors were grouped into basic corridors (cluster 1), improved corridors (cluster 2), Bus Rapid Transit (BRT) and Bus with High Level of Service (BHLS) systems (cluster 3), and express, limited-stop services (cluster 4). Keywords Bus priority corridors · Variable selection · Clustering analysis · BRT · BHLS
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s1246 9-020-00245-x) contains supplementary material, which is available to authorized users. * Miriam Rocha [email protected] Extended author information available on the last page of the article
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1 Introduction The current growth of urban areas and motorization rates has challenged researchers and practitioners towards the development of new approaches for describing and monitoring urban transportation systems. United Nations (2019) pointed out that nearly 55% of the world population used to live in urban areas in 2018; in 2050, that rate is expected to reach 68%. Additionally, an elevation in the number of cars is also expected due to population’s increasing income (Kharas 2010): the current fleet of 1 billion vehicles will probably reach 2 billion in 2050 (OECD/ ITF 2012). An efficient alternative to reduce the negative effects emerging from traffic congestion consists of prioritizing high-occupancy veh
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