Hub-Periphery Hierarchy in Bus Transportation Networks: Gini Coefficients and the Seoul Bus System

Chansoo Kim,Keumsook Lee,Segun Goh,M. Y. Choi,Myeong Seon Choi

doi:10.3390/su12187297

Abstract

Bus transportation networks are characteristically different from other mass transportation systems such as airline or subway networks, and thus the usual approach may not work properly. In this paper, to analyze the bus transportation network, we employ the Gini coefficient, which measures the disparity of weights of bus stops. Applied to the Seoul bus system specifically, the Gini coefficient allows us to classify nodes in the bus network into two distinct types: hub and peripheral nodes. We elucidate the structural properties of the two types in the years 2011 and 2013, and probe the evolution of each type over the two years. It is revealed that the hub type evolves according to the controlled growth process while the peripheral one, displaying a number of new constructions as well as sudden closings of bus stops, is not described by growth dynamics. The Gini coefficient thus provides a key mathematical criterion of decomposing the transportation network into a growing one and the other. It would also help policymakers to deal with the complexity of urban mobility and make more sustainable city planning.

Highlights

Complex networks have attracted much attention in various research areas
The growth process of the Seoul subway network was described by a master equation for a Yule-type model [18], and the passenger flow on the network was described by a gravity model modified by a Hill function [19]
Serving as a dominant transportation mode for short- to mid-distance trips, the bus transportation network (BTN) is important for policymakers to deal with the complexity of urban mobility and make more sustainable city planning, which should impact on both passengers and policymakers

Summary

Introduction

Complex networks have attracted much attention in various research areas. Since the seminal works on small-world networks [1] and scale-free networks [2], interest in spatial networks has grown significantly including various fields of application [3,4]. There has been much effort to assess the sustainability of transportation networks [5,6,7,8,9,10,11]. A better understanding of the network structure is required for improving those analyses [12,13,14,15]. There have appeared studies of mass transportation networks: For instance, the network structure and passenger flows of the Seoul subway system were analyzed [16,17]. In the case of the bus transportation network (BTN), scaling and renormalization ideas manifested the emergence of criticality in the passenger flow [21] while accessibility measurement was successfully performed [22]

Methods

Results

Conclusion