Abstract

The urban bus service system is one of the most important components of a public transport system. Thus, exploring the spatial configuration of the urban bus service system promotes an understanding of the quality of bus services. Such an understanding is of great importance to urban transport planning and policy making. In this study, we investigated the spatial characteristics of an urban bus service system by using the complex network approach. First, a three-step workflow was developed to collect a bus operating dataset from a public website. Then, we utilized the P-space method to represent the bus service network by connecting all bus stop pairs along each bus line. With the constructed bus network, a set of network analysis indicators were calculated to quantify the role of nodes in the network. A case study of Shenzhen, China was implemented to understand the statistical properties and spatial characteristics of the urban bus network configuration. The empirical findings can provide insights into the statistical laws and distinct convenient areas in a bus service network, and consequently aid in optimizing the allocation of bus stops and routes.

Highlights

  • A city can be considered as a place where people are more densely distributed, with developed industrial and commercial activities

  • On the basis of the method of network construction, the degree of a traffic analysis zones (TAZs) indicates the number of directly connected TAZs, that is, at least one bus line runs between the TAZ and others

  • For the cumulative degree distribution (Figure 4b), the distribution appears as an approximate process of linear decline, and the decay process can be well fitted by an exponential function, which is consistent with previous studies [28,32,47]

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Summary

Introduction

A city can be considered as a place where people are more densely distributed, with developed industrial and commercial activities. The present study aimed to investigate the spatial characteristics of bus networks to determine nodes with dominant or low accessibility and connections in urban space, the spatial interaction structure generated by bus lines, and the relationship between the bus and road networks Understanding these characteristics would provide a deep spatial insight of bus networks, which is meaningful to urban transportation and planners to optimize the original network such as by changing the route of some bus lines to improve the accessibility of areas with few buses passing through. The results of this study can be referenced by other cities to improve the efficiency of their bus services

Dataset Collection
Network Construction
Nodes’ Centrality Measurement of the Weighted Bus Network Structure
Degree Distribution
Findings
Small-World Property
Full Text
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