Bulletin of geography. Socio–economic series

W. Zhang, B. Derudder, J. Wang, F. Witlox / Bulletin of Geography. Socio-economic Series / 31 (2016): 145–160

147

tend to cover the supply of route structures between 

airports (e.g. the data from International Civil 

Aviation Organisation (ICAO) and International Air 

Transport Association (IATA)). This focus on the 

supply side of infrastructure networks does to some 

degree reflect demand for connectivity between 

city-pairs, especially in an increasingly deregulated 

air travel market, but there are of course major 

intervening effects. The most important one relates 

to the hub-and-spoke organization of global airline 

networks, where many passengers are routed via 

major airports to their destination. This overvaluing 

of ‘major hubs’ reveals that an analysis of supply 

of infrastructure provision does not directly match 

the actual demand or use. Neal (2014) has recently 

demonstrated the effect of this for urban network 

analysis, and prompted him to reveal the structural 

uniqueness of the networks of supply and demand.

Fig. 1. The high-speed railway network within the Yangtze River Delta

Source: Own studies

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W. Zhang, B. Derudder, J. Wang, F. Witlox  / Bulletin of Geography. Socio-economic Series / 31 (2016): 145–160

148

To date, however, few studies of urban networks 

have analyzed the parallels and differences be-

tween physical infrastructure networks and the ac-

tual flows they enable. In most cases, the former is 

used as a proxy for the conceptually more meaning-

ful latter. This implies that, in spite of a plethora of 

papers analysing urban networks through the lens 

of infrastructure networks, there remains scope for 

analytical improvement. There are some compara-

tive studies on different layers of urban networks 

that may inform our understanding of their spa-

tial outline (e.g. Taylor et al., 2007; Liu et al., 2012; 

Choi et al., 2006), but in this paper we focus more 

specifically on how data on infrastructure provision 

can be adapted so that it better reflects actual in-

ter-city flows. To this end, we focus on the example 

of rail networks reflecting urban network-forma-

tion at the level of ‘megaregions’ (cf. Harrison, Hoy-

ler, 2015). Previous research on this topic serves to 

clarify our research question. In a recent analysis of 

infrastructure networks in South Asia, Derudder et 

al. (2014) find that cities along transport corridors, 

often defined by road and train networks, are well 

connected. However, this may be an artifact of the 

network lay-out rather than ‘real connectivity’: 

the connectivity of cities located on a connection 

between two major interacting nodes may be vastly 

over-estimated. In the case of the Yangtze River 

Delta, which will be the empirical focus of this pa-

per, this would result in overestimating the connec-

tivity of Wuxi as it is on the Nanjing-Shanghai HSR 

line (which is officially called Huning Inter-city 

Line), granting the Wuxi-Nanjing and Wuxi-Shang-

hai links de facto equal status to the Shanghai-Nan-

jing connection (fig. 1). The purpose of this paper 

is to elaborate a method that would allow for an 

improved guesstimate of inter-city flows based 

on infrastructures. The paper focuses on urban 

networks at lower scales such those in mega-city-

regions and countries, where road and rail networks 

are the key facilitators of inter-city flows.

The remainder of this paper is organized as 

follows. First, we give a brief overview of the 

methods for measuring inter-city interactions 

in railway networks in previous research, and 

survey the deficiencies encountered by the 

proxies of infrastructure networks for actual in-

ter-city interactions in more detail. Following this 

discussion, we focus on setting out an alternative 

approach to approximating passenger flows in rail-

way networks. This is followed by an empirical test 

of this approach by applying it to the HSR network 

within the Yangtze River Delta (YRD) and exam-

ining the difference between our transformed net-

work and the original network. We then evaluate 

the validity of our method through a comparison 

with a benchmark dataset of actual flows of people, 

after which the paper is concluded with an overview 

of our main findings and a discussion of possible 

avenues for further research.

2.  Methods for measuring inter-city 

interactions through railway networks

Railways constitute one of the main means for trans-

porting people between cities, and thus play a ma-

jor role in the structuring of inter-city interactions, 

especially at the regional and national level. Within 

the burgeoning literature on inter-city networks 

and spatial interactions, many researchers have thus 

tried to measure inter-city linkages through the lens 

of railway networks (e.g. Luo et al., 2011; Hall et al., 

2006). However, few papers have mapped inter-city 

interactions using a direct measure of the volumes 

of inter-city passenger flows. This can be attribut-

ed to the lack of data on actual traffic volumes 

between train stations. As a consequence, a num-

ber of researchers have resorted to proxy strategies 

for measuring inter-city linkages. Two main solu-

tions have been devised in the context of railway 

networks: (1) measuring the potential for interac-

tions by train, and (2) measuring the volume of 

trains making inter-city connections.

2.1.  Interaction potential

Interaction potential can be defined as the conve-

nience and opportunity of inter-city travel through 

rail transport. The most commonly used indicator 

in this respect is travel time, which is often seen as 

an ‘unproductive’ cost (time) (Lyons et al., 2007) in 

a journey influencing potential inter-city interaction 

(see for example, Kramar, Kadi, 2013; Bruinsma, Ri-

etveld, 1993; Murayama, 1994). Similarly, travel dis-

tance or the generalized cost of transport (distance 

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