Identification and classification of public transport activity centres in Stockholm using passenger flows data

Highlights

• A methodology for identifying urban clusters based on public transport flows

• A methodology for classifying urban clusters based on temporal mobility profiles

• Unravelling the urban structure dynamics of metropolitan Stockholm

• Stockholm has not yet transformed into a polycentric or multi-centric structure.

Abstract

Urban geography could be characterized by analysing the patterns that describe the flows of people and goods. Measuring urban structures is essential for supporting an evidence-based spatial planning policy. The objective of this study is to examine how the spatial–temporal distribution of public transport passenger flow could be used to reveal urban structure dynamics. A methodology to identify and classify centres based on mobility data was applied to Metropolitan Stockholm in Sweden using multi-modal public transport passenger flows. Stockholm is known for its long-term monocentric planning with a dominant central core and radial public transport system. Strategic nodes along its radial public transport system have been a focus for development of sub-centres. Although the regional planning policy embraces a shift towards a polycentric planning policy, the results indicate that this has not been realized insofar.

Introduction

Many urban regions worldwide tend to evolve from monocentric to a more complex distribution of activities. At the inter-urban scale, a polycentric policy was adopted by the European Union to support a balanced territorial development (Walsh, 2012). At the intra-urban scale this pattern is driven by urban sprawl, suburbanization, the emergence of specialized employment clusters, shopping centres or other big visitor attractors located on the urban fringe or the combination of several interlining trends. In some cases, such developments may amount to the emergence of a polycentric or multi-centric urban structure (Davoudi, 2003, Baum-Snow, 2010). Whilst these multi-centric concepts remain contested in the urban geography literature, they are essentially defined by the plurality of urban centres, often driven by the decentralization of people, jobs, and services from the core area to sub-centres. The emergence and the spatial distribution of urban centres are facilitated by the underlying transport network. This paper proposes a methodology to identify and classify activity centres based on transport flow data. The spatial and temporal mobility patterns facilitate the analysis of the centres and their relation to the urban activities and transport networks.

Anas et al. (1998) classified centres into two types — old towns incorporating an expanded urban area and newly spawned centres located at nodes of a transportation network. The growth of the second group is seen as the most popular pattern in changing the urban landscape. There is an extensive literature on the impact of urban forms and land-use distribution on travel patterns, such as the influence of urban agglomeration (Garcia-Palomares, 2010) and polycentric urban structure (Schwanen et al., 2001, Casello, 2007) on modal split and commuting patterns. Based on a spatial economics model, Louf and Barthelemy (2013) showed that the monocentric regime becomes unstable as the population grows and that the number of sub-centres grows sub-linearly with population size. External economics of scale underlie the emergence of urban clusters as the spatial concentration of specialized economic activities could foster innovation, spill-over effects and agglomeration benefits. In contrast to these studies, Shearmur and Coffey (2002) questioned the attempts to generalize spatial distribution trends and to imply that urban areas converge to a common spatial development trajectory.

Measuring urban structures and the underlying activity patterns is essential for supporting an evidence-based spatial planning policy. The identification of activity centres and their clustering and characterization will provide planners and policy makers with a better understanding of the existing metropolitan structure and enable them to assess how well it corresponds or diverts from planning policies. As pointed out by Meijers (2008) in the inter-urban context, there is an empirical deficit in the context of spatial planning development that should be addressed by applying a more analytical approach. Previous studies stressed the difficulty of obtaining flow data for analysing urban structure and using transport network attributes or topology indicators as proxies (e.g. EPSON, 2004, Rodrigues da Silva et al., 2014). However, the growing availability of ‘big data’ in the transport sector and in particular travel flow data facilitates the spatial and temporal analysis of urban activity.

This paper presents a methodology which consists of two stages: (1) identifying and (2) classifying activity centres based on mobility data. The objective of this study is to examine how the spatial–temporal distribution of public transport passenger flow could be used to reveal the urban structure dynamics in the case of Stockholm, Sweden. The term public transport activity centres (PTACs) is used rather than urban centre or sub-centre because intermediate interchange hubs cannot be distinguished from genuine demand generators and attractors. PTACs are identified by clustering transport nodes according to their spatial proximity and travel flows. The method proposed in this paper could be applied in different spatial contexts and no particular structure – monocentric or polycentric – is assumed from the outset. A sensitivity analysis is performed to investigate the implications of different parameter values on the number of centres, their spatial distribution and the distribution of passenger flows. PTACs are then classified based on their time-dependent flow profile including magnitude, directness and the distribution of incoming and outgoing flows. It is postulated that urban structure and the spatial distribution of activities are manifested through time-dependent flow profile because activity centres with distinguished functions will yield distinctive travel patterns throughout the day.

The remainder of this paper is organized as follows. The following section reviews the literature on measuring urban structure based on urban geography and economics, spatial analysis and transport flow applications. We then propose a methodology for identifying and classifying PTACs based on time-dependent transport flows. Section 4 presents the context of our case study area – Stockholm Metropolitan Area – and the data available for this analysis. The results are presented and discussed in the context of Stockholm regional and transport development in Section 5. The paper concludes with a discussion on our findings in the context of the urban geography of Stockholm and study limitations.