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Identification and classification of activity centers based on passenger flows data.
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In the past decades, the spatial structure of metropolitan areas has progressively changed

towards a more polycentric structure. Many researchers have studied this polycentric structure

in the context of North American and European metropolitans by identifying sub-centers,

mainly using two methods which are analyzing the employment or population density or

mobility data. In spite of huge effort in identifying sub-centers, fewer studies characterize the

identified sub-centers and classify them based on their patterns and features simultaneously.

And this research will identify sub-centers and then classify them as well.

Following the introduction of polycentricity and a review of previous methodologies for

identification and classification of sub-centers, this study introduces two different algorithms:

flow-based and distance-based for identifying sub-centers based on passenger flows data at

public transport stations. In addition, the study presents the classification process of identified

clusters based on time-dependent passenger flows data. Temporal profiles of each cluster are

created and used to describe their characteristics, and then classification is conducted based on

hierarchical clustering analysis.

As a case study, the emergence of polycentric structure in Stockholm County is analyzed using

public transport passenger flows at each station including metros, commuter trains, buses and

light rails. After comparing results of the two proposed algorithms, the distance-based is chosen

for Stockholm case. The identification algorithm yields 17 clusters. These 17 clusters are then

classified using three different indicators based on flow data by time intervals. As a result, we

have three classification results. Finally, the classification results are analyzed and synthesized

by considering the urban environment of clusters and their roles in transport network,

providing a comprehensive interpretation of resulted clusters. Clusters are classified into seven

more general classes of center, business, residential, hub or combinations of them. The result

suggests that each cluster is associated with distinctive functions and they are all active, unlike

‘’sleeping towns’’, however, clusters in the inner city are still able to generate and attract more

flows and flows are still more concentrated in the central part, indicating the aim to release

pressure from central part by polycentric structure hasn’t been fully achieved yet.

Place, publisher, year, edition, pages
2014. , 87 p.
Series
TSC-MT, 14-003
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-149467OAI: oai:DiVA.org:kth-149467DiVA: diva2:739745
Available from: 2014-08-21 Created: 2014-08-21

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