Identifying the Transit Needs of Socioeconomic Groups by Evaluating the Relationship between a Network's Supply and Demand: A Case Study of the City of Amsterdam

Identifying the Transit Needs of Socioeconomic Groups by Evaluating the Relationship between a Network's Supply and Demand: A Case Study of the City of Amsterdam

Drybrough, Ben (TU Delft Civil Engineering and Geosciences; TU Delft Transport and Planning)

Cats, Oded (graduation committee)
van Oort, Niels (graduation committee)
Kroesen, M. (graduation committee)
Kouwenberg, Machiel (graduation committee)

Delft University of Technology

Civil Engineering | Transport and Planning

2020-11-25

An inclusive and sustainable transport network allows people the opportunity to access their daily needs. When there is a discrepancy between someone’s transport needs and their supplied network, they are at the risk of experiencing transport poverty. This study looks to define the transport needs for clusters of neighbourhoods of different socioeconomic and demographic characteristics in Amsterdam, The Netherlands by evaluating the relationship between the network’s supply and demand. Neighbourhoods are grouped through their characteristics using a Latent Class Clustering Analysis into likewise clusters. Supply and demand indicators are designed for each neighbourhood using GTFS and smart card ridership data. The effect of a neighbourhood’s cluster on the relationship is analyzed through an interaction effects linear regression. It was found that improvements to the supply indicators had a positive relationship with the overall demand of the network. However, this is consistent for all defined clusters of Amsterdam which is contrary to the expectations of the analysis. It was assumed that the supply indicators, reflecting convenience and efficiency of the network, would be needed variably for different socioeconomic groups of Amsterdam. These findings provide valuable insights into the current needs of Amsterdam residents, the state of the public transit network, and how transport planners should focus future infrastructure improvements. Insights are also gathered on how transport poverty research may be improved through statistical clustering techniques and using data in order to measure the use of the network.

Transport poverty
Public transport
GTFS data
Smartcard data
Clustering techniques
Amsterdam

http://resolver.tudelft.nl/uuid:312f5643-3ea9-496a-a53c-be7779e26eb8

Student theses

master thesis

© 2020 Ben Drybrough