Abstracts Engineering

Add abstract

Want to add your dissertation abstract to this database? It only takes a minute!

Search abstract

Search for abstracts by subject, author or institution

Share this abstract

Decision-support tool for identifying locations of shared mobility hubs : A case study in Amsterdam

by Pietro Podestà

Institution: KTH
Department: Transport planning
Degree:
Year: 2022
Keywords: Shared-mobility hubs; GIS; cluster analysis; stated preference; discrete-choice models; hub usage prediction; Engineering and Technology; Teknik och teknologier
Posted: 3/25/2025
Record ID: 2269018
Full text PDF: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-320839


Abstract

Shared mobility is considered a more sustainable alternative to private modes. Nonetheless, its sudden and sometimes “out of control” emergence poses issues that need to be addressed. Lack of regulations and public space mismanagement cause sidewalks and city roads to be overcrowded with shared vehicles (especially in the case of micromobility). This causes nuisance and safety concerns and hinders the societal benefits shared mobility may provide. Shared mobility hubs have the potential to address these issues. The research was carried out within the context of the SmartHubs project, an EIT Urban Mobility project initiated in 2021 by a diverse consortium of 7 cities, companies, and universities to develop and validate effective and economically viable mobility hub solutions. This degree project aims to improve the Decision-Support-Tool (DST) developed by SmartHubs to identify locations of shared-mobility hubs having high potential in driving sustainable travel usage. To achieve that, the thesis proposes a methodology for determining smart hub locations and their corresponding utilities based on the combination of GIS cluster analysis of free-floating shared mobility parking patterns and a stated-preference study. The potential hub locations were determined from the cluster analysis of free-floating trip characteristics. Using the stated preference survey data, the thesis develops a model to estimate the probability of parking at the hub as a function of explanatory variables, including walking distance, reward policies and the parking situation. The model testing results showed that the proposed methodology can well predict the hub (usage) demand and improve the current DST originally developed in the SmartHubs project.

Add abstract

Want to add your dissertation abstract to this database? It only takes a minute!

Search abstract

Search for abstracts by subject, author or institution

Share this abstract

Relevant publications

Book cover thumbnail image
Predicting the Admission Decision of a Participant...
by Yigit Ozsert, Gozde
   
Book cover thumbnail image
Development of New Models Using Machine Learning M...
by Akgol, Derman
   
Book cover thumbnail image
The Adaptation Process of a Resettled Community to... A Study of the Nubian Experience in Egypt
by Fahmi, Wael Salah
   
Book cover thumbnail image
Development of an Artificial Intelligence System f...
by Chand, Praneel
   
Book cover thumbnail image
Theoretical and Experimental Analysis of Dissipati...
by Latour, Massimo
   
Book cover thumbnail image
Optical Fiber Sensors for Residential Environments
by García-Olcina, Raimundo
   
Book cover thumbnail image
Calibration of Deterministic Parameters Reassessment of Offshore Platforms in the Arabian ...
by Zaghloul, Hassan
   
Book cover thumbnail image
How Passion Relates to Performance A Study of Consultant Civil Engineers
by Cadieux, Trevor J.