A network analysis method for optimized location planning of shared mobility hubs

A network analysis method for optimized location planning of shared mobility hubs

Kistenfeger, Moritz (TU Delft Architecture and the Built Environment)

Correia, Gonçalo (mentor)
van Lammeren, Ron (mentor)

Delft University of Technology
Wageningen University & Research

Metropolitan Analysis, Design and Engineering (MADE)

2022-07-28

To tackle challenges such as climate change, air pollution, traffic accidents, or the lack of space in cities, our urban transportation systems must become sustainable, emission-free, safer, and more efficient. The introduction of shared mobility is seen as a critical component in facilitating a mobility transition in large cities, as shared mobility can promote multimodal travel behavior, leading to reduced ownership and usage of the private car.
Shared mobility requires charging solutions and parking space within the existing urban fabric. Additionally, it must have digital and physical integration into existing transportation systems. These requirements should be jointly addressed within the concept of shared mobility hubs. Recently, cities have moved from pilot testing standalone hubs to the scaling of city-wide hub networks. Planning the locations of these hubs remains a challenge for cities trying to optimize their distribution.
This research suggests an improved location planning method for shared mobility hubs, combining Multiple Criteria Decision Analysis (MCDA) and Network Analysis. Different prioritizations at the municipal decision-making level can be translated into placement strategies through MCDA. If necessary, multiple stakeholders can also be involved through a Multi-actor Multi-criteria Analysis (MAMCA). The resulting MCDA score for each spatial unit converts a multivariate problem into a single variable location-optimization problem. Utilizing single variable location-optimization tools, such as ArcGIS location allocation, specific location suggestions can be computed along with their respective catchment areas. This also allows a comparison of different placement strategies based on city-wide Key Performance Indicators (KPIs). In turn, decision-makers are enabled to compare different placement strategies in terms of their potential impacts on their objectives. This method holds the potential to accelerate micro-planning processes with defined target scenarios and data-based insights per hub location.
The improved method is developed upon the case study of Munich, considering various location planning objectives. 600 locations composed of 3 hub types are suggested in order to achieve citywide accessibility within 5 minutes of walking time. 1000 locations of an additional hub type are suggested to achieve city-wide accessibility within 3 minutes of walking time.

Shared mobility
Shared mobility hubs
Multimodal integration
Intermodal integration
Location planning
Spatial MCDA
Network analysis
Location Optimization

http://resolver.tudelft.nl/uuid:66bee6bb-863b-49ac-94bd-357384570b1f

Student theses

master thesis

© 2022 Moritz Kistenfeger