In recent years an effort to tackle pollution and achieve sustainability has been made throughout the world. In terms of energy and mobility, this has resulted in low or emission-free mobility implementation. Electric mobility in an urban area requires a well thought development plan, especially when considering the charging infrastructure required for it. In cities such as Amsterdam, electric mobility also involves boats. This means that a holistic plan for electric boats in the future must be implemented, along with the needed charging infrastructure.
To select a suitable location for charging infrastructure for boats in Amsterdam, this study develops a methodology combining a spatio-temporal analysis with a multiple criteria decision analysis. Moreover, different charging technologies, such as Wireless power Transfer are reviewed to assess the potential of charging boats in water.
Based on real life vessel location data, this study implements spatio-temporal analysis for location selection for new charging stations in Amsterdam Centrum, taking its unique spatial characteristics into consideration, such as the great number of bridges in the canals, the lack of space at the quays or the current charging infrastructure for electric vehicles. This study provides insights on route and flows, density of vessels, speeds, characterization of vessels. The spatio-temporal analysis results in the calculation of energy demand of vessels that is followed by a spatial multicriteria decision analysis that incorporates the energy demand points and several spatial constraints. The data-driven methodology used in this work results in new proposed locations for emission-free vessel charging infrastructure that accommodate for the Amsterdam Centrum’s context and minimize vessel trajectory disturbance.

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.