A novel methodology for micromobility system assessment using multi-criteria analysis

•A new method for evaluation of neighborhood suitability for micromobility modes.•Calculation of a numerical index using a weighted multi-criteria approach.•A framework for selecting the optimal micromobility system type for each study area. With cities around the world increasingly looking at micromobility as a means to achieve their goals for cleaner transportation and better air quality, local authorities, planners, and shared mobility operators are increasingly confronted with decision problems related to investment in micromobility systems. In order for decision making to be better informed, and objectives to be achieved, prioritizing investment according to the area needs and characteristics is highly important. In this context, the objectives of the present study are two: (1) propose a framework for the evaluation of neighborhood suitability for micromobility modes, (2) assist decision making with regards to the selection of the optimal micromobility system type for a study area. First, considering a range of both supply- and demand-based criteria, we construct an index calculated using a weighted multi-criteria approach with the purpose of identifying neighborhoods that are more conducive or less conducive to the integration of micromobility. Relative weights of the criteria and the respective indicators used for the calculation of the index are derived through the Analytic Hierarchy Process (AHP) method. The developed assessment framework is applied in three case studies of different scale: an urban district in the center of Paris, a university campus in the southern suburbs, and an eco-district currently under development in the southwestern suburbs of the city. Then, in the context of the second objective of this work, we propose a multi-criteria analysis framework for the selection of the optimal system type among seven alternative options for each study area. Considering multiple factors associated with the desirability of each of these modes in a neighborhood, we calculate priorities for each system type for the three selected case studies, using the classical AHP method.