Problem
Research questions and friction points this paper is trying to address.
e-scooter mobility hub planning
causal discovery
urban typology
demand modeling
city-specific variation
From Causal Discovery to Implementation: An Agentic AI Framework for E-Scooter Mobility Hub Planning Across 29 German Cities
🤖 AI Summary
This study addresses the lack of causal evidence linking urban typologies to micromobility hub planning and the reliance on proprietary data that obscures heterogeneous user behavior. To overcome these limitations, we propose a three-stage agent-based AI framework leveraging publicly available GBFS data from 29 German cities. The framework employs an LLM-coordinated causal discovery pipeline to construct a library of causal templates spanning diverse city and neighborhood types, enabling a planning tool that supports site scoring, facility configuration, and automated reporting. For the first time, we systematically identify distinct demand mechanisms driven by environmental factors in core versus peripheral areas across urban typologies. Stable causal patterns are detected across 57 city-neighborhood units, and the framework’s efficacy and transferability are validated through its successful deployment in guiding the construction of two scooter hubs in Heilbronn.
📝 Abstract
Existing approaches to e-scooter mobility hub planning lack city-type-specific causal evidence. Demand models are typically correlational, built on proprietary trip data, and do not distinguish how driver profiles vary across urban typologies. This paper presents a three-phase agentic AI framework that constructs a Causal Template Library from public GBFS data across 29 German cities, encoding which environmental features causally drive hotspot demand for each combination of city type (large, university, industrial, hilly) and cluster type (core, peripheral). A large language model (LLM) orchestrated causal discovery pipeline adapts algorithm selection to local data conditions across 57 city-cluster units. The library reveals systematic variation. Core demand is driven by activity access and transit proximity, while peripheral demand responds to built form, with city-type-specific patterns supporting transferable siting templates. A planning tool built on the library scores candidate sites, calibrates infrastructure recommendations to local demographics, and generates practitioner-ready reports. In Heilbronn, Germany, two hub sites informed by the framework's causal evidence are currently under construction, illustrating how the outputs can support real-world siting decisions.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Causal Discovery
Agentic AI
Urban Mobility Planning
Large Language Model (LLM)
GBFS Data
🔎 Similar Papers
No similar papers found.
