City Information Modelling (CIM)
A four-layer digital twin that integrates the physical city with human perception and time — turning youth mobility data into testable scenarios and a shared space for stakeholders.
A digital backbone for the project
The City Information Model (CIM) is the digital backbone of NextGenMobility. It moves beyond 3D mapping to integrate the physical city with human perception and temporal constraints — translating youth-friendly, 15-minute-city principles into a data-driven model that doubles as a co-creation space for stakeholders. By organising data into distinct layers, the CIM shows not only where infrastructure exists, but how well it actually serves the mobility needs of young people.
Four layers
Structural — the physical skeleton
The geometric and topological foundation of the city: road and path networks, sidewalks and pedestrian routes, dedicated bicycle lanes and bike infrastructure, plus a 3D built environment of building footprints used to analyse urban density and visibility.
Functional — the serviceable city
Maps the city's supply of services and tests their accessibility within the 15-minute frame: youth-centric points of interest (schools, vocational centres, sports and social hubs), service opening hours within travel-time isochrones, and public-transit schedules (GTFS) for multimodal travel times.
Human logic & behaviour (ABM)
Introduces human logic into the digital twin — how perceived safety, comfort and attractiveness change real movement. Inputs include perception weights from Likert-scale surveys, anonymised GPS trajectories from roughly 100 young people per pilot, and activity-duration distributions that anchor the simulation in time.
Simulation — the predictive environment
The testing ground for the Urban Living Labs, where "what-if" scenarios are evaluated before anything is built on the street. It draws on experimental outcomes from interventions and agent decision rules (multinomial logit) that predict how young people choose between walking, cycling and transit.
Three analytical focuses
Each layer answers a different question — Where and When the city enables movement, Why young people choose as they do, and the combined Youth Utility that emerges.
Integration · "Where / When"
Space Syntax audits the street grid's connectivity; 2SFCA and gravity models measure access to youth-relevant services; chrono-urbanism checks that 15-minute goals are physically achievable.
Attractiveness weights · "Why"
Structural Equation Modelling identifies the psychological facilitators and barriers to mobility; AHP prioritises user preferences; exploratory analysis of trajectories reveals where young people actually go, not just where they could.
Youth utility · "I × W"
Agent-based modelling combines the integration data with attractiveness weights to create a "weighted movement potential" — then simulates how youth would respond to different scenarios.
Technical readiness in each Living Lab
Because every city starts from a different point, the methodology includes a readiness audit before the model is built. It checks what the CIM should achieve (a reference map versus a live decision-making tool), whether data should be static or dynamic, hosting and open-source preferences, each lab's technical proficiency (GIS network analysis, accessibility modelling, data engineering), and the availability of key datasets — from detailed road, sidewalk and bike-lane data to anonymised youth GPS trajectories.
Source: CIM Methodology & Expectations in ULLs, Work Package 2, prepared by Junia HEI.