Research Summary: National-Scale Multimodal Accessibility Fields from Open Geospatial Data

Researchers have introduced MAPPER (Multimodal Accessibility and Place Profiling Engine for Arbitrary Regions), an open-data analytical pipeline designed to address reproducibility barriers in large-scale accessibility analysis. The framework combines OpenStreetMap network data and GTFS public transit feeds across pedestrian, bicycle, vehicular, and transit modes, utilizing network kernel density estimation and a high-performance C++/OpenMP compute core to model continuous accessibility fields.

Why it matters

This development establishes an open, reproducible framework for measuring multimodal transit and infrastructure accessibility at scale. By eliminating reliance on fragmented or proprietary routing engines, it enables consistent cross-regional comparisons of transport equity and land-use integration. Consequently, public and private planners gain a transparent mechanism to evaluate spatial access and optimize capital investments.

Key insights

• Large-area multimodal accessibility analytics historically suffer from reproducibility issues due to fragmented workflows and heterogeneous spatial datasets.
• The MAPPER pipeline unifies multimodal network graphs (walking, cycling, driving, and transit) utilizing OpenStreetMap and General Transit Feed Specification (GTFS) data.
• The framework standardizes points of interest (POI) and proxy layers to map land-use opportunities against travel networks using network kernel density estimation.
• High-performance routing and processing are achieved via contraction hierarchies and a native C++/OpenMP compute core.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2610.32479