Speed-limit information is one of the most policy-relevant yet operationally difficult elements of the OpenStreetMap road network. It is essential for intelligent speed assistance, driver warning systems, speed-aware navigation, and public-sector road-safety analysis, but it is not represented by a single complete attribute. In OSM, speed-limit evidence may appear as explicit maxspeed=* tags, inherited rule tokens such as source:maxspeed or maxspeed:type, contextual road classes, traffic-sign tags, or area-dependent legal defaults. It can also be absent, stale, or split across many short ways. For mobile driver assistance, this creates a scientific and engineering problem: an application must not only query OSM quickly, but also preserve provenance, infer missing context, update from daily map changes, and remain reproducible enough that claims about performance and completeness can be independently checked.

This study presents YouSpeed.de, an offline-first smartphone speed-limit runtime built from OSM extracts, as a deployment-oriented investigation of that problem. The aim is not to demonstrate a consumer application in isolation, but to answer a narrower research question: which OSM-derived data structures and update mechanisms make country-scale, low-latency speed-limit inference feasible on commodity smartphones while preserving traceability to the original map objects? The contribution is a reproducible benchmark and artifact pipeline that treats OSM as a mutable runtime knowledge base rather than as a static background map.

The methodology combines four empirical components. First, we scan European Geofabrik country extracts and measure explicit maxspeed=* coverage on car-drivable ways, separating direct speed evidence from data that requires rule-aware fallback. Second, using a Germany OSM snapshots, we generate four runtime architectures with identical source semantics: S1, a global-index baseline; S2, a spatially tiled content-addressed pack; S3, a single-file SQLite database with RTree spatial indexing; and S4, a SQLite variant with tile-membership prefiltering. The architectures are evaluated with three maxspeed query modes plus built-up-area containment at a fixed Berlin probe point, so that storage effects can be compared under a common workload. Measurements are taken both on a cloud-like Apple M4 Max host and on a physical iPhone 14 Pro to expose mobile sandbox costs. Third, we analyze a month of consecutive Germany daily OSM diffs, quantifying changed ways, speed-tag events, partition invalidations, simulated SQL patch runtimes, and payload sizes. Fourth, we evaluate several matching approaches on recorded logs covering 42,654 GPS fixes and 29,411 hindsight labels, so that architectural choices can be compared with route-level matching cost rather than lookup latency alone.

The first finding is that explicit speed-limit completeness varies strongly by country and cannot be reduced to extract size. The Netherlands leads the current scan with 0,92 million explicit-speed ways out of 1,36 million car-drivable ways, or 68 percent. Germany ranks eighth with 2,63 million explicit-speed ways out of 7,96 million, or 33 percent. France has 1,49 million explicit-speed ways out of 7,11 million, or 21 percent. These figures show why an OSM-based speed assistant needs provenance-aware fallback logic and area-context lookup: in many jurisdictions, the majority of drivable ways do not carry direct numeric speed limits.

The second finding is that physical packaging matters as much as spatial indexing. In the Germany benchmark, we investigate four storage approaches in terms of file size and query latency and derive a concrete systems conclusion: for the current OSM speed-limit runtime, a single-file embedded spatial database is the best measured deployment default. This result is not merely an implementation preference. It shows that mobile OSM applications with frequent lookups need artifact designs that respect operating-system file handling, local database performance, and update granularity, not just spatial-index theory.

The third finding concerns OSM's update dynamics. The practical implication is that daily OSM change ingestion is not only a backend concern: update granularity and mobile artifact design determine whether open map freshness can reach offline clients without excessive bandwidth or file-management overhead. In this setting, reproducible diff analysis is part of the scientific result, because speed-limit assistance depends on both low-latency lookup and the ability to keep OSM-derived artifacts current.

The route-level replay results refine, rather than overturn, the data-layer conclusion. We evaluate several approaches for matching GPS fixes to OSM road segments. The strongest lightweight matcher profile achieves 94 percent replay accuracy with a p95 query latency of 8.5 ms. A comparison with the state-of-the-art routing-engine Valhalla shows similar matching performance, but also indicates that the richer topology of Valhalla requires more space and is not automatically better when storage size, updateability, traceability, and mobile latency are measured together.

The study's scientific contribution is therefore threefold. It provides a quantitative view of European OSM speed-limit provenance, a reproducible architecture benchmark for transforming OSM speed and area context into mobile runtime artifacts, and a deployment-facing analysis that links lookup latency, daily diff behavior, and lightweight map matching. The practical benefit for the OSM community is a concrete feedback loop: local observations can be captured on device, reviewed after the drive, exported as editor-oriented .osc change packages, and later reconciled through normal OSM diffs, rather than uploaded automatically by an opaque app pipeline. Our reproducible conclusion is that OSM can support offline speed-limit assistance when speed provenance, jurisdictional fallback, artifact packaging, and update mechanics are evaluated as one system.