Page History
...
Workflow Step | TMC (Traffic Message Channel) | OpenLR (Dynamic Location Referencing) | Key Differences / Notes |
|---|---|---|---|
1. Location Model | Pre-coded locations stored in a Location Table with static IDs | Dynamic encoding based on geometry, topology, and attributes | OpenLR does not rely on static tables; supports unlimited locations. |
2. Map Dependency | Relatively dependent on the specific map version/revision used to build and link the TMC table codes to the road network map elements | Map‑agnostic and designed to work across multiple maps and versions | As road networks evolve, older TMC location tables may become outdated. |
3. Location Identification | Lookup of a pre-defined TMC Location Code based on table or attributed map elements. | On‑the‑fly encoding of point/line based on actual map geometry | TMC is instant lookup; OpenLR requires computation. |
4. Encoding Process | Encoding = selecting the right TMC Location Codes from the table | Encoding = generating a reference path via attributes + geometry | OpenLR encoding is computationally heavier but flexible. |
5. Message Construction | Very compact messages (a few bytes) | Larger messages (~20–30 bytes for a line location) | Size is rarely an issue today, but OpenLR uses more bandwidth. |
6. Transmission | Typically used in broadcast (RDS, DAB), and low‑bandwidth IP environments | Typically used in wider bandwidth IP-based environments. | OpenLR is suitable for richer digital ecosystems. |
7. Decoding Method | Match Location Codes to same TMC table on receiver side, and look up associated map elements in map. | Decoder reconstructs location using map matching + shortest-path algorithms | OpenLR decoding is more CPU-intensive compared to TMC decoding. |
Further migration considerations
- Performance and Bandwidth Considerations: The shift from TMC to OpenLR also introduces changes in message size and computational workload. TMC messages are extremely compact—typically only a few bytes—because they reference predefined IDs. OpenLR messages are larger, although size is rarely a limiting factor today. More important is the computational load: while encoding can be handled efficiently on central servers, decoding is significantly more demanding and must be performed on the end-user device. This is especially evident in major metropolitan areas such as London or Paris, where devices may need to decode large volumes of traffic messages within short timeframes. In such environments, CPU load may increase noticeably, potentially delaying the processing of subsequent message batches
- Legacy System Integration: TMC has been deeply embedded in navigation devices, broadcast protocols, and traffic‑management workflows for many years. As a result, migration to OpenLR typically requires transitional measures. These often include dual support for both formats, bidirectional conversion between TMC and OpenLR, and updates to broadcast or distribution systems. Maintaining continuity for existing services is an important operational requirement, making careful planning and phased migration essential.
- Quality Assurance: The transition to OpenLR requires thorough verification to ensure that encoded locations correspond correctly to their intended positions. This includes validating accuracy across different map versions, detecting mismatches caused by attribute or topology differences, and ensuring resilience as underlying maps evolve. Achieving this level of reliability requires automated QA infrastructure, consistent regression testing, and ongoing monitoring to verify that references continue to behave correctly as maps are updated.
Governance and Standardization: TMC benefits from a long-standing standardized framework, although maintaining location tables is resource‑intensive. OpenLR is open and broadly adopted, but the ecosystem includes several variants—such as the TomTom formats, the ISO TPEG2‑OLR standard, and the XML‑based adaptations used in DATEX II and TN‑ITS. This diversity provides flexibility but introduces complexity when interoperability across stakeholders is required. Ensuring encoder–decoder compatibility across these variants is therefore an important aspect of system design and governance.
Requisite skills and investmentInvestment: Migrating from TMC to OpenLR is not merely a change in location referencing format, but a transition that requires dedicated development effort and specialized expertise. Implementing a functional OpenLR encoder/decoder typically involves knowledge in geographic information systems (GIS), routing algorithms, and map-matching techniques. Available reference implementations should not be considered “plug‑and‑play” solutions. Integrating OpenLR into a specific deployment context could require substantial customization, performance optimization, and validation effort.
...
