Migrating from TMC (Traffic Message Channel) to OpenLR involves a shift from fixed, pre‑coded location tables to a dynamic, map‑agnostic location‑referencing approach. OpenLR enables significantly greater flexibility, supports virtually unlimited coverage of road networks and locations of interest, and improves interoperability across different digital map providers. At the same time, this transition introduces new technical, operational, and performance considerations. Whereas TMC encoding did not require access to a routable digital road network, now OpenLR encoding does depend on the availability of a suitable digital map with a routable road network. Successful adoption therefore requires careful attention to encoding accuracy, as well as verifying cross‑map interoperability of OpenLR encoders.
Migrating from TMC (Traffic Message Channel) to OpenLR involves a shift from fixed, pre‑coded location tables to a dynamic, map‑agnostic location‑referencing approach. OpenLR enables significantly greater flexibility, supports virtually unlimited coverage of road networks and locations of interest, and improves interoperability across different digital map providers. At the same time, this transition introduces new technical, operational, and performance considerations. Whereas TMC encoding did not require access to a routable digital road network, now OpenLR encoding does depend on the availability of a suitable digital map with a routable road network. Successful adoption therefore requires careful attention to encoding accuracy, as well as verifying cross‑map interoperability of OpenLR encoders.
Why the Need for Migrating from TMC to OpenLR?
TMC relies on static, pre-coded location tables that limit coverage to predetermined road segments and require frequent maintenance. As digital mobility ecosystems evolve and more stakeholders use diverse maps (e.g. from Google, HERE, TomTom, Open Street Map), a map‑agnostic approach becomes essential. OpenLR supports unlimited coverage, map independence, and dynamic location encoding, enabling consistent interpretation of locations across different maps and versions. Its flexibility is better suited for modern traffic services, dynamic updates, and large-scale multi-map environments.
Stakeholder Relevance / Rationale
Relevance and rationale for migrating to OpenLR for various stakeholders can be summarized as follows:
Public Authorities: Supports interoperability across national access points and data platforms. Scales to new road networks, and eliminates dependency on pre-coded tables with limited coverage and associated maintenance costs.
Content Providers: Enables consistent location exchange across different map providers. Simplifies data aggregation from heterogeneous sources. Reduces constraints caused by limited TMC location coverage.
Service Providers: Facilitates real-time, high-volume traffic message handling. Supports dynamic updates that work across multiple map ecosystems. Avoids reliance on static TMC tables that may lag behind map updates.
OEMs: Offers a flexible method for in‑vehicle systems where map providers vary. Allows dynamic encoding for real-time routing and ADAS services. Reduces the need for large, pre-installed TMC tables and updates thereof.
Detailed Explanation
Migration means adopting a fundamental change in referencing approach
TMC relies on fixed identifiers that point to pre‑defined entries in a centrally maintained location table. Each identifier refers to a known, pre‑coded location point, linear segment, or area, which guarantees deterministic and lightweight matching on the receiver side. However, this approach inherently restricts flexibility: only locations that have been coded in advance can be referenced, coverage is limited by the size and maintenance of the location table, and extending to new roads or regions requires coordinated updates across the ecosystem.
OpenLR works fundamentally differently. Instead of static identifiers, it generates dynamic location references derived from the actual geometry, topology, and attributes of the road network. Encoding a location therefore requires access to a routable digital map, as the encoder must understand how road segments connect, how paths are formed, and where decision points such as junctions and merges occur. While OpenLR is map‑agnostic in the sense that it is not tied to a specific vendor or map version, it nevertheless requires access to a suitable routable road network at encoding time.
Encoding/decoding TMC location references versus OpenLR location references
This section compares encoding and decoding of TMC location references with encoding and decoding of OpenLRLocation References, highlighting how each approach represents locations, constructs location references, and reconstructs them at the receiver side. By contrasting the table‑driven, point‑based nature of TMC with the map‑based, path‑oriented design of OpenLR, the following text illustrates how these two mechanisms differ in terms of map dependency, flexibility, robustness, and suitability for various traffic and travel information ecosystems.
Encoding
Decoding
TMC
TMC location codes (also known as Problem Locations or PLOCs) are defined in a TMC location table. Point Locations are most commonly used for TMC location referencing. A point location typically refers to an intersection in e.g. along a Highway. In a TMC location table, a TMC point location has as attributes the following (and more):
a WGS84 coordinate pair
relevant location names, including the road number and/or road name and the name of the exit or cross-road
two links to the previous and next TMC point locations (e.g. in the picture above for PLOC 4187 the next PLOC would be 4188 and the previous one 4186).
Thus based on the WGS84 coordinate pairs, these TMC problem locations can be overlaid on a satellite image (as above) or overlaid on a scanned map for defining TMC location references. No digital map with routable roadsegments is needed!
Encoding TMC location references entails first identifying which TMC location codes straddle the location of the event of interest. In the picture above assume these are PLOC 4185 and PLOC 4188. PLOC 4188 is referred to as the Head Location (the location point where a driver exits the event), and PLOC 4185 as the Tail Location (the location point after which a driver would enter the event)
Given this information and the previous/next links in the TMC location table then a TMC location reference is constructed as:
Head location: PLOC 4188
Extent: -3 (the number of steps in the TMC location table from the Head location (4188) to the Tail Location (4185) following the next (in case of positive extent) or previous links (negative extent).
Decoding TMC location references in a receiver first involves expanding the Head Location (4188) and Extent (-3) again into the set of TMC locations, i.e. 4185, 4186, 4187, 4188, using the TMC location table and the previous linkage.
Given this set of locations and the driving direction (positive, or using the next linkage), then in a digital roadmap, the set of relevant road segments can be retrieved. In TMC enabled digital maps, individual road segments have an attribute indicating to which TMC point location they are leading to, and whether that is external to the TMC problem location or internal, e.g. in case of a large highway intersection with separate exit and entry link roads. Then the road segments after the first intersection exit until the last intersection entry road are considered to be location internal road segments.
For the positive driving direction these segments are labeled as '+' for location external segments and 'P' for location internal segments. For the negative driving direction these segments are labeled as '-' for location external segments and 'N' for location internal segments.
Thus for the given location reference, this involves identifying road segments with attributes
(4186, '+' or 'P'): red in the figure above
(4187, '+' or 'P'), yellow in the figure above, and
(4188, '+' or 'P'), green in the figure above.
Stringing together these road segments provides then for the location of interest in the receiver. These road segments then can be colored to mark abnormal traffic flow, or given a special color with an Icon at the tail location to indicate e.g. a roadworks event or other relevant incident.
OpenLR
Encoding OpenLR location references relies directly on a digital road map and its routable road segments. To create an OpenLR location reference, a digital map with routable road segments is therefore required.
The intended location reference represents a routable path. This path is described by selecting at least two Location Referencing Points (LRPs), which together with the shortest route in between them define the location.
Each LRP has the following attributes:
A WGS‑84 coordinate pair, specifying the geographic position of the LRP.
Line attributes that further characterize the road segment at the LRP and support robust matching at the receiver side of LRPs, including:
Bearing: indicates the direction of travel along the road at the LRP.
Functional Road Class (FRC): indicates the relative importance of the road at the LRP.
Form of Way (FoW): indicates the type of road (for example, single carriageway or dual carriageway) at the LRP.
In addition, for the path between two successive LRPs, the following attributes are defined:
Lowest Functional Road Class to Next Point (LFRCNP): indicates the lowest FRC encountered along the path from the current LRP to the next.
Distance to Next Point(DNP): indicates the length of the route between two successive LRPs.
Note: For simplicity, only two LRPs are shown here. In practice, an OpenLR location reference may include multiple intermediate LRPs to ensure that each routing path between successive LRPs is unique.
Decoding an OpenLR location reference also relies on a digital road map with routable road segments. A digital map that supports routing is therefore required to decode an OpenLR location reference.
The decoding process starts by matching the Location Referencing Points (LRPs) onto the receiver’s digital map. This matching is based on the WGS‑84 coordinate pairs in combination with the associated line attributes.
These line attributes help identify the correct road segment and travel direction. The Bearing attribute is used to determine the direction of travel at the LRP. In situations where multiple nearby or parallel road segments exist, the FRC and FoW attributes help distinguish between roads of different importance and type, enabling selection of the most appropriate candidate.
In a second step, the route between successive LRPs is reconstructed using a routing algorithm on the receiver’s map. The validity of this reconstructed route (and thus of the decoded location reference) is verified using the provided path attributes. These attributes indicate the expected length of the route (DNP attribute) and whether the route remains on roads with the expected functional road classes (LFRCNP attribure), rather than diverging onto lower‑importance roads.
Together, the line attributes and path attributes provide for a highly robust decoding mechanism by enabling cross-checks for matching and routing correctness. This robustness makes OpenLR highly interoperable across digital maps from different vendors and across different map vintages, for example, when decoding is performed on an older map than the one used during encoding.
In summary, TMC and OpenLR represent two fundamentally different approaches to location referencing. TMC is table‑driven and map‑agnostic at encoding time, relying on predefined point locations and logical linkages rather than on a routable digital maps. Decoding TMC references requires TMC‑enabled maps that explicitly link road segments to TMC locations. OpenLR, by contrast, is fully map‑based: both encoding and decoding depend on routable digital maps, with location references defined as paths between Location Referencing Points enriched with line and path attributes. These attributes enable robust matching and route reconstruction, making OpenLR highly resilient to map differences across vendors and vintages. As a result, OpenLR offers greater flexibility, scalability, and interoperability for modern digital map ecosystems, while TMC reflects an earlier, compact, but more static approach to traffic location referencing.
Migrating from TMC to OpenLR: impact on Location Referencing Workflows
Migrating from TMC to OpenLR (has a direct impact on how locations are defined, encoded, transmitted, and decoded throughout the traffic information workflow. While TMC is based on pre‑coded, table‑driven location references with relatively simple encoding and decoding logic, OpenLR introduces a fully map‑based, dynamic approach that relies on routable digital maps and algorithmic matching. The table below compares the key workflow steps for TMC and OpenLR, highlighting how responsibilities, dependencies, and computational complexity shift when moving from a static to a dynamic location referencing paradigm.
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. With OpenLR, a larger difference between source and target map versions needs careful matching to ensure correct location referencing, it may reduce success rate .
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. TPEG enables hybrid TMC and OpenLR use in digital Broadcast transmission and IP 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.
Decision Guide
Requirement /consideration
TMC
OpenLR
Notes
Cross-map compatibility
Limited
Excellent
TMC location referencing requires pre‑use agreement between parties and explicit processing to insert location codes into digital maps. OpenLR does not require prior agreement on location codes, and can be decoded against different map databases, making it significantly better suited for heterogeneous, multi‑map ecosystems.
Coverage
Fixed, limited
Unlimited
OpenLR does not rely on predefined location tables; any location that exists in a digital map can be encoded and transmitted. TMC location tables are limited in size (typically around 60,000 locations per table). In Europe, countries typically maintain a single national table, while larger markets such as the USA and China deploy multiple tables (often on the order of 30).
Real-time dynamic updates
Moderate
Excellent
With TMC, locations must be pre‑identified, agreed, and entered into both location tables and digital maps before they can be referenced, limiting responsiveness. OpenLR allows previously unidentified or newly relevant locations to be referenced immediately, for example when an unexpected incident occurs or temporary traffic management is introduced..
Decoder workload
Low
Higher
TMC decoding is computationally efficient, as it relies primarily on table look‑ups. OpenLR decoding requires on‑the‑fly map matching and routing, which increases processing demand on the receiving device. Notwithstanding the higher workload, millions of in‑vehicle and backend systems use OpenLR decoding for traffic updates without practical issues.
Interoperability
Table-dependent
Map-agnostic
TMC interoperability depends on consistent implementation of the same location tables across all parties, which complicates cross‑vendor, or multi‑provider deployments. OpenLR enables interoperability without shared tables, facilitating data exchange across different maps, map suppliers, service providers, and system architectures. However, interoperability still depends on consistent encoder–decoder behavior and alignment on OpenLR formats.
Legacy embedded systems
Strong
Requires migration
TMC is in very widespread use in the intelligent transportation ecosystem, with long-life expectations for e.g. in-vehicle traffic information and navigation systems. Transitional strategies such as dual TMC/OpenLR support and phased migration are recommended to ensure service continuity while gradually increasing the scale of OpenLR adoption across a user base.
Implementation Notes
Provide dual TMC/OpenLR output during transition to ensure backward compatibility.
Pre-deployment testing must account for all major map vendors used by end users.
Measure decoder performance under realistic, high‑volume scenarios.
Establish automated QA and monitoring for geometric mismatches.
References & Tools
For understanding OpenLR as a method the following references are useful:
Migrating from TMC (Traffic Message Channel) to OpenLR means shifting from fixed, pre‑coded location tables to a dynamic, map‑agnostic referencing method. OpenLR offers greater flexibility, unlimited road network coverage of locations of interest, and cross-map interoperability. Nonetheless, the migration introduces technical, operational, and performance considerations. Successful adoption requires careful planning around encoding accuracy, interoperability across digital map providers, decoder performance on end devices, and the integration of legacy systems.
Why the Need for Migrating from TMC to OpenLR?
TMC relies on static, pre-coded location tables that limit coverage to predetermined road segments and require frequent maintenance. As digital mobility ecosystems evolve and more stakeholders use diverse maps (e.g. from Google, HERE, TomTom, OSM), a map‑agnostic approach becomes essential. OpenLR supports unlimited coverage, map independence, and dynamic location encoding, enabling consistent interpretation of locations across different maps and versions. Its flexibility is better suited for modern traffic services, dynamic updates, and large-scale multi-map environments.
Stakeholder Relevance / Rationale
Relevance and rationale for migrating to OpenLR for various stakeholders can be summarized as follows:
Public Authorities: Supports interoperability across national access points and data platforms. Scales to new road networks, and eliminates dependency on pre-coded tables with limited coverage and associated maintenance costs.
Content Providers: Enables consistent location exchange across different map providers. Simplifies data aggregation from heterogeneous sources. Reduces constraints caused by limited TMC location coverage.
Service Providers: Facilitates real-time, high-volume traffic message handling. Supports dynamic updates that work across multiple map ecosystems. Avoids reliance on static TMC tables that may lag behind map updates.
OEMs: Offers a flexible method for in‑vehicle systems where map providers vary. Allows dynamic encoding for real-time routing and ADAS services. Reduces the need for large, pre-installed TMC tables and updates thereof.
Detailed Explanation
Migration means adopting a fundamental change in referencing approach
TMC relies on fixed identifiers that point to pre‑defined entries in a location table. This approach guarantees deterministic matching, but it also restricts flexibility and limits coverage to only those locations that have been coded in advance.
OpenLR works fundamentally differently. Instead of static IDs, it generates dynamic encodings based on the actual geometry and attributes of the road network. Because it does not depend on a specific map version or vendor, it functions as a fully map‑agnostic method that can be used consistently across different maps and updates.
Migrating from TMC to OpenLR: impact on Location Referencing Workflows
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. With OpenLR, a larger difference between source and target map versions needs careful matching to ensure correct location referencing, it may reduce success rate .
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. TPEG enables hybrid TMC and OpenLR use in digital Broadcast transmission and IP 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.
Decision Guide
Requirement /consideration
TMC
OpenLR
Notes
Cross-map compatibility
Limited
Excellent
TMC location referencing requires pre‑use agreement between parties and explicit processing to insert location codes into digital maps. OpenLR does not require prior agreement on location codes, and can be decoded against different map databases, making it significantly better suited for heterogeneous, multi‑map ecosystems.
Coverage
Fixed, limited
Unlimited
OpenLR does not rely on predefined location tables; any location that exists in a digital map can be encoded and transmitted. TMC location tables are limited in size (typically around 60,000 locations per table). In Europe, countries typically maintain a single national table, while larger markets such as the USA and China deploy multiple tables (often on the order of 30).
Real-time dynamic updates
Moderate
Excellent
With TMC, locations must be pre‑identified, agreed, and entered into both location tables and digital maps before they can be referenced, limiting responsiveness. OpenLR allows previously unidentified or newly relevant locations to be referenced immediately, for example when an unexpected incident occurs or temporary traffic management is introduced..
Decoder workload
Low
Higher
TMC decoding is computationally efficient, as it relies primarily on table look‑ups. OpenLR decoding requires on‑the‑fly map matching and routing, which increases processing demand on the receiving device. Notwithstanding the higher workload, millions of in‑vehicle and backend systems use OpenLR decoding for traffic updates without practical issues.
Interoperability
Table-dependent
Map-agnostic
TMC interoperability depends on consistent implementation of the same location tables across all parties, which complicates cross‑vendor, or multi‑provider deployments. OpenLR enables interoperability without shared tables, facilitating data exchange across different maps, map suppliers, service providers, and system architectures. However, interoperability still depends on consistent encoder–decoder behavior and alignment on OpenLR formats.
Legacy embedded systems
Strong
Requires migration
TMC is in very widespread use in the intelligent transportation ecosystem, with long-life expectations for e.g. in-vehicle traffic information and navigation systems. Transitional strategies such as dual TMC/OpenLR support and phased migration are recommended to ensure service continuity while gradually increasing the scale of OpenLR adoption across a user base.
Implementation Notes
Provide dual TMC/OpenLR output during transition to ensure backward compatibility.
Pre-deployment testing must account for all major map vendors used by end users.
Measure decoder performance under realistic, high‑volume scenarios.
Establish automated QA and monitoring for geometric mismatches.
References & Tools
For understanding OpenLR as a method the following references are useful:
Nevertheless, the large majority of modern in‑vehicle and backend systems are capable of handling OpenLR decoding for traffic updates without practical issues.
Agreed rewording: Notwithstanding the higher workload, millions of in‑vehicle and backend systems use OpenLR decoding for traffic updates without practical issues.
DS: I did not receive any confirmation from the car industry that this is the case
VW does not use OpenLR
Mercedes Audi, Stellantis, BMW, Kia use it.
2
Maybe we can convert one specific TMC-segment (a segment of 3 TMC-points) to an OpenLR-segment as an example how to proceed
Need a example, TMC location codes, TMC labeling of road segments, and a generated OpenLR code.
TH: Let's see whether an example on USA maps can be generated.
3
It may be worth to also mention, that it requires a model of the road network (i.e., not only a map in the sense of a picture/drawing) to encode and decode OpenLR. This is an obstacle (and is often misunderstood) for a public authority who is not also the creator of maps/digial road networks. (TMDL)
Explain this. Make workflow recommendation, with migration start from digital map, and parallel generate both TMC and OpenLR location reference, not first TMC LR then TMC decoding on map, then OpenLR encoding.
4
A question, maybe for discussion in the WG: Should a road authority without a TMC or TPEG service continue to maintain a TMC location code set for their road network after a transition to OpenLR? What are arguments for and against? (TMDL)
→ discussion item for the next telco
Proposal 2 (discussion item )
Short Summary Answer
Migrating from TMC (Traffic Message Channel) to OpenLR means shifting from fixed, pre‑coded location tables to a dynamic, map‑agnostic referencing method. OpenLR offers greater flexibility, unlimited road network coverage of locations of interest, and cross-map interoperability. Nonetheless, the migration introduces technical, operational, and performance considerations. Successful adoption requires careful planning around encoding accuracy, interoperability across map providers, decoder performance on end devices, and the integration of legacy systems.
Why the Need for Migrating from TMC to OpenLR?
TMC relies on static, pre-coded location tables that limit coverage to predetermined road segments and require frequent maintenance. As digital mobility ecosystems evolve and more stakeholders use diverse maps (e.g. from Google, HERE, TomTom, OSM), a map‑agnostic approach becomes essential. OpenLR supports unlimited coverage, map independence, and dynamic location encoding, enabling consistent interpretation of locations across different maps and versions. Its flexibility is better suited for modern traffic services, dynamic updates, and large-scale multi-map environments.
Stakeholder Relevance / Rationale
Relevance and rationale for migrating to OpenLR for various stakeholders can be summarized as follows:
Public Authorities: Supports interoperability across national access points and data platforms. Scales to new road networks, and eliminates dependency on pre-coded tables with limited coverage and associated maintenance costs.
Content Providers: Enables consistent location exchange across different map providers. Simplifies data aggregation from heterogeneous sources. Reduces constraints caused by limited TMC location coverage.
Service Providers: Facilitates real-time, high-volume traffic message handling. Supports dynamic updates that work across multiple map ecosystems. Avoids reliance on static TMC tables that may lag behind map updates.
OEMs: Offers a flexible method for in‑vehicle systems where map providers vary. Allows dynamic encoding for real-time routing and ADAS services. Reduces the need for large, pre-installed TMC tables and updates thereof.
Detailed Explanation
Migration means adopting a fundamental change in referencing approach
TMC relies on fixed identifiers that point to pre‑defined entries in a location table. This approach guarantees deterministic matching, but it also restricts flexibility and limits coverage to only those locations that have been coded in advance.
OpenLR works fundamentally differently. Instead of static IDs, it generates dynamic encodings based on the actual geometry and attributes of the road network. Because it does not depend on a specific map version or vendor, it functions as a fully map‑agnostic method that can be used consistently across different maps and updates.
Migrating from TMC to OpenLR: impact on Location Referencing Workflows
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 change, TMC location definitions may become outdated. Older versions of TMC location tables anOpenLR requires careful matching between source and target maps
3. Location Identification
Lookup of a pre-defined TMC Location Code (LC) 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 LCs 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
Used in IP-based, broadcast, or hybrid systems
OpenLR suitable for richer digital ecosystems
7. Decoding Method
Match LC 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.
Decision Guide
Requirement /consideration
TMC
OpenLR
Notes
Cross-map compatibility
Limited
Excellent
OpenLR best for heterogeneous ecosystems
Coverage
Fixed, limited
Unlimited
No need for location tables
Real-time dynamic updates
Moderate
Excellent
OpenLR more flexible
Decoder workload
Low
Higher
Notwithstanding the higher workload, millions of in‑vehicle and backend systems use OpenLR decoding for traffic updates without practical issues.
Interoperability
Table-dependent
Map-agnostic
Better for multi-provider environments
Legacy embedded systems
Strong
Requires migration
Transitional dual support recommended
Implementation Notes
Provide dual TMC/OpenLR output during transition to ensure backward compatibility.
Pre-deployment testing must account for all major map vendors used by end users.
Measure decoder performance under realistic, high‑volume scenarios.
Establish automated QA and monitoring for geometric mismatches.
References & Tools
For understanding OpenLR as a method the following references are useful:
Migrating from TMC to OLR: what are typical issues:
CoPilot input
Great question! Migrating fromTMC (Traffic Message Channel)location referencing—which uses pre-coded locations—to anon-the-fly method like OpenLRintroduces several technical and operational challenges. Here are the key issues:
First present fundamental reasons to migrate:
Benefits
Opposite argument:TMC coverage is limited to pre-coded locations, OpenLR is unlimited
1. Fundamental Differences in Referencing Approach
TMC: Uses a fixed, pre-defined location table (with IDs for road segments, junctions, etc.). This ensures consistency but limits flexibility.
OpenLR: Encodes locations dynamically based on geometry and attributes, making it map-agnostic and more scalable.
Implication: Migration requires rethinking how locations are identified and transmitted—no more reliance on static IDs.
2. Map Provider Variability
Different providers (Google, HERE, TomTom) havedifferent network topologies, segment definitions, and attribute sets.
OpenLR is designed to be map-independent, butaccuracy depends on how well the encoding matches the target map.
Challenge: Ensuring interoperability and consistent decoding across providers.
3. Accuracy and Ambiguity
TMC guarantees exact matches because of pre-coded IDs.
OpenLR uses shortest-path algorithms and attribute matching, which can lead to:
Ambiguityin dense urban areas.
Errorsif road attributes differ between source and target maps.
Mitigation: Fine-tuning encoding parameters and implementing robust fallback strategies.
Opposite argument:TMC coverage is limited to pre-coded locations, OpenLR is unlimited
4. Performance and Bandwidth
TMC: Compact messages (just IDs).
OpenLR: Larger payloads (geometry, attributes), which can impact bandwidth and processing time.
Consideration: Efficient encoding and compression are critical for real-time systems.
Size: Less of an issue now going from ~2-4 bytes to ~20-30 bytes per location reference
Encoding / decoding time and processing impact:
Encoding is more compute-intensive, but most often done on large server
Decoding on user devices is much more compute intensive, and especially in large urban areas with lots of traffic messages (e.g. London, Paris) may strain a CPU and take a long time to process, which is a risk if the next batch of traffic messages.
5. Legacy System Integration
Many traffic systems, navigation devices, and broadcast protocols are built around TMC.
Migration requires:
Dual supportduring transition.
Conversion tools(TMC ↔ OpenLR) for backward compatibility.
6. Quality Assurance
Need to validate that OpenLR-referenced locationsmatch intended real-world positionsin a map-agnostic way ( handling multiple map versions from same map vendors, as well as maps from different vendors).
Requiresthorough testingand needs SW and data infrastructure for automaticmachine-based error detection.
Variations in maps and map updates could impact accuracy, and re-validation is advice.
7. Governance and Standardization
TMC is standardized and widely adopted, but supporting various versions of TMC location tables presents large overhead at the server side/encoding side.
OpenLR is open source, also widely adopted, but less regulated, so encoder/decoder compatibility may require attention.
Agreed rewording: