The Roads Management Insights data models are built by combining different information sources to provide insights into road congestion.

## Road congestion

The Roads Management Insights data models for trip duration and speed reading are built
by combining different information sources:

- **Aggregated maps data:** The most critical source is aggregated, anonymized
  data from Google Maps, which allows Google Maps to calculate the real-time
  speed of vehicles on roads around the world.

- **Historical traffic data:** Over time, the aggregated user data is used to
  build historical traffic patterns, which help the system understand the
  "normal" traffic for a specific road at any given time and day of the week.

- **Supplemental data:** Historical data is combined with other data,
  including third-party information from partners like local Departments of
  Transportation, as well as real-time user feedback from Maps users reporting
  incidents like crashes or construction.

AI combines these information sources together to understand current conditions
with real-time data, and to provide baseline predictions with historical data.
This fusion is key for how routes are predicted, for example:

- Short routes depend largely on **current** , **real-time** information
- Longer routes use **advanced AI modeling** , where nearby segments are predicted using **real-time data** , while more-distant segments rely more heavily on **historical patterns.**
- Roads with limited real-time signals rely more heavily on its **historical
  data** to predict slowdowns.

### Speed reading intervals

A **speed reading interval (SRI)** is a contiguous stretch of a route where
traffic falls into a single speed category. Together, a route's speed reading
intervals cover the entire route in order from origin to destination.

#### Speed categories

The speed categories align with the colors shown in the Google Maps traffic
layer:

| Speed category | Google Maps traffic color | Description |
|---|---|---|
| `NORMAL` | Green | Traffic is flowing smoothly; no slowdown is detected. |
| `SLOW` | Yellow | Slowdown is detected, but no traffic jam has formed. |
| `TRAFFIC_JAM` | Red | Traffic jam is detected. |
| `TRAFFIC_JAM_HEAVY` | Dark red | Heavy traffic jam is detected. Available as of October 8, 2026 in offset-based speed reading intervals (`speed_reading_offsets`) only. |

#### Formats

- **Offset-based speed reading intervals (`speed_reading_offsets`):** Each
  interval is demarcated by `start_offset` (inclusive) and `end_offset`
  (exclusive), measured in meters from the start of the route. The
  `end_offset` of each interval equals the `start_offset` of the subsequent
  interval.

  For example, a 100-meter route where the first 80 meters have a slowdown and
  the remaining 20 meters flow normally is represented as:

  | `start_offset` | `end_offset` | `speed` |
  |---|---|---|
  | `0` | `80` | `SLOW` |
  | `80` | `100` | `NORMAL` |

- **Coordinate-based speed reading intervals (`speed_reading_intervals`,
  legacy):** Each interval is defined by polyline coordinates
  (`interval_coordinates`).
  In this format, heavy traffic jams (dark red) are reported as `TRAFFIC_JAM`;
  `TRAFFIC_JAM_HEAVY` is never used. This legacy field might stop being
  populated on or after January 13, 2027. Use `speed_reading_offsets` instead.

For schema details, see
[Real-time road data](https://developers.google.com/maps/documentation/roads-management-insights/real-time-data.md.txt#travel_duration_and_speed_reading_intervals_schema)
and the
[`recent_roads_data` table](https://developers.google.com/maps/documentation/roads-management-insights/accumulated-data.md.txt#recent_roads_data_table).

### BigQuery tables

To query the accumulated data for trip duration and speed reading intervals, see
the
[`historical_travel_time` table](https://developers.google.com/maps/documentation/roads-management-insights/accumulated-data.md.txt#historical_travel_time_table)
and the
[`recent_roads_data` table](https://developers.google.com/maps/documentation/roads-management-insights/accumulated-data.md.txt#recent_roads_data_table)
in BigQuery.