# User Adaptive Score

The **User Adaptive Score** provides a dynamic and ongoing assessment of a user’s driving or riding behavior over time, based on the user's car and motorcycle trips respectively. Unlike trip-level scores that evaluate individual trips, the User AdaptiveScore is a rolling aggregate that reflects a user’s overall performance across multiple trips. This continuous evaluation helps in tracking long-term trends in driving or riding behavior, making it easier to identify improvements or areas that require further attention.

The user adaptive score is updated at the end of every trip and is computed using the following formula:\
&#x20;    <mark style="color:blue;">**User\_Adaptive\_Score = \[(1 − α) × Current\_User\_Adaptive\_Score ] + (α × Current\_Trip\_Score)**</mark>

Where:

* **Current User Adaptive Score**: The user’s adaptive score from previous trips
* **Current Trip Score**: The score for the most recent trip
* **Alpha (α)**: A weighting factor between 0 and 1 that determines the influence of the current trip score on the overall User Adaptive Score

{% hint style="info" %}
**GQL Definition**: UserEngagementScore

**GQL Path**: `user.engagement.scores.slice.name`
{% endhint %}

```graphql
query User($user_id: String) {
  user(user_id: $user_id) {
    engagement {
      scores {
	slice {
	  name # name of the score. Ex: OVERALL_SCORE, ATTENTION_SCORE, LEGAL_SCORE, etc
	  value # value of the score. Between [0, 100] for OVERALL_SCORE and between [0,1] for rest of scores ]
        }
      }
    }
  }
}
```

## Sub Scores

The Dynamic Scoring Model features an adaptive overall score, denoted as `OVERALL_SCORE`, which represents a comprehensive assessment of a user's driving or riding behavior. In addition to this overarching score, there are specific sub scores designed to highlight particular aspects of driving or riding behavior. All scores, including the overall score, are computed per transport mode. For some use cases it might be useful to [merge scores for cars and motorcycles](https://app.gitbook.com/o/-LTy4edtsWdQgbEsKB-i/s/B9ZHBaHKglgKmgIlyHT0/~/changes/208/important-topics/merging-scores-for-motorcycles-and-cars).

<table><thead><tr><th width="247">Score</th><th>Comment</th></tr></thead><tbody><tr><td>FOCUS_SCORE</td><td>Acknowledges periods of attentive driving or riding without active phone handling</td></tr><tr><td>CALL_WHILE_MOVING_SCORE</td><td>Identifies instances where drivers or riders avoid making calls while moving over a speed of 15 km/h</td></tr><tr><td>ATTENTION_SCORE</td><td>Acknowledges periods of attentive driving without distractions like hands free calling, handheld calling, phone handling or screen use</td></tr><tr><td>LEGAL_SCORE</td><td>Rewards adherence to speed limits</td></tr><tr><td>MFFS_SCORE</td><td>Variation of LEGAL_SCORE that excludes congested roads</td></tr><tr><td>HARSH_MOVEMENT_SCORE</td><td>Recognises sequences of smooth driving, emphasising gentle acceleration and braking</td></tr><tr><td>SMOOTH_SCORE</td><td>Similar to HARSH_MOVEMENT_SCORE but also takes into account harsh turning events</td></tr></tbody></table>

## Example Wireframe

<div align="left"><figure><img src="https://3097961207-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FB9ZHBaHKglgKmgIlyHT0%2Fuploads%2Fgit-blob-cd30602a59306fc13b05c4cc6922dc10e00f9fed%2Fimage.png?alt=media" alt="" width="375"><figcaption></figcaption></figure></div>

***

Additional helpful links:

* GQL [UserEngagementScore](https://graphqldocs.sentiance.com/#definition-UserEngagementScores) definition


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