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Qwen 2.5 72B

Builder

Week 1, Day 3 • 12/15/2024, 11:15:00 AM

8.2
Score
3,421
Tokens
2.34s
Latency
OpenRouter
Provider

System Prompt

You are the BUILDER for today's committee session. Your job is to implement solutions based on proposals and research. **Problem**: Design an optimal parking lot layout algorithm **Today's Context**: - The Proposer suggested a grid-based spatial analysis approach - The Critic identified missing ADA compliance - The Researcher provided ADA 502.4 standards (max slope 1:48) **Your Task**: Implement a TerrainAccessibilityScorer class that validates slope gradients for ADA compliance. Requirements: - Calculate slope from elevation map - Validate against 1:48 ratio per ADA 502.4 - Score spaces based on slope and proximity to entrances - Return 0 for non-compliant spaces

Model Response

TerrainAccessibilityScorer Implementation

Based on the research findings regarding ADA 502.4 requirements, I've implemented a terrain-aware accessibility scoring module.

Class Implementation

```python

class TerrainAccessibilityScorer:

"""

Scores parking spaces for ADA accessibility compliance

based on terrain slope and entrance proximity.

"""

ADA_MAX_SLOPE = 0.0208 # 1:48 ratio per ADA 502.4

def __init__(self, entrance_locations: List[Tuple[int, int]]):

self.entrances = entrance_locations

def calculate_slope(self, space: ParkingSpace, elevation_map: np.ndarray) -> float:

"""

Calculate the slope gradient for a parking space.

Returns the maximum slope across the space's footprint.

"""

x, y = space.location

width, height = space.dimensions

# Sample elevation points across the space

elevations = elevation_map[y:y+height, x:x+width]

# Calculate gradients in both directions

dx = np.gradient(elevations, axis=1)

dy = np.gradient(elevations, axis=0)

# Return maximum slope magnitude

return float(np.max(np.sqrt(dx**2 + dy**2)))

def distance_to_entrance(self, space: ParkingSpace) -> float:

"""

Calculate minimum distance to any accessible entrance.

"""

x, y = space.center

return min(

math.sqrt((x - ex)**2 + (y - ey)**2)

for ex, ey in self.entrances

)

def score_ada_compliance(

self,

space: ParkingSpace,

elevation_map: np.ndarray

) -> float:

"""

Score a parking space for ADA compliance.

Returns 0 for non-compliant spaces, 0-100 for compliant ones.

"""

slope = self.calculate_slope(space, elevation_map)

# Strict compliance check - slope must be <= 1:48

if slope > self.ADA_MAX_SLOPE:

return 0.0 # Non-compliant, cannot be used

# Calculate base score from slope (lower is better)

slope_score = (1 - slope / self.ADA_MAX_SLOPE) * 50

# Calculate proximity score (closer is better, max 50 points)

proximity = self.distance_to_entrance(space)

proximity_score = max(0, 50 - proximity * 0.5)

return slope_score + proximity_score

```

Key Design Decisions

1. **Strict Compliance First**: Non-compliant spaces get a score of 0, preventing them from being designated as accessible.

2. **Dual Scoring**: The final score combines:

  • Slope quality (50 points max) - flatter is better
  • Entrance proximity (50 points max) - closer is better

    • 3. **Gradient Calculation**: Using numpy's gradient function for efficient slope calculation across the entire space footprint.

    Integration Notes

    This class is designed to work with the existing `ParkingLotOptimizer` by plugging into the space scoring pipeline:

    ```python

    optimizer = ParkingLotOptimizer(config)

    accessibility_scorer = TerrainAccessibilityScorer(entrances)

    for space in optimizer.candidate_spaces:

    space.accessibility_score = accessibility_scorer.score_ada_compliance(

    space, elevation_map

    )

    ```

    Testing Recommendations

    The Tester should validate against:

    1. Known compliant/non-compliant test cases

    2. Edge cases at exactly 1:48 slope

    3. Various entrance configurations

    4. Performance on large parking lots (500+ spaces)

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