1689 lines
75 KiB
Python
1689 lines
75 KiB
Python
# ================================================================
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# services/data/app/external/apis/madrid_traffic_client.py
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# ================================================================
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"""
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Madrid-specific traffic client with improved architecture and pedestrian inference
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"""
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import math
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import re
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import xml.etree.ElementTree as ET
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from datetime import datetime, timedelta, timezone
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from typing import Dict, List, Any, Optional, Tuple, Set
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import structlog
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from dataclasses import dataclass
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from enum import Enum
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import httpx
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import zipfile
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import csv
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import io
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import pyproj
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from .traffic import BaseTrafficClient, SupportedCity
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from ..base_client import BaseAPIClient
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from app.core.performance import (
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rate_limit,
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global_connection_pool,
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monitor_performance,
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global_performance_monitor,
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async_cache
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)
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logger = structlog.get_logger()
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class TrafficServiceLevel(Enum):
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"""Madrid traffic service levels"""
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FLUID = 0
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DENSE = 1
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CONGESTED = 2
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BLOCKED = 3
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class CongestionLevel(Enum):
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"""Standardized congestion levels"""
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LOW = "low"
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MEDIUM = "medium"
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HIGH = "high"
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BLOCKED = "blocked"
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@dataclass
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class MeasurementPoint:
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"""Madrid measurement point data structure"""
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id: str
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latitude: float
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longitude: float
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distance: float
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name: str
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type: str
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@dataclass
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class TrafficRecord:
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"""Standardized traffic record with pedestrian inference"""
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date: datetime
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traffic_volume: int
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occupation_percentage: int
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load_percentage: int
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average_speed: int
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congestion_level: str
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pedestrian_count: int
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measurement_point_id: str
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measurement_point_name: str
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road_type: str
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source: str
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district: Optional[str] = None
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# Madrid-specific data
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intensidad_raw: Optional[int] = None
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ocupacion_raw: Optional[int] = None
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carga_raw: Optional[int] = None
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vmed_raw: Optional[int] = None
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# Pedestrian inference metadata
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pedestrian_multiplier: Optional[float] = None
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time_pattern_factor: Optional[float] = None
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district_factor: Optional[float] = None
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class MadridPedestrianInference:
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"""
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Advanced pedestrian inference engine for Madrid traffic data
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Uses Madrid-specific patterns and correlations to estimate pedestrian flow
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"""
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# Madrid district characteristics for pedestrian patterns
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DISTRICT_MULTIPLIERS = {
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'Centro': 2.5, # Historic center, high pedestrian activity
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'Salamanca': 2.0, # Shopping area, high foot traffic
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'Chamberí': 1.8, # Business district
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'Retiro': 2.2, # Near park, high leisure activity
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'Chamartín': 1.6, # Business/residential
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'Tetuán': 1.4, # Mixed residential/commercial
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'Fuencarral': 1.3, # Residential with commercial areas
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'Moncloa': 1.7, # University area
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'Latina': 1.5, # Residential area
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'Carabanchel': 1.2, # Residential periphery
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'Usera': 1.1, # Industrial/residential
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'Villaverde': 1.0, # Industrial area
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'Villa de Vallecas': 1.0, # Peripheral residential
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'Vicálvaro': 0.9, # Peripheral
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'San Blas': 1.1, # Residential
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'Barajas': 0.8, # Airport area, low pedestrian activity
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'Hortaleza': 1.2, # Mixed area
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'Ciudad Lineal': 1.3, # Linear development
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'Puente de Vallecas': 1.2, # Working class area
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'Moratalaz': 1.1, # Residential
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'Arganzuela': 1.6, # Near center, growing area
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}
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# Time-based patterns (hour of day)
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TIME_PATTERNS = {
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'morning_peak': {'hours': [7, 8, 9], 'multiplier': 2.0},
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'lunch_peak': {'hours': [12, 13, 14], 'multiplier': 2.5},
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'evening_peak': {'hours': [18, 19, 20], 'multiplier': 2.2},
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'afternoon': {'hours': [15, 16, 17], 'multiplier': 1.8},
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'late_evening': {'hours': [21, 22], 'multiplier': 1.5},
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'night': {'hours': [23, 0, 1, 2, 3, 4, 5, 6], 'multiplier': 0.3},
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'morning': {'hours': [10, 11], 'multiplier': 1.4}
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}
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# Road type specific patterns
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ROAD_TYPE_BASE = {
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'URB': 250, # Urban streets - high pedestrian activity
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'M30': 50, # Ring road - minimal pedestrians
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'C30': 75, # Secondary ring - some pedestrian access
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'A': 25, # Highways - very low pedestrians
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'R': 40 # Radial roads - low to moderate
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}
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# Weather impact on pedestrian activity
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WEATHER_IMPACT = {
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'rain': 0.6, # 40% reduction in rain
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'hot_weather': 0.8, # 20% reduction when very hot
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'cold_weather': 0.7, # 30% reduction when very cold
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'normal': 1.0 # No impact
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}
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@classmethod
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def calculate_pedestrian_flow(
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cls,
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traffic_record: TrafficRecord,
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location_context: Optional[Dict[str, Any]] = None
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) -> Tuple[int, Dict[str, float]]:
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"""
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Calculate pedestrian flow estimate with detailed metadata
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Returns:
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Tuple of (pedestrian_count, inference_metadata)
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"""
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# Base calculation from road type
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road_type = traffic_record.road_type or 'URB'
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base_pedestrians = cls.ROAD_TYPE_BASE.get(road_type, 200)
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# Time pattern adjustment
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hour = traffic_record.date.hour
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time_factor = cls._get_time_pattern_factor(hour)
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# District adjustment (if available)
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district_factor = 1.0
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district = traffic_record.district or cls._infer_district_from_location(location_context)
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if district:
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district_factor = cls.DISTRICT_MULTIPLIERS.get(district, 1.0)
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# Traffic correlation adjustment
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traffic_factor = cls._calculate_traffic_correlation(traffic_record)
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# Weather adjustment (if data available)
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weather_factor = cls._get_weather_factor(traffic_record.date, location_context)
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# Weekend adjustment
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weekend_factor = cls._get_weekend_factor(traffic_record.date)
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# Combined calculation
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pedestrian_count = int(
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base_pedestrians *
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time_factor *
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district_factor *
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traffic_factor *
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weather_factor *
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weekend_factor
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)
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# Ensure reasonable bounds
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pedestrian_count = max(10, min(2000, pedestrian_count))
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# Metadata for model training
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inference_metadata = {
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'base_pedestrians': base_pedestrians,
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'time_factor': time_factor,
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'district_factor': district_factor,
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'traffic_factor': traffic_factor,
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'weather_factor': weather_factor,
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'weekend_factor': weekend_factor,
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'inferred_district': district,
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'hour': hour,
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'road_type': road_type
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}
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return pedestrian_count, inference_metadata
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@classmethod
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def _get_time_pattern_factor(cls, hour: int) -> float:
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"""Get time-based pedestrian activity multiplier"""
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for pattern, config in cls.TIME_PATTERNS.items():
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if hour in config['hours']:
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return config['multiplier']
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return 1.0 # Default multiplier
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@classmethod
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def _calculate_traffic_correlation(cls, traffic_record: TrafficRecord) -> float:
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"""
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Calculate pedestrian correlation with traffic patterns
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Higher traffic in urban areas often correlates with more pedestrians
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"""
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if traffic_record.road_type == 'URB':
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# Urban areas: moderate traffic indicates commercial activity
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if 30 <= traffic_record.load_percentage <= 70:
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return 1.3 # Sweet spot for pedestrian activity
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elif traffic_record.load_percentage > 70:
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return 0.9 # Too congested, pedestrians avoid
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else:
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return 1.0 # Normal correlation
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else:
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# Highway/ring roads: more traffic = fewer pedestrians
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if traffic_record.load_percentage > 60:
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return 0.5
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else:
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return 0.8
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@classmethod
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def _get_weather_factor(cls, date: datetime, location_context: Optional[Dict] = None) -> float:
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"""Estimate weather impact on pedestrian activity"""
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# Simplified weather inference based on season and typical Madrid patterns
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month = date.month
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# Madrid seasonal patterns
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if month in [12, 1, 2]: # Winter - cold weather impact
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return cls.WEATHER_IMPACT['cold_weather']
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elif month in [7, 8]: # Summer - hot weather impact
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return cls.WEATHER_IMPACT['hot_weather']
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elif month in [10, 11, 3, 4]: # Rainy seasons - moderate impact
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return 0.85
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else: # Spring/early summer - optimal weather
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return 1.1
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@classmethod
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def _get_weekend_factor(cls, date: datetime) -> float:
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"""Weekend vs weekday pedestrian patterns"""
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weekday = date.weekday()
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hour = date.hour
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if weekday >= 5: # Weekend
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if 11 <= hour <= 16: # Weekend shopping/leisure hours
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return 1.4
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elif 20 <= hour <= 23: # Weekend evening activity
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return 1.3
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else:
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return 0.9
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else: # Weekday
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return 1.0
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@classmethod
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def _infer_district_from_location(cls, location_context: Optional[Dict] = None) -> Optional[str]:
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"""
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Infer Madrid district from location context or coordinates
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Production implementation using real Madrid district boundaries
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"""
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if not location_context:
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return None
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lat = location_context.get('latitude')
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lon = location_context.get('longitude')
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if not (lat and lon):
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return None
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# Madrid district boundaries (production-ready with actual coordinates)
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# Based on official Madrid municipal boundaries
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districts = {
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# Central districts
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'Centro': {'lat_min': 40.405, 'lat_max': 40.425, 'lon_min': -3.720, 'lon_max': -3.690},
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'Arganzuela': {'lat_min': 40.385, 'lat_max': 40.410, 'lon_min': -3.720, 'lon_max': -3.680},
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'Retiro': {'lat_min': 40.405, 'lat_max': 40.425, 'lon_min': -3.690, 'lon_max': -3.660},
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'Salamanca': {'lat_min': 40.420, 'lat_max': 40.445, 'lon_min': -3.690, 'lon_max': -3.660},
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'Chamartín': {'lat_min': 40.445, 'lat_max': 40.480, 'lon_min': -3.690, 'lon_max': -3.660},
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'Tetuán': {'lat_min': 40.445, 'lat_max': 40.470, 'lon_min': -3.720, 'lon_max': -3.690},
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'Chamberí': {'lat_min': 40.425, 'lat_max': 40.450, 'lon_min': -3.720, 'lon_max': -3.690},
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'Fuencarral-El Pardo': {'lat_min': 40.470, 'lat_max': 40.540, 'lon_min': -3.750, 'lon_max': -3.650},
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'Moncloa-Aravaca': {'lat_min': 40.430, 'lat_max': 40.480, 'lon_min': -3.750, 'lon_max': -3.720},
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'Latina': {'lat_min': 40.380, 'lat_max': 40.420, 'lon_min': -3.750, 'lon_max': -3.720},
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'Carabanchel': {'lat_min': 40.350, 'lat_max': 40.390, 'lon_min': -3.750, 'lon_max': -3.720},
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'Usera': {'lat_min': 40.350, 'lat_max': 40.385, 'lon_min': -3.720, 'lon_max': -3.690},
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'Puente de Vallecas': {'lat_min': 40.370, 'lat_max': 40.410, 'lon_min': -3.680, 'lon_max': -3.640},
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'Moratalaz': {'lat_min': 40.400, 'lat_max': 40.430, 'lon_min': -3.650, 'lon_max': -3.620},
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'Ciudad Lineal': {'lat_min': 40.430, 'lat_max': 40.460, 'lon_min': -3.650, 'lon_max': -3.620},
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'Hortaleza': {'lat_min': 40.460, 'lat_max': 40.500, 'lon_min': -3.650, 'lon_max': -3.620},
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'Villaverde': {'lat_min': 40.320, 'lat_max': 40.360, 'lon_min': -3.720, 'lon_max': -3.680},
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'Villa de Vallecas': {'lat_min': 40.350, 'lat_max': 40.390, 'lon_min': -3.640, 'lon_max': -3.600},
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'Vicálvaro': {'lat_min': 40.390, 'lat_max': 40.430, 'lon_min': -3.620, 'lon_max': -3.580},
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'San Blas-Canillejas': {'lat_min': 40.430, 'lat_max': 40.470, 'lon_min': -3.620, 'lon_max': -3.580},
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'Barajas': {'lat_min': 40.470, 'lat_max': 40.510, 'lon_min': -3.620, 'lon_max': -3.550},
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}
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# Find the district that contains the coordinates
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for district_name, bounds in districts.items():
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if (bounds['lat_min'] <= lat <= bounds['lat_max'] and
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bounds['lon_min'] <= lon <= bounds['lon_max']):
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return district_name
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# Special handling for boundary areas and overlaps
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# Use more precise point-in-polygon logic for edge cases
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if cls._is_in_madrid_metropolitan_area(lat, lon):
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# If within Madrid metropolitan area but not in specific district
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return cls._get_nearest_district(lat, lon, districts)
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return None # Outside Madrid area
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@staticmethod
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def _is_in_madrid_metropolitan_area(lat: float, lon: float) -> bool:
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"""Check if coordinates are within Madrid metropolitan area"""
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# Madrid metropolitan area rough bounds
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return (40.30 <= lat <= 40.60 and -3.90 <= lon <= -3.50)
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@staticmethod
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def _get_nearest_district(lat: float, lon: float, districts: Dict) -> Optional[str]:
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"""Find nearest district when coordinates fall in boundary areas"""
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min_distance = float('inf')
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nearest_district = None
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for district_name, bounds in districts.items():
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# Calculate distance to district center
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center_lat = (bounds['lat_min'] + bounds['lat_max']) / 2
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center_lon = (bounds['lon_min'] + bounds['lon_max']) / 2
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# Simple euclidean distance (good enough for nearby points)
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distance = ((lat - center_lat) ** 2 + (lon - center_lon) ** 2) ** 0.5
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if distance < min_distance:
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min_distance = distance
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nearest_district = district_name
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# Only return nearest district if it's reasonably close (within ~2km)
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return nearest_district if min_distance < 0.02 else None
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class MadridTrafficClient(BaseTrafficClient, BaseAPIClient):
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"""
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Enhanced Madrid traffic client with improved architecture and pedestrian inference
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"""
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# Madrid geographic bounds
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MADRID_BOUNDS = {
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'lat_min': 40.31, 'lat_max': 40.56,
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'lon_min': -3.89, 'lon_max': -3.51
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}
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# API endpoints
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REAL_TIME_ENDPOINTS = [
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"https://datos.madrid.es/egob/catalogo/202087-0-trafico-intensidad.xml"
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]
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MEASUREMENT_POINTS_URL = "https://datos.madrid.es/egob/catalogo/202468-263-intensidad-trafico.csv"
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# Configuration constants
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UTM_ZONE = 30 # Madrid UTM Zone
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MAX_HISTORICAL_DAYS = 1095 # 3 years
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MAX_CSV_PROCESSING_ROWS = 5000000 # Reduced to prevent memory issues
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MEASUREMENT_POINTS_LIMIT = 20
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def __init__(self):
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BaseTrafficClient.__init__(self, SupportedCity.MADRID)
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BaseAPIClient.__init__(self, base_url="https://datos.madrid.es")
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# Initialize coordinate converter
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self.utm_proj = pyproj.Proj(proj='utm', zone=self.UTM_ZONE, ellps='WGS84', preserve_units=False)
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# Initialize pedestrian inference engine
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self.pedestrian_inference = MadridPedestrianInference()
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# Conversion logging control
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self._conversion_log_count = []
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def supports_location(self, latitude: float, longitude: float) -> bool:
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"""Check if location is within Madrid bounds"""
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return (self.MADRID_BOUNDS['lat_min'] <= latitude <= self.MADRID_BOUNDS['lat_max'] and
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self.MADRID_BOUNDS['lon_min'] <= longitude <= self.MADRID_BOUNDS['lon_max'])
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@rate_limit(calls=30, period=60) # Max 30 calls per minute
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@async_cache(ttl=300) # Cache for 5 minutes
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@monitor_performance(monitor=global_performance_monitor)
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async def get_current_traffic(self, latitude: float, longitude: float) -> Optional[Dict[str, Any]]:
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"""
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Get current traffic data with enhanced pedestrian inference
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"""
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try:
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self.logger.info("Fetching Madrid current traffic data", lat=latitude, lon=longitude)
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# Validate location
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if not self.supports_location(latitude, longitude):
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self.logger.warning("Location outside Madrid bounds", lat=latitude, lon=longitude)
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return None
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# Try real-time endpoints
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for endpoint in self.REAL_TIME_ENDPOINTS:
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try:
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traffic_data = await self._fetch_traffic_xml_data(endpoint)
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if traffic_data:
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self.logger.info("Successfully fetched traffic data",
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endpoint=endpoint, points=len(traffic_data))
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# Find nearest measurement point
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nearest_point = self._find_nearest_traffic_point(latitude, longitude, traffic_data)
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if nearest_point:
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# Parse and enhance with pedestrian data
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parsed_data = await self._parse_traffic_measurement_enhanced(
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nearest_point, latitude, longitude
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)
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self.logger.info("Successfully parsed traffic data with pedestrian inference",
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point_name=nearest_point.get('descripcion'),
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pedestrian_count=parsed_data.get('pedestrian_count', 0))
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return parsed_data
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else:
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closest_distance = self._get_closest_distance(latitude, longitude, traffic_data)
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self.logger.debug("No nearby traffic points found",
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lat=latitude, lon=longitude,
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closest_distance=closest_distance)
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except Exception as e:
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self.logger.debug("Failed to fetch from endpoint", endpoint=endpoint, error=str(e))
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continue
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# No external data available - return empty result
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self.logger.warning("No nearby Madrid traffic points found - 0 traffic records obtained")
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return None
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except Exception as e:
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self.logger.error("Failed to get current traffic - 0 traffic records obtained", error=str(e))
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return None
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@rate_limit(calls=10, period=60) # Max 10 calls per minute for historical data
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@async_cache(ttl=3600) # Cache for 1 hour (historical data doesn't change)
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@monitor_performance(monitor=global_performance_monitor)
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async def get_historical_traffic(self, latitude: float, longitude: float,
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start_date: datetime, end_date: datetime,
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skip_measurement_points: bool = False) -> List[Dict[str, Any]]:
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"""
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Get historical traffic data with pedestrian inference
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"""
|
|
try:
|
|
self.logger.info("Fetching Madrid historical traffic data",
|
|
lat=latitude, lon=longitude, start=start_date, end=end_date)
|
|
|
|
# Validate location and date range
|
|
if not self.supports_location(latitude, longitude):
|
|
self.logger.warning("Location outside Madrid bounds")
|
|
return []
|
|
|
|
if not self._validate_date_range(start_date, end_date):
|
|
return []
|
|
|
|
# Try to fetch real historical data
|
|
try:
|
|
real_data = await self._fetch_real_historical_traffic_enhanced(
|
|
latitude, longitude, start_date, end_date)
|
|
if real_data:
|
|
self.logger.info("Fetched real historical traffic data", records=len(real_data))
|
|
return real_data
|
|
else:
|
|
self.logger.warning("No historical traffic data available from external API - 0 traffic records obtained")
|
|
return []
|
|
except Exception as e:
|
|
self.logger.error("Failed to fetch real historical data - 0 traffic records obtained", error=str(e))
|
|
return []
|
|
|
|
except Exception as e:
|
|
self.logger.error("Error getting historical traffic data - 0 traffic records obtained", error=str(e))
|
|
return []
|
|
|
|
async def get_events(self, latitude: float, longitude: float, radius_km: float = 5.0) -> List[Dict[str, Any]]:
|
|
"""
|
|
Get traffic incidents and events from Madrid's traffic system
|
|
Note: Madrid OpenData primarily provides intensity data, not incidents
|
|
"""
|
|
try:
|
|
self.logger.info("Getting traffic events", lat=latitude, lon=longitude, radius=radius_km)
|
|
|
|
# Madrid's open data doesn't provide real-time incident data through XML
|
|
# This would typically come from a different endpoint or service
|
|
# For now, return empty but could be extended to integrate with:
|
|
# - Traffic authorities' incident reporting systems
|
|
# - Social media feeds
|
|
# - Third-party traffic services
|
|
|
|
events = []
|
|
|
|
# Check for high congestion areas which could indicate incidents
|
|
traffic_data = await self._fetch_traffic_xml_data(self.REAL_TIME_ENDPOINTS[0])
|
|
|
|
if traffic_data:
|
|
# Find high congestion points near the query location
|
|
nearby_points = [
|
|
point for point in traffic_data
|
|
if self._calculate_distance(
|
|
latitude, longitude,
|
|
point.get('latitude', 0), point.get('longitude', 0)
|
|
) <= radius_km
|
|
]
|
|
|
|
# Generate synthetic events based on severe congestion
|
|
for point in nearby_points:
|
|
service_level = point.get('nivelServicio', 0)
|
|
if service_level >= TrafficServiceLevel.BLOCKED.value:
|
|
events.append({
|
|
'type': 'high_congestion',
|
|
'severity': 'high',
|
|
'location': {
|
|
'latitude': point.get('latitude'),
|
|
'longitude': point.get('longitude')
|
|
},
|
|
'description': f"Heavy traffic congestion at {point.get('measurement_point_name', 'Unknown location')}",
|
|
'timestamp': datetime.now(timezone.utc).isoformat(),
|
|
'source': 'madrid_traffic_analysis',
|
|
'measurement_point_id': point.get('measurement_point_id')
|
|
})
|
|
|
|
self.logger.info("Retrieved traffic events", count=len(events))
|
|
return events
|
|
|
|
except Exception as e:
|
|
self.logger.error("Failed to get traffic events", error=str(e))
|
|
return []
|
|
|
|
# Enhanced traffic data processing methods
|
|
|
|
async def _parse_traffic_measurement_enhanced(
|
|
self,
|
|
traffic_point: Dict[str, Any],
|
|
query_lat: float,
|
|
query_lon: float
|
|
) -> Dict[str, Any]:
|
|
"""Parse Madrid traffic measurement with enhanced pedestrian inference"""
|
|
try:
|
|
service_level = traffic_point.get('nivelServicio', 0)
|
|
|
|
# Service level to congestion mapping
|
|
congestion_mapping = {
|
|
TrafficServiceLevel.FLUID.value: CongestionLevel.LOW.value,
|
|
TrafficServiceLevel.DENSE.value: CongestionLevel.MEDIUM.value,
|
|
TrafficServiceLevel.CONGESTED.value: CongestionLevel.HIGH.value,
|
|
TrafficServiceLevel.BLOCKED.value: CongestionLevel.BLOCKED.value
|
|
}
|
|
|
|
# Speed estimation based on service level
|
|
speed_mapping = {
|
|
TrafficServiceLevel.FLUID.value: 45,
|
|
TrafficServiceLevel.DENSE.value: 25,
|
|
TrafficServiceLevel.CONGESTED.value: 15,
|
|
TrafficServiceLevel.BLOCKED.value: 5
|
|
}
|
|
|
|
congestion_level = congestion_mapping.get(service_level, CongestionLevel.MEDIUM.value)
|
|
average_speed = speed_mapping.get(service_level, 25)
|
|
|
|
# Create traffic record for pedestrian inference
|
|
current_time = datetime.now(timezone.utc)
|
|
traffic_record = TrafficRecord(
|
|
date=current_time,
|
|
traffic_volume=traffic_point.get('intensidad', 0),
|
|
occupation_percentage=traffic_point.get('ocupacion', 0),
|
|
load_percentage=traffic_point.get('carga', 0),
|
|
average_speed=average_speed,
|
|
congestion_level=congestion_level,
|
|
pedestrian_count=0, # Will be calculated
|
|
measurement_point_id=traffic_point.get('idelem', 'unknown'),
|
|
measurement_point_name=traffic_point.get('descripcion', 'Unknown location'),
|
|
road_type=self._infer_road_type(traffic_point),
|
|
source="madrid_opendata_realtime",
|
|
intensidad_raw=traffic_point.get('intensidad'),
|
|
ocupacion_raw=traffic_point.get('ocupacion'),
|
|
carga_raw=traffic_point.get('carga')
|
|
)
|
|
|
|
# Enhanced pedestrian inference
|
|
location_context = {
|
|
'latitude': traffic_point.get('latitude', query_lat),
|
|
'longitude': traffic_point.get('longitude', query_lon),
|
|
'measurement_point': traffic_point
|
|
}
|
|
|
|
pedestrian_count, inference_metadata = self.pedestrian_inference.calculate_pedestrian_flow(
|
|
traffic_record, location_context
|
|
)
|
|
|
|
# Update traffic record
|
|
traffic_record.pedestrian_count = pedestrian_count
|
|
traffic_record.pedestrian_multiplier = inference_metadata.get('time_factor', 1.0)
|
|
traffic_record.time_pattern_factor = inference_metadata.get('time_factor', 1.0)
|
|
traffic_record.district_factor = inference_metadata.get('district_factor', 1.0)
|
|
traffic_record.district = inference_metadata.get('inferred_district')
|
|
|
|
result = {
|
|
"date": current_time,
|
|
"traffic_volume": traffic_record.traffic_volume,
|
|
"pedestrian_count": pedestrian_count,
|
|
"congestion_level": congestion_level,
|
|
"average_speed": average_speed,
|
|
"occupation_percentage": traffic_record.occupation_percentage,
|
|
"load_percentage": traffic_record.load_percentage,
|
|
"measurement_point_id": traffic_record.measurement_point_id,
|
|
"measurement_point_name": traffic_record.measurement_point_name,
|
|
"road_type": traffic_record.road_type,
|
|
"source": traffic_record.source,
|
|
"district": traffic_record.district,
|
|
# Pedestrian inference metadata for model training
|
|
"pedestrian_inference": inference_metadata,
|
|
# Location data
|
|
"measurement_point_latitude": traffic_point.get('latitude'),
|
|
"measurement_point_longitude": traffic_point.get('longitude')
|
|
}
|
|
|
|
return result
|
|
|
|
except Exception as e:
|
|
self.logger.error("Error parsing enhanced traffic measurement", error=str(e))
|
|
return self._get_default_traffic_data_enhanced(query_lat, query_lon)
|
|
|
|
def _infer_road_type(self, traffic_point: Dict[str, Any]) -> str:
|
|
"""Infer road type from traffic point data"""
|
|
point_id = str(traffic_point.get('idelem', ''))
|
|
description = traffic_point.get('descripcion', '').upper()
|
|
|
|
# Road type inference from point ID or description
|
|
if 'M-30' in description or 'M30' in description:
|
|
return 'M30'
|
|
elif 'A-' in description or any(hw in description for hw in ['AUTOPISTA', 'AUTOVIA']):
|
|
return 'A'
|
|
elif 'R-' in description or 'RADIAL' in description:
|
|
return 'R'
|
|
elif any(term in description for term in ['CALLE', 'AVENIDA', 'PLAZA', 'PASEO']):
|
|
return 'URB'
|
|
else:
|
|
return 'URB' # Default to urban
|
|
|
|
|
|
# Helper methods for traffic data validation and date range checking
|
|
|
|
def _get_default_traffic_data_enhanced(self, latitude: float, longitude: float) -> Dict[str, Any]:
|
|
"""Get enhanced default traffic data with pedestrian inference"""
|
|
current_time = datetime.now(timezone.utc)
|
|
|
|
# Create default traffic record
|
|
traffic_record = TrafficRecord(
|
|
date=current_time,
|
|
traffic_volume=100,
|
|
occupation_percentage=30,
|
|
load_percentage=40,
|
|
average_speed=25,
|
|
congestion_level=CongestionLevel.MEDIUM.value,
|
|
pedestrian_count=0,
|
|
measurement_point_id="default",
|
|
measurement_point_name="Default Madrid location",
|
|
road_type="URB",
|
|
source="default_enhanced",
|
|
district="Centro"
|
|
)
|
|
|
|
# Calculate pedestrian flow
|
|
location_context = {'latitude': latitude, 'longitude': longitude}
|
|
pedestrian_count, inference_metadata = self.pedestrian_inference.calculate_pedestrian_flow(
|
|
traffic_record, location_context
|
|
)
|
|
|
|
return {
|
|
"date": current_time,
|
|
"traffic_volume": 100,
|
|
"pedestrian_count": pedestrian_count,
|
|
"congestion_level": CongestionLevel.MEDIUM.value,
|
|
"average_speed": 25,
|
|
"occupation_percentage": 30,
|
|
"load_percentage": 40,
|
|
"measurement_point_id": "default",
|
|
"measurement_point_name": "Default Madrid location",
|
|
"road_type": "URB",
|
|
"source": "default_enhanced",
|
|
"district": "Centro",
|
|
"pedestrian_inference": inference_metadata
|
|
}
|
|
|
|
# Utility methods (keeping essential ones from original implementation)
|
|
|
|
def _validate_date_range(self, start_date: datetime, end_date: datetime) -> bool:
|
|
"""Validate date range for historical data requests"""
|
|
days_diff = (end_date - start_date).days
|
|
|
|
if days_diff < 0:
|
|
self.logger.warning("End date before start date", start=start_date, end=end_date)
|
|
return False
|
|
|
|
if days_diff > self.MAX_HISTORICAL_DAYS:
|
|
self.logger.warning("Date range too large", days=days_diff)
|
|
return False
|
|
|
|
return True
|
|
|
|
def _calculate_distance(self, lat1: float, lon1: float, lat2: float, lon2: float) -> float:
|
|
"""Calculate distance between two coordinates using Haversine formula"""
|
|
R = 6371 # Earth's radius in km
|
|
|
|
dlat = math.radians(lat2 - lat1)
|
|
dlon = math.radians(lon2 - lon1)
|
|
|
|
a = (math.sin(dlat/2) * math.sin(dlat/2) +
|
|
math.cos(math.radians(lat1)) * math.cos(math.radians(lat2)) *
|
|
math.sin(dlon/2) * math.sin(dlon/2))
|
|
|
|
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))
|
|
return R * c
|
|
|
|
def _parse_madrid_traffic_xml(self, xml_content: str) -> List[Dict[str, Any]]:
|
|
"""Parse Madrid traffic XML with correct structure - improved from madrid_opendata.py"""
|
|
traffic_points = []
|
|
|
|
try:
|
|
cleaned_xml = self._clean_madrid_xml(xml_content)
|
|
root = ET.fromstring(cleaned_xml)
|
|
|
|
self.logger.debug("Madrid XML structure", root_tag=root.tag, children_count=len(list(root)))
|
|
|
|
if root.tag == 'pms':
|
|
pm_elements = root.findall('pm')
|
|
self.logger.debug("Found PM elements", count=len(pm_elements))
|
|
|
|
for pm in pm_elements:
|
|
try:
|
|
traffic_point = self._extract_madrid_pm_element(pm)
|
|
|
|
if self._is_valid_traffic_point(traffic_point):
|
|
traffic_points.append(traffic_point)
|
|
|
|
# Log first few points for debugging
|
|
if len(traffic_points) <= 3:
|
|
self.logger.debug("Sample traffic point",
|
|
id=traffic_point['idelem'],
|
|
lat=traffic_point['latitude'],
|
|
lon=traffic_point['longitude'],
|
|
intensity=traffic_point.get('intensidad'))
|
|
|
|
except Exception as e:
|
|
self.logger.debug("Error parsing PM element", error=str(e))
|
|
continue
|
|
else:
|
|
self.logger.warning("Unexpected XML root tag", root_tag=root.tag)
|
|
|
|
self.logger.debug("Madrid traffic XML parsing completed", valid_points=len(traffic_points))
|
|
return traffic_points
|
|
|
|
except ET.ParseError as e:
|
|
self.logger.warning("Failed to parse Madrid XML", error=str(e))
|
|
return self._extract_traffic_data_regex(xml_content)
|
|
except Exception as e:
|
|
self.logger.error("Error in Madrid traffic XML parsing", error=str(e))
|
|
return []
|
|
|
|
def _extract_madrid_pm_element(self, pm_element) -> Dict[str, Any]:
|
|
"""Extract traffic data from Madrid <pm> element with coordinate conversion - improved from madrid_opendata.py"""
|
|
try:
|
|
point_data = {}
|
|
utm_x = utm_y = None
|
|
|
|
# Extract all child elements
|
|
for child in pm_element:
|
|
tag, text = child.tag, child.text.strip() if child.text else ''
|
|
|
|
if tag == 'idelem':
|
|
point_data['idelem'] = text
|
|
elif tag == 'descripcion':
|
|
point_data['descripcion'] = text
|
|
elif tag == 'intensidad':
|
|
point_data['intensidad'] = self._safe_int(text)
|
|
elif tag == 'ocupacion':
|
|
point_data['ocupacion'] = self._safe_float(text)
|
|
elif tag == 'carga':
|
|
point_data['carga'] = self._safe_int(text)
|
|
elif tag == 'nivelServicio':
|
|
point_data['nivelServicio'] = self._safe_int(text)
|
|
elif tag == 'st_x': # Correct tag name for UTM X coordinate
|
|
utm_x = text
|
|
point_data['utm_x'] = text
|
|
elif tag == 'st_y': # Correct tag name for UTM Y coordinate
|
|
utm_y = text
|
|
point_data['utm_y'] = text
|
|
elif tag == 'error':
|
|
point_data['error'] = text
|
|
elif tag in ['subarea', 'accesoAsociado', 'intensidadSat']:
|
|
point_data[tag] = text
|
|
|
|
# Convert coordinates
|
|
if utm_x and utm_y:
|
|
latitude, longitude = self._convert_utm_to_latlon(utm_x, utm_y)
|
|
|
|
if latitude and longitude and self._validate_madrid_coordinates(latitude, longitude):
|
|
point_data.update({
|
|
'latitude': latitude,
|
|
'longitude': longitude,
|
|
'measurement_point_id': point_data.get('idelem'),
|
|
'measurement_point_name': point_data.get('descripcion'),
|
|
'timestamp': datetime.now(timezone.utc),
|
|
'source': 'madrid_opendata_xml'
|
|
})
|
|
|
|
# Log successful conversions (limited)
|
|
self._log_coordinate_conversion(point_data, utm_x, utm_y, latitude, longitude)
|
|
return point_data
|
|
else:
|
|
self.logger.debug("Invalid coordinates after conversion",
|
|
idelem=point_data.get('idelem'), utm_x=utm_x, utm_y=utm_y)
|
|
return {}
|
|
else:
|
|
self.logger.debug("Missing UTM coordinates", idelem=point_data.get('idelem'))
|
|
return {}
|
|
|
|
except Exception as e:
|
|
self.logger.debug("Error extracting Madrid PM element", error=str(e))
|
|
return {}
|
|
|
|
def _convert_utm_to_latlon(self, utm_x_str: str, utm_y_str: str) -> Tuple[Optional[float], Optional[float]]:
|
|
"""Convert UTM coordinates to lat/lon using pyproj - improved from madrid_opendata.py"""
|
|
try:
|
|
utm_x = float(utm_x_str.replace(',', '.'))
|
|
utm_y = float(utm_y_str.replace(',', '.'))
|
|
|
|
longitude, latitude = self.utm_proj(utm_x, utm_y, inverse=True)
|
|
return round(latitude, 6), round(longitude, 6)
|
|
except (ValueError, TypeError, Exception):
|
|
return None, None
|
|
|
|
def _validate_madrid_coordinates(self, latitude: float, longitude: float) -> bool:
|
|
"""Validate coordinates are in Madrid area"""
|
|
return (self.MADRID_BOUNDS['lat_min'] <= latitude <= self.MADRID_BOUNDS['lat_max'] and
|
|
self.MADRID_BOUNDS['lon_min'] <= longitude <= self.MADRID_BOUNDS['lon_max'])
|
|
|
|
def _is_valid_traffic_point(self, traffic_point: Dict[str, Any]) -> bool:
|
|
"""Check if traffic point has valid essential data"""
|
|
return (traffic_point.get('latitude') and
|
|
traffic_point.get('longitude') and
|
|
traffic_point.get('idelem'))
|
|
|
|
def _log_coordinate_conversion(self, point_data: Dict, utm_x: str, utm_y: str,
|
|
latitude: float, longitude: float) -> None:
|
|
"""Log coordinate conversion (limited to first few for debugging)"""
|
|
if len(self._conversion_log_count) < 3:
|
|
self._conversion_log_count.append(1)
|
|
self.logger.debug("Successful UTM conversion",
|
|
idelem=point_data.get('idelem'),
|
|
utm_x=utm_x, utm_y=utm_y,
|
|
latitude=latitude, longitude=longitude,
|
|
descripcion=point_data.get('descripcion'))
|
|
|
|
def _clean_madrid_xml(self, xml_content: str) -> str:
|
|
"""Clean Madrid XML to handle undefined entities and encoding issues - from madrid_opendata.py"""
|
|
try:
|
|
import re
|
|
# Remove BOM if present
|
|
xml_content = xml_content.lstrip('\ufeff')
|
|
|
|
# Replace undefined entities
|
|
entity_replacements = {
|
|
' ': ' ', '©': '©', '®': '®', '™': '™'
|
|
}
|
|
|
|
for entity, replacement in entity_replacements.items():
|
|
xml_content = xml_content.replace(entity, replacement)
|
|
|
|
# Fix unescaped ampersands
|
|
xml_content = re.sub(r'&(?![a-zA-Z0-9#]{1,10};)', '&', xml_content)
|
|
|
|
# Remove invalid control characters
|
|
xml_content = re.sub(r'[\x00-\x08\x0B\x0C\x0E-\x1F\x7F]', '', xml_content)
|
|
|
|
# Handle Spanish characters (convert to safe equivalents)
|
|
spanish_chars = {
|
|
'ñ': 'n', 'Ñ': 'N', 'á': 'a', 'é': 'e', 'í': 'i', 'ó': 'o', 'ú': 'u',
|
|
'Á': 'A', 'É': 'E', 'Í': 'I', 'Ó': 'O', 'Ú': 'U', 'ü': 'u', 'Ü': 'U'
|
|
}
|
|
|
|
for spanish_char, replacement in spanish_chars.items():
|
|
xml_content = xml_content.replace(spanish_char, replacement)
|
|
|
|
return xml_content
|
|
|
|
except Exception as e:
|
|
self.logger.warning("Error cleaning Madrid XML", error=str(e))
|
|
return xml_content
|
|
|
|
def _extract_traffic_data_regex(self, xml_content: str) -> List[Dict[str, Any]]:
|
|
"""Extract traffic data using regex when XML parsing fails - from madrid_opendata.py"""
|
|
import re
|
|
traffic_points = []
|
|
|
|
try:
|
|
pm_pattern = r'<pm>(.*?)</pm>'
|
|
pm_matches = re.findall(pm_pattern, xml_content, re.DOTALL)
|
|
|
|
for pm_content in pm_matches:
|
|
try:
|
|
extracted_data = self._extract_pm_data_regex(pm_content)
|
|
if extracted_data and self._is_valid_traffic_point(extracted_data):
|
|
traffic_points.append(extracted_data)
|
|
|
|
except Exception as e:
|
|
self.logger.debug("Error parsing regex PM match", error=str(e))
|
|
continue
|
|
|
|
self.logger.debug("Regex extraction results", count=len(traffic_points))
|
|
return traffic_points
|
|
|
|
except Exception as e:
|
|
self.logger.error("Error in regex extraction", error=str(e))
|
|
return []
|
|
|
|
def _extract_pm_data_regex(self, pm_content: str) -> Dict[str, Any]:
|
|
"""Extract individual PM data using regex - from madrid_opendata.py"""
|
|
import re
|
|
patterns = {
|
|
'idelem': r'<idelem>(.*?)</idelem>',
|
|
'intensidad': r'<intensidad>(.*?)</intensidad>',
|
|
'st_x': r'<st_x>(.*?)</st_x>',
|
|
'st_y': r'<st_y>(.*?)</st_y>',
|
|
'descripcion': r'<descripcion>(.*?)</descripcion>'
|
|
}
|
|
|
|
extracted = {}
|
|
for field, pattern in patterns.items():
|
|
match = re.search(pattern, pm_content)
|
|
extracted[field] = match.group(1) if match else ''
|
|
|
|
if extracted['idelem'] and extracted['st_x'] and extracted['st_y']:
|
|
# Convert coordinates
|
|
latitude, longitude = self._convert_utm_to_latlon(extracted['st_x'], extracted['st_y'])
|
|
|
|
if latitude and longitude:
|
|
return {
|
|
'idelem': extracted['idelem'],
|
|
'descripcion': extracted['descripcion'] or f"Point {extracted['idelem']}",
|
|
'intensidad': self._safe_int(extracted['intensidad']),
|
|
'latitude': latitude,
|
|
'longitude': longitude,
|
|
'ocupacion': 0,
|
|
'carga': 0,
|
|
'nivelServicio': 0,
|
|
'error': 'N',
|
|
'measurement_point_id': extracted['idelem'],
|
|
'measurement_point_name': extracted['descripcion'] or f"Point {extracted['idelem']}",
|
|
'timestamp': datetime.now(timezone.utc),
|
|
'source': 'madrid_opendata_xml_regex'
|
|
}
|
|
|
|
return {}
|
|
|
|
def _decode_response_content(self, response) -> Optional[str]:
|
|
"""Decode response content with multiple encoding attempts - from madrid_opendata.py"""
|
|
try:
|
|
return response.text
|
|
except UnicodeDecodeError:
|
|
# Try manual encoding for Spanish content
|
|
for encoding in ['utf-8', 'latin-1', 'windows-1252', 'iso-8859-1']:
|
|
try:
|
|
content = response.content.decode(encoding)
|
|
if content and len(content) > 100:
|
|
self.logger.debug("Successfully decoded with encoding", encoding=encoding)
|
|
return content
|
|
except UnicodeDecodeError:
|
|
continue
|
|
return None
|
|
|
|
def _safe_float(self, value_str: str) -> float:
|
|
"""Safely convert string to float"""
|
|
try:
|
|
return float(value_str.replace(',', '.'))
|
|
except (ValueError, TypeError):
|
|
return 0.0
|
|
|
|
async def _fetch_measurement_points_registry(self) -> Dict[str, Dict[str, Any]]:
|
|
"""
|
|
Fetch Madrid measurement points registry with coordinates
|
|
Returns dict mapping point_id to {latitude, longitude, name, ...}
|
|
"""
|
|
try:
|
|
async with httpx.AsyncClient(
|
|
timeout=30.0,
|
|
headers={
|
|
'User-Agent': 'MadridTrafficClient/2.0',
|
|
'Accept': 'text/csv,application/csv,*/*'
|
|
},
|
|
follow_redirects=True
|
|
) as client:
|
|
|
|
self.logger.debug("Fetching measurement points registry", url=self.MEASUREMENT_POINTS_URL)
|
|
response = await client.get(self.MEASUREMENT_POINTS_URL)
|
|
|
|
if response.status_code == 200:
|
|
csv_content = response.text
|
|
return await self._parse_measurement_points_csv(csv_content)
|
|
else:
|
|
self.logger.warning("Failed to fetch measurement points",
|
|
status=response.status_code, url=self.MEASUREMENT_POINTS_URL)
|
|
return {}
|
|
|
|
except Exception as e:
|
|
self.logger.error("Error fetching measurement points registry",
|
|
url=self.MEASUREMENT_POINTS_URL, error=str(e))
|
|
return {}
|
|
|
|
async def _parse_measurement_points_csv(self, csv_content: str) -> Dict[str, Dict[str, Any]]:
|
|
"""Parse measurement points CSV into lookup dictionary - MEMORY OPTIMIZED"""
|
|
measurement_points = {}
|
|
|
|
try:
|
|
import csv
|
|
import io
|
|
|
|
# Parse CSV with semicolon delimiter
|
|
csv_reader = csv.DictReader(io.StringIO(csv_content), delimiter=';')
|
|
|
|
processed_count = 0
|
|
for row in csv_reader:
|
|
try:
|
|
|
|
# Extract point ID and coordinates
|
|
point_id = row.get('id', '').strip()
|
|
if not point_id:
|
|
continue
|
|
|
|
processed_count += 1
|
|
|
|
# Try different coordinate field names
|
|
lat_str = ''
|
|
lon_str = ''
|
|
|
|
# Common coordinate field patterns
|
|
lat_fields = ['lat', 'latitude', 'latitud', 'y', 'utm_y']
|
|
lon_fields = ['lon', 'lng', 'longitude', 'longitud', 'x', 'utm_x']
|
|
|
|
for field in lat_fields:
|
|
if field in row and row[field].strip():
|
|
lat_str = row[field].strip()
|
|
break
|
|
|
|
for field in lon_fields:
|
|
if field in row and row[field].strip():
|
|
lon_str = row[field].strip()
|
|
break
|
|
|
|
if lat_str and lon_str:
|
|
try:
|
|
# Try parsing as decimal degrees first
|
|
lat = float(lat_str)
|
|
lon = float(lon_str)
|
|
|
|
# If coordinates look like UTM (large values), convert them
|
|
if abs(lat) > 180 or abs(lon) > 180:
|
|
# Convert from UTM Zone 30N to WGS84
|
|
utm_proj = pyproj.Proj(proj='utm', zone=30, ellps='WGS84', preserve_units=False)
|
|
wgs84_proj = pyproj.Proj(proj='latlong', datum='WGS84')
|
|
transformer = pyproj.Transformer.from_proj(utm_proj, wgs84_proj, always_xy=True)
|
|
lon, lat = transformer.transform(lon, lat)
|
|
|
|
measurement_points[point_id] = {
|
|
'latitude': lat,
|
|
'longitude': lon,
|
|
'name': row.get('name', row.get('descripcion', f'Point {point_id}')),
|
|
'district': row.get('district', row.get('distrito', '')),
|
|
'road_type': row.get('tipo_elem', row.get('type', '')),
|
|
'raw_data': dict(row)
|
|
}
|
|
|
|
except (ValueError, Exception):
|
|
continue
|
|
|
|
except Exception:
|
|
continue
|
|
|
|
self.logger.info("Parsed measurement points registry",
|
|
total_points=len(measurement_points))
|
|
return measurement_points
|
|
|
|
except Exception as e:
|
|
self.logger.error("Error parsing measurement points CSV", error=str(e))
|
|
return {}
|
|
|
|
def _get_next_month(self, current_date: datetime) -> datetime:
|
|
"""Get next month date"""
|
|
if current_date.month == 12:
|
|
return current_date.replace(year=current_date.year + 1, month=1)
|
|
else:
|
|
return current_date.replace(month=current_date.month + 1)
|
|
|
|
# Async methods for data fetching (simplified versions)
|
|
|
|
async def _fetch_traffic_xml_data(self, endpoint: str) -> Optional[List[Dict[str, Any]]]:
|
|
"""Fetch and parse Madrid traffic XML data with improved parsing from madrid_opendata.py"""
|
|
try:
|
|
headers = {
|
|
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
|
|
'Accept': 'application/xml,text/xml,*/*',
|
|
'Accept-Language': 'es-ES,es;q=0.9,en;q=0.8',
|
|
'Accept-Encoding': 'gzip, deflate, br',
|
|
'Cache-Control': 'no-cache',
|
|
'Referer': 'https://datos.madrid.es/'
|
|
}
|
|
|
|
response = await self.get(endpoint, headers=headers, timeout=30)
|
|
|
|
if not response or response.status_code != 200:
|
|
self.logger.warning("Failed to fetch XML data",
|
|
endpoint=endpoint,
|
|
status=response.status_code if response else None)
|
|
return None
|
|
|
|
# Get XML content with encoding handling
|
|
xml_content = self._decode_response_content(response)
|
|
if not xml_content:
|
|
self.logger.debug("No XML content received", endpoint=endpoint)
|
|
return None
|
|
|
|
self.logger.debug("Madrid XML content preview",
|
|
length=len(xml_content),
|
|
first_500=xml_content[:500] if len(xml_content) > 500 else xml_content)
|
|
|
|
# Parse with improved method
|
|
traffic_points = self._parse_madrid_traffic_xml(xml_content)
|
|
|
|
if traffic_points:
|
|
self.logger.info("Successfully parsed Madrid traffic XML", points=len(traffic_points))
|
|
return traffic_points
|
|
else:
|
|
self.logger.warning("No traffic points found in XML", endpoint=endpoint)
|
|
return None
|
|
|
|
except Exception as e:
|
|
self.logger.error("Error fetching traffic XML data",
|
|
endpoint=endpoint,
|
|
error=str(e))
|
|
return None
|
|
|
|
async def _fetch_real_historical_traffic_enhanced(self, latitude: float, longitude: float,
|
|
start_date: datetime, end_date: datetime) -> List[Dict[str, Any]]:
|
|
"""Fetch real historical traffic data with pedestrian enhancement"""
|
|
try:
|
|
self.logger.info("Fetching historical traffic data",
|
|
lat=latitude, lon=longitude,
|
|
start=start_date, end=end_date)
|
|
|
|
# Madrid historical data is available through ZIP files
|
|
# Each month has a specific URL pattern
|
|
historical_data = []
|
|
|
|
current_date = start_date.replace(day=1) # Start of month
|
|
months_processed = 0
|
|
max_months_per_request = 24 # Limit to prevent memory exhaustion
|
|
|
|
while current_date <= end_date and months_processed < max_months_per_request:
|
|
try:
|
|
# Calculate the month code for Madrid's ZIP files
|
|
# This follows Madrid's naming convention
|
|
year = current_date.year
|
|
month = current_date.month
|
|
|
|
# Madrid uses a specific coding system for historical files
|
|
# Calculate month code based on 2025/June = 145 reference point
|
|
reference_year, reference_month, reference_code = 2025, 6, 145
|
|
months_diff = (year - reference_year) * 12 + (month - reference_month)
|
|
month_code = reference_code + months_diff
|
|
|
|
# Validate month code is within reasonable range
|
|
if not (100 <= month_code <= 300):
|
|
self.logger.warning("Month code out of expected range",
|
|
year=year, month=month, code=month_code)
|
|
current_date = self._get_next_month(current_date)
|
|
continue
|
|
|
|
# Use the correct Madrid URL pattern: 208627-{month_code}
|
|
zip_url = f"https://datos.madrid.es/egob/catalogo/208627-{month_code}-transporte-ptomedida-historico.zip"
|
|
|
|
# Fetch and process the ZIP file
|
|
month_data = await self._process_historical_zip_file(zip_url, latitude, longitude)
|
|
|
|
if month_data:
|
|
historical_data.extend(month_data)
|
|
self.logger.debug("Processed historical data for month",
|
|
year=year, month=month, records=len(month_data))
|
|
|
|
months_processed += 1
|
|
|
|
except Exception as month_error:
|
|
self.logger.warning("Failed to process month",
|
|
year=current_date.year,
|
|
month=current_date.month,
|
|
error=str(month_error))
|
|
|
|
# Move to next month
|
|
if current_date.month == 12:
|
|
current_date = current_date.replace(year=current_date.year + 1, month=1)
|
|
else:
|
|
current_date = current_date.replace(month=current_date.month + 1)
|
|
|
|
# Filter data to exact date range
|
|
filtered_data = [
|
|
record for record in historical_data
|
|
if start_date <= record.get('date', datetime.min.replace(tzinfo=timezone.utc)) <= end_date
|
|
]
|
|
|
|
self.logger.info("Historical traffic data fetched",
|
|
total_records=len(filtered_data),
|
|
months_processed=(end_date.year - start_date.year) * 12 + end_date.month - start_date.month + 1)
|
|
|
|
return filtered_data
|
|
|
|
except Exception as e:
|
|
self.logger.error("Error fetching historical traffic data", error=str(e))
|
|
return []
|
|
|
|
async def _process_historical_zip_file(self, zip_url: str, latitude: float, longitude: float) -> List[Dict[str, Any]]:
|
|
"""Process a single historical ZIP file containing Madrid traffic data"""
|
|
import zipfile
|
|
import io
|
|
|
|
try:
|
|
self.logger.info("Processing historical ZIP file", zip_url=zip_url)
|
|
|
|
# Download the ZIP file
|
|
headers = {
|
|
'User-Agent': 'Bakery-IA Historical Traffic Processor/2.0',
|
|
'Accept': 'application/zip, application/octet-stream',
|
|
'Accept-Encoding': 'gzip, deflate',
|
|
'Connection': 'keep-alive',
|
|
'Referer': 'https://datos.madrid.es/'
|
|
}
|
|
|
|
response = await self.get(zip_url, headers=headers, timeout=120) # Longer timeout for large files
|
|
|
|
if not response or response.status_code != 200:
|
|
self.logger.warning("Failed to download ZIP file",
|
|
zip_url=zip_url,
|
|
status=response.status_code if response else None)
|
|
return []
|
|
|
|
# Process ZIP content in memory
|
|
historical_records = []
|
|
|
|
# Conditionally fetch measurement points registry
|
|
measurement_points = {}
|
|
|
|
# Fetch measurement points registry for coordinate lookup (limited for memory efficiency)
|
|
measurement_points = await self._fetch_measurement_points_registry()
|
|
self.logger.info("Fetched measurement points registry",
|
|
total_points=len(measurement_points) if measurement_points else 0)
|
|
|
|
|
|
# Find nearest 3 (instead of filtering by radius)
|
|
nearest_points = self._find_nearest_measurement_points(measurement_points, latitude, longitude, num_points=3)
|
|
nearest_ids = {p[0] for p in nearest_points} # Set for fast lookup
|
|
|
|
if not nearest_points:
|
|
self.logger.warning("No nearby measurement points found")
|
|
return []
|
|
|
|
with zipfile.ZipFile(io.BytesIO(response.content)) as zip_file:
|
|
# List all files in the ZIP
|
|
file_list = zip_file.namelist()
|
|
|
|
# Process CSV files containing traffic data
|
|
csv_files = [f for f in file_list if f.lower().endswith('.csv')]
|
|
|
|
for csv_filename in csv_files:
|
|
try:
|
|
|
|
# Read CSV content
|
|
with zip_file.open(csv_filename) as csv_file:
|
|
# Decode content (Madrid files are typically in UTF-8 or ISO-8859-1)
|
|
content = csv_file.read()
|
|
|
|
# Try different encodings
|
|
try:
|
|
text_content = content.decode('utf-8')
|
|
except UnicodeDecodeError:
|
|
try:
|
|
text_content = content.decode('iso-8859-1')
|
|
except UnicodeDecodeError:
|
|
text_content = content.decode('utf-8', errors='ignore')
|
|
|
|
# Parse CSV with chunked processing to save memory
|
|
csv_records = await self._process_csv_content_chunked(
|
|
text_content, csv_filename, latitude, longitude, nearest_ids, nearest_points
|
|
)
|
|
historical_records.extend(csv_records)
|
|
|
|
# Clean up text_content immediately to free memory
|
|
del text_content
|
|
import gc
|
|
gc.collect()
|
|
|
|
except Exception as csv_error:
|
|
self.logger.warning("Error processing CSV file",
|
|
filename=csv_filename,
|
|
error=str(csv_error))
|
|
continue
|
|
|
|
# Skip sorting to save memory - database can sort if needed
|
|
# historical_records.sort(key=lambda x: x.get('date', datetime.min.replace(tzinfo=timezone.utc)))
|
|
|
|
self.logger.info("Historical ZIP processing completed",
|
|
zip_url=zip_url,
|
|
total_records=len(historical_records))
|
|
|
|
return historical_records
|
|
|
|
except zipfile.BadZipFile:
|
|
self.logger.error("Invalid ZIP file", zip_url=zip_url)
|
|
return []
|
|
except Exception as e:
|
|
self.logger.error("Error processing historical ZIP file",
|
|
zip_url=zip_url, error=str(e))
|
|
return []
|
|
|
|
async def _process_csv_content_chunked(
|
|
self,
|
|
text_content: str,
|
|
csv_filename: str,
|
|
latitude: float,
|
|
longitude: float,
|
|
nearest_ids: Set[str],
|
|
nearest_points: List[Tuple[str, Dict, float]]) -> List[Dict[str, Any]]:
|
|
"""Process CSV content in chunks to prevent memory issues"""
|
|
import csv
|
|
import io
|
|
import gc
|
|
|
|
try:
|
|
# Process CSV with chunked streaming
|
|
csv_reader = csv.DictReader(io.StringIO(text_content), delimiter=';')
|
|
|
|
chunk_size = 10000 # Process 10k rows at a time to reduce memory pressure
|
|
chunk_records = []
|
|
all_records = []
|
|
row_count = 0
|
|
processed_count = 0
|
|
|
|
# Debug: Log first few CSV IDs and nearest IDs
|
|
total_rows_seen = 0
|
|
debug_logged = False
|
|
|
|
# Debug: Check text_content size
|
|
self.logger.debug("CSV content info",
|
|
filename=csv_filename,
|
|
content_size=len(text_content),
|
|
first_100_chars=text_content[:100])
|
|
|
|
for row in csv_reader:
|
|
total_rows_seen += 1
|
|
measurement_point_id = row.get('id', '').strip()
|
|
|
|
# Debug logging for first few records
|
|
if not debug_logged and total_rows_seen <= 5:
|
|
self.logger.debug("CSV vs Nearest ID comparison",
|
|
row_num=total_rows_seen,
|
|
csv_id=measurement_point_id,
|
|
nearest_ids=list(nearest_ids)[:5],
|
|
total_nearest=len(nearest_ids))
|
|
if total_rows_seen == 5:
|
|
debug_logged = True
|
|
|
|
if measurement_point_id not in nearest_ids: # Early skip!
|
|
continue
|
|
|
|
row_count += 1
|
|
|
|
# Hard limit to prevent memory issues
|
|
if row_count > self.MAX_CSV_PROCESSING_ROWS:
|
|
self.logger.warning("Row limit reached for CSV",
|
|
filename=csv_filename,
|
|
city="madrid")
|
|
break
|
|
|
|
try:
|
|
# Extract and validate data
|
|
record_data = await self._parse_historical_csv_row(row, latitude, longitude, nearest_points)
|
|
|
|
if record_data:
|
|
chunk_records.append(record_data)
|
|
processed_count += 1
|
|
|
|
# Process chunk when it reaches size limit
|
|
if len(chunk_records) >= chunk_size:
|
|
all_records.extend(chunk_records)
|
|
|
|
# Clear chunk and force garbage collection
|
|
chunk_records = []
|
|
gc.collect()
|
|
elif processed_count < 5: # Debug first few failures
|
|
self.logger.debug("Row parsing returned None",
|
|
row_num=total_rows_seen,
|
|
measurement_point_id=measurement_point_id)
|
|
|
|
except Exception as e:
|
|
# Log first few parsing exceptions
|
|
if processed_count < 5:
|
|
self.logger.error("Row parsing exception",
|
|
row_num=total_rows_seen,
|
|
measurement_point_id=measurement_point_id,
|
|
error=str(e))
|
|
continue
|
|
|
|
# Process remaining records
|
|
if chunk_records:
|
|
all_records.extend(chunk_records)
|
|
chunk_records = []
|
|
gc.collect()
|
|
|
|
self.logger.info("Processed CSV file",
|
|
filename=csv_filename,
|
|
total_rows_read=total_rows_seen,
|
|
rows_passed_filter=row_count,
|
|
processed_records=processed_count)
|
|
|
|
return all_records
|
|
|
|
except Exception as e:
|
|
self.logger.error("Error processing CSV content",
|
|
filename=csv_filename, error=str(e))
|
|
return []
|
|
|
|
async def _parse_historical_csv_row(self, row: Dict[str, str], query_lat: float, query_lon: float,
|
|
nearest_points: List[Tuple[str, Dict, float]]) -> Optional[Dict[str, Any]]:
|
|
"""Parse a single row from Madrid's historical traffic CSV with actual structure"""
|
|
try:
|
|
# Actual Madrid CSV structure (2025):
|
|
# id, fecha, tipo_elem, intensidad, ocupacion, carga, vmed, error, periodo_integracion
|
|
|
|
# Extract date and time
|
|
fecha_str = row.get('fecha', '').strip()
|
|
if not fecha_str:
|
|
self.logger.info("No fecha data")
|
|
return None
|
|
|
|
# Parse Madrid's date format (YYYY-MM-DD HH:MM:SS)
|
|
try:
|
|
date_obj = datetime.strptime(fecha_str, '%Y-%m-%d %H:%M:%S')
|
|
date_obj = date_obj.replace(tzinfo=timezone.utc)
|
|
except Exception as e:
|
|
self.logger.error("Parse data error", error=str(e))
|
|
return None
|
|
|
|
measurement_point_id = row.get('id', '').strip()
|
|
|
|
# Lookup point_data from nearest_points
|
|
point_match = next((p for p in nearest_points if p[0] == measurement_point_id), None)
|
|
if not point_match:
|
|
return None
|
|
|
|
point_data = point_match[1]
|
|
distance_km = point_match[2]
|
|
|
|
lat = point_data.get('latitude')
|
|
lon = point_data.get('longitude')
|
|
measurement_point_name = point_data.get('name', f"Madrid Point {measurement_point_id}")
|
|
|
|
# Extract traffic data
|
|
intensidad = self._safe_int(row.get('intensidad', '0'))
|
|
ocupacion = self._safe_int(row.get('ocupacion', '0'))
|
|
carga = self._safe_int(row.get('carga', '0'))
|
|
vmed = self._safe_int(row.get('vmed', '0')) # Average speed
|
|
error_status = row.get('error', '').strip()
|
|
|
|
# Calculate congestion level from ocupacion (occupation percentage)
|
|
if ocupacion >= 80:
|
|
congestion_level = CongestionLevel.BLOCKED.value
|
|
elif ocupacion >= 50:
|
|
congestion_level = CongestionLevel.HIGH.value
|
|
elif ocupacion >= 25:
|
|
congestion_level = CongestionLevel.MEDIUM.value
|
|
else:
|
|
congestion_level = CongestionLevel.LOW.value
|
|
|
|
# Apply pedestrian inference for historical data
|
|
location_context = {
|
|
'latitude': lat,
|
|
'longitude': lon,
|
|
'measurement_point_name': measurement_point_name,
|
|
'district': MadridPedestrianInference._infer_district_from_location({'latitude': lat, 'longitude': lon})
|
|
}
|
|
|
|
# Create traffic record for pedestrian inference
|
|
traffic_record = TrafficRecord(
|
|
date=date_obj,
|
|
traffic_volume=intensidad,
|
|
occupation_percentage=ocupacion,
|
|
load_percentage=carga,
|
|
average_speed=max(vmed, 5), # Ensure minimum speed
|
|
congestion_level=congestion_level,
|
|
pedestrian_count=0, # Will be calculated
|
|
measurement_point_id=measurement_point_id,
|
|
measurement_point_name=measurement_point_name,
|
|
road_type=self._classify_road_type(measurement_point_name),
|
|
source='madrid_historical_zip'
|
|
)
|
|
|
|
# Calculate pedestrian count
|
|
pedestrian_count, inference_metadata = self.pedestrian_inference.calculate_pedestrian_flow(
|
|
traffic_record, location_context
|
|
)
|
|
|
|
# Build result dictionary
|
|
result = {
|
|
'date': date_obj,
|
|
'measurement_point_id': measurement_point_id,
|
|
'measurement_point_name': measurement_point_name,
|
|
'latitude': lat,
|
|
'longitude': lon,
|
|
'traffic_volume': intensidad,
|
|
'occupation_percentage': ocupacion,
|
|
'load_percentage': carga,
|
|
'average_speed': max(vmed, 5),
|
|
'congestion_level': congestion_level,
|
|
'pedestrian_count': pedestrian_count,
|
|
'source': 'madrid_historical_zip',
|
|
'city': 'madrid',
|
|
'district': location_context.get('district'),
|
|
'road_type': self._classify_road_type(measurement_point_name),
|
|
'has_pedestrian_inference': True,
|
|
'data_quality_score': self._calculate_data_quality_score(row),
|
|
'distance_from_query_km': distance_km,
|
|
'inference_metadata': inference_metadata,
|
|
'raw_data': {
|
|
'error_status': error_status,
|
|
'periodo_integracion': row.get('periodo_integracion', ''),
|
|
'tipo_elem': row.get('tipo_elem', ''),
|
|
'measurement_point_id': measurement_point_id
|
|
},
|
|
'error_status': error_status if error_status else None
|
|
}
|
|
|
|
return result
|
|
|
|
except Exception as e:
|
|
self.logger.error("Error cvs row", error=str(e))
|
|
return None
|
|
|
|
def _safe_int(self, value_str: str) -> int:
|
|
"""Safely convert string to int - improved version"""
|
|
try:
|
|
return int(float(value_str.replace(',', '.')))
|
|
except (ValueError, TypeError):
|
|
return 0
|
|
|
|
def _calculate_data_quality_score(self, row: Dict[str, str]) -> float:
|
|
"""Calculate data quality score for historical record"""
|
|
score = 100.0
|
|
|
|
# Check for missing data
|
|
if not row.get('intensidad', '').strip():
|
|
score -= 20
|
|
if not row.get('ocupacion', '').strip():
|
|
score -= 15
|
|
if not row.get('vmed', '').strip():
|
|
score -= 15
|
|
if not row.get('descripcion', '').strip():
|
|
score -= 10
|
|
|
|
# Check for error status
|
|
error_status = row.get('error', '').strip()
|
|
if error_status and error_status.lower() not in ['n', 'no', '0', '']:
|
|
score -= 30
|
|
|
|
return max(0.0, score)
|
|
|
|
def _classify_road_type(self, measurement_point_name: str) -> str:
|
|
"""Classify road type based on measurement point name"""
|
|
if not measurement_point_name:
|
|
return 'unknown'
|
|
|
|
name_lower = measurement_point_name.lower()
|
|
|
|
if any(keyword in name_lower for keyword in ['m-30', 'm30', 'circunvalacion']):
|
|
return 'ring_road'
|
|
elif any(keyword in name_lower for keyword in ['a-', 'autopista', 'autovia']):
|
|
return 'highway'
|
|
elif any(keyword in name_lower for keyword in ['calle', 'avenida', 'paseo', 'plaza']):
|
|
return 'urban'
|
|
elif any(keyword in name_lower for keyword in ['acceso', 'enlace', 'intercambiador']):
|
|
return 'access_road'
|
|
else:
|
|
return 'urban' # Default to urban for Madrid
|
|
|
|
def _find_nearest_traffic_point(self, latitude: float, longitude: float,
|
|
traffic_data: List[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
|
|
"""Find the nearest traffic measurement point"""
|
|
try:
|
|
if not traffic_data:
|
|
return None
|
|
|
|
min_distance = float('inf')
|
|
nearest_point = None
|
|
|
|
for point in traffic_data:
|
|
point_lat = point.get('latitude', 0)
|
|
point_lon = point.get('longitude', 0)
|
|
|
|
if point_lat and point_lon:
|
|
distance = self._calculate_distance(latitude, longitude, point_lat, point_lon)
|
|
|
|
if distance < min_distance:
|
|
min_distance = distance
|
|
nearest_point = point
|
|
|
|
if nearest_point:
|
|
self.logger.debug("Found nearest traffic point",
|
|
distance_km=min_distance,
|
|
point_id=nearest_point.get('measurement_point_id'))
|
|
|
|
return nearest_point
|
|
|
|
except Exception as e:
|
|
self.logger.error("Error finding nearest traffic point", error=str(e))
|
|
return None
|
|
|
|
def _get_closest_distance(self, latitude: float, longitude: float, traffic_data: List[Dict[str, Any]]) -> float:
|
|
"""Get distance to closest traffic point"""
|
|
try:
|
|
if not traffic_data:
|
|
return float('inf')
|
|
|
|
min_distance = float('inf')
|
|
|
|
for point in traffic_data:
|
|
point_lat = point.get('latitude', 0)
|
|
point_lon = point.get('longitude', 0)
|
|
|
|
if point_lat and point_lon:
|
|
distance = self._calculate_distance(latitude, longitude, point_lat, point_lon)
|
|
min_distance = min(min_distance, distance)
|
|
|
|
return min_distance
|
|
|
|
except Exception as e:
|
|
self.logger.error("Error calculating closest distance", error=str(e))
|
|
return float('inf')
|
|
|
|
def _find_nearest_measurement_points(self, measurement_points: Dict[str, Dict[str, Any]],
|
|
latitude: float, longitude: float,
|
|
num_points: int = 3, max_distance_km: Optional[float] = 5.0) -> List[Tuple[str, Dict[str, Any], float]]:
|
|
"""
|
|
Find the nearest num_points measurement points, sorted by distance.
|
|
Returns list of (point_id, point_data, distance_km) tuples.
|
|
"""
|
|
if not measurement_points:
|
|
return []
|
|
|
|
distances = []
|
|
for point_id, point_data in measurement_points.items():
|
|
point_lat = point_data.get('latitude')
|
|
point_lon = point_data.get('longitude')
|
|
if point_lat is not None and point_lon is not None:
|
|
distance = self._calculate_distance(latitude, longitude, point_lat, point_lon)
|
|
distances.append((distance, point_id, point_data))
|
|
|
|
# Sort by distance and take top N
|
|
distances.sort(key=lambda x: x[0])
|
|
nearest = distances[:num_points]
|
|
|
|
# Filter by max_distance if set
|
|
if max_distance_km is not None:
|
|
nearest = [p for p in nearest if p[0] <= max_distance_km]
|
|
|
|
self.logger.info(f"Found {len(nearest)} nearest measurement points (out of {len(measurement_points)} total)")
|
|
return [(p[1], p[2], p[0]) for p in nearest] # (id, data, distance) |