予測分析・シミュレーションによる状況認識¶

// 統合時系列予測システム
interface TimeSeriesPredictionResult {
  predictions: PredictionPoint[];
  confidence: ConfidenceInterval[];
  uncertainty: UncertaintyMetrics;
  modelPerformance: ModelPerformanceMetrics;
  anomalies: TimeSeriesAnomaly[];
  explanation: PredictionExplanation;
}

interface PredictionPoint {
  timestamp: Date;
  value: number;
  confidence: number;
  contributingFactors: Factor[];
}

interface ConfidenceInterval {
  timestamp: Date;
  lower: number;
  upper: number;
  confidence: number; // 95%, 99% etc.
}

interface UncertaintyMetrics {
  aleatoric: number; // データの不確実性
  epistemic: number; // モデルの不確実性
  total: number;
  sources: UncertaintySource[];
}

class AdvancedTimeSeriesPredictionSystem {
  private arimaModel: ARIMAModel;
  private lstmModel: LSTMTimeSeriesModel;
  private prophetModel: ProphetModel;
  private transformerModel: TimeSeriesTransformer;
  private ensembleModel: EnsembleTimeSeriesModel;
  private anomalyDetector: TimeSeriesAnomalyDetector;
  private uncertaintyEstimator: UncertaintyQuantifier;

  constructor() {
    this.initializePredictionModels();
  }

  private initializePredictionModels(): void {
    // ARIMA モデル（統計的手法）
    this.arimaModel = new ARIMAModel({
      autoOrder: true,
      seasonalOrder: [1, 1, 1, 12], // 季節性対応
      informationCriterion: 'aic'
    });

    // LSTM による深層学習予測
    this.lstmModel = new LSTMTimeSeriesModel({
      sequenceLength: 60,
      features: ['value', 'trend', 'seasonal', 'external'],
      layers: [64, 32, 16],
      dropout: 0.2,
      recurrentDropout: 0.2
    });

    // Facebook Prophet（トレンド+季節性）
    this.prophetModel = new ProphetModel({
      growthType: 'logistic',
      seasonalityMode: 'multiplicative',
      holidays: true,
      changepoints: 'auto'
    });

    // Transformer（Attention機構）
    this.transformerModel = new TimeSeriesTransformer({
      sequenceLength: 96,
      predictionLength: 24,
      hiddenSize: 256,
      numHeads: 8,
      numLayers: 6
    });

    // アンサンブルモデル
    this.ensembleModel = new EnsembleTimeSeriesModel({
      baseModels: [
        { model: this.arimaModel, weight: 0.25 },
        { model: this.lstmModel, weight: 0.35 },
        { model: this.prophetModel, weight: 0.25 },
        { model: this.transformerModel, weight: 0.15 }
      ],
      metaLearner: new LinearRegression(),
      stackingEnabled: true
    });

    // 異常検知
    this.anomalyDetector = new TimeSeriesAnomalyDetector({
      methods: ['isolation_forest', 'lstm_autoencoder', 'statistical'],
      sensitivity: 0.95,
      adaptiveThreshold: true
    });

    // 不確実性定量化
    this.uncertaintyEstimator = new BayesianUncertaintyQuantifier({
      mcSamples: 1000,
      conformalPrediction: true,
      quantileRegression: true
    });
  }

  // 包括的時系列予測の実行
  async predictTimeSeries(
    historicalData: TimeSeriesData[],
    predictionHorizon: number,
    externalFactors?: ExternalFactors[]
  ): Promise<TimeSeriesPredictionResult> {
    // データ前処理と特徴抽出
    const preprocessedData = await this.preprocessTimeSeriesData(
      historicalData,
      externalFactors
    );

    // 複数モデルでの予測実行
    const individualPredictions = await Promise.all([
      this.arimaModel.predict(preprocessedData, predictionHorizon),
      this.lstmModel.predict(preprocessedData, predictionHorizon),
      this.prophetModel.predict(preprocessedData, predictionHorizon),
      this.transformerModel.predict(preprocessedData, predictionHorizon)
    ]);

    // アンサンブル予測
    const ensemblePrediction = await this.ensembleModel.predict(
      preprocessedData,
      individualPredictions,
      predictionHorizon
    );

    // 不確実性の定量化
    const uncertaintyMetrics = await this.uncertaintyEstimator.quantify(
      ensemblePrediction,
      individualPredictions,
      preprocessedData
    );

    // 信頼区間の計算
    const confidenceIntervals = this.calculateConfidenceIntervals(
      ensemblePrediction,
      uncertaintyMetrics
    );

    // 異常検知
    const anomalies = await this.anomalyDetector.detectAnomalies(
      historicalData,
      ensemblePrediction
    );

    // 予測説明の生成
    const explanation = await this.generatePredictionExplanation(
      ensemblePrediction,
      individualPredictions,
      preprocessedData
    );

    // モデル性能評価
    const modelPerformance = await this.evaluateModelPerformance(
      individualPredictions,
      ensemblePrediction
    );

    return {
      predictions: ensemblePrediction.points,
      confidence: confidenceIntervals,
      uncertainty: uncertaintyMetrics,
      modelPerformance: modelPerformance,
      anomalies: anomalies,
      explanation: explanation
    };
  }

  // リアルタイム予測更新
  updatePredictionsRealTime(
    dataStream: Observable<TimeSeriesDataPoint>
  ): Observable<RealtimePredictionUpdate> {
    return dataStream.pipe(
      // データバッファリング
      bufferTime(60000), // 1分間のバッファ
      filter(buffer => buffer.length > 0),

      // 増分学習
      concatMap(async (newDataPoints) => {
        // 新データでのモデル更新
        await this.updateModelsIncremental(newDataPoints);

        // 予測の再実行
        const updatedPrediction = await this.predictTimeSeries(
          this.getRecentHistoricalData(1000), // 直近1000点
          24 // 24時間先
        );

        // 変化の検出
        const changeDetection = this.detectPredictionChanges(
          this.previousPrediction,
          updatedPrediction
        );

        this.previousPrediction = updatedPrediction;

        return {
          timestamp: new Date(),
          updatedPrediction: updatedPrediction,
          changeDetection: changeDetection,
          modelDrift: this.assessModelDrift(newDataPoints)
        };
      }),

      // エラーハンドリング
      catchError(error => {
        console.error('リアルタイム予測更新エラー:', error);
        return of({
          timestamp: new Date(),
          error: error.message,
          fallbackPrediction: this.getFallbackPrediction()
        } as RealtimePredictionUpdate);
      }),

      share()
    );
  }

  // シナリオベース予測
  async generateScenarioPredictions(
    baseData: TimeSeriesData[],
    scenarios: Scenario[]
  ): Promise<ScenarioPredictionResult[]> {
    const scenarioResults = await Promise.all(
      scenarios.map(async (scenario) => {
        // シナリオ固有の外部要因を適用
        const modifiedData = this.applyScenarioFactors(baseData, scenario);

        // 各シナリオでの予測実行
        const prediction = await this.predictTimeSeries(
          modifiedData,
          scenario.horizonDays * 24
        );

        // シナリオの実現可能性評価
        const feasibility = this.assessScenarioFeasibility(
          scenario,
          prediction
        );

        return {
          scenario: scenario,
          prediction: prediction,
          feasibility: feasibility,
          keyInsights: this.extractScenarioInsights(scenario, prediction)
        };
      })
    );

    // シナリオ間の比較分析
    const comparativeAnalysis = this.compareScenarios(scenarioResults);

    return scenarioResults.map(result => ({
      ...result,
      comparativeAnalysis: comparativeAnalysis
    }));
  }

  // 多変量時系列予測
  async predictMultivariateTimeSeries(
    multivariateData: MultivariateTimeSeriesData,
    targetVariables: string[],
    predictionHorizon: number
  ): Promise<MultivariatePredictionResult> {
    // ベクトル自己回帰（VAR）モデル
    const varModel = new VectorAutoregressionModel({
      maxLags: 12,
      targetVariables: targetVariables
    });

    // 多変量LSTM
    const multivariatelstm = new MultivariateLSTMModel({
      sequenceLength: 48,
      features: multivariateData.variables,
      hiddenSize: 128,
      numLayers: 3
    });

    // 動的因子モデル
    const factorModel = new DynamicFactorModel({
      numFactors: 5,
      factorLags: 2
    });

    // 各モデルでの予測
    const [varPrediction, lstmPrediction, factorPrediction] = 
      await Promise.all([
        varModel.predict(multivariateData, predictionHorizon),
        multivariatehtml.predict(multivariateData, predictionHorizon),
        factorModel.predict(multivariateData, predictionHorizon)
      ]);

    // グレンジャー因果性検定
    const causalityAnalysis = await this.analyzeGrangerCausality(
      multivariateData
    );

    // 多変量異常検知
    const multivariateAnomalies = await this.detectMultivariateAnomalies(
      multivariateData,
      [varPrediction, lstmPrediction, factorPrediction]
    );

    return {
      predictions: {
        var: varPrediction,
        lstm: lstmPrediction,
        factor: factorPrediction
      },
      causalityAnalysis: causalityAnalysis,
      anomalies: multivariateAnomalies,
      crossCorrelations: this.calculateCrossCorrelations(multivariateData)
    };
  }

  // 予測精度の継続評価
  async evaluatePredictionAccuracy(
    predictions: TimeSeriesPredictionResult[],
    actualValues: TimeSeriesData[]
  ): Promise<AccuracyEvaluationResult> {
    // 基本精度指標
    const basicMetrics = this.calculateBasicAccuracyMetrics(
      predictions,
      actualValues
    );

    // 方向性精度（上昇・下降の方向予測精度）
    const directionalAccuracy = this.calculateDirectionalAccuracy(
      predictions,
      actualValues
    );

    // 異常時の予測精度
    const anomalyPredictionAccuracy = this.evaluateAnomalyPredictionAccuracy(
      predictions,
      actualValues
    );

    // 信頼区間の妥当性
    const confidenceIntervalValidation = this.validateConfidenceIntervals(
      predictions,
      actualValues
    );

    return {
      basicMetrics: basicMetrics,
      directionalAccuracy: directionalAccuracy,
      anomalyAccuracy: anomalyPredictionAccuracy,
      confidenceValidation: confidenceIntervalValidation,
      overallScore: this.calculateOverallAccuracyScore({
        basicMetrics,
        directionalAccuracy,
        anomalyAccuracy
      })
    };
  }
}

// 結果インターフェース
interface RealtimePredictionUpdate {
  timestamp: Date;
  updatedPrediction?: TimeSeriesPredictionResult;
  changeDetection?: ChangeDetectionResult;
  modelDrift?: ModelDriftAssessment;
  error?: string;
  fallbackPrediction?: any;
}

interface ScenarioPredictionResult {
  scenario: Scenario;
  prediction: TimeSeriesPredictionResult;
  feasibility: FeasibilityAssessment;
  keyInsights: ScenarioInsight[];
  comparativeAnalysis?: ComparativeAnalysis;
}

interface MultivariatePredictionResult {
  predictions: {
    var: VARPrediction;
    lstm: LSTMPrediction;
    factor: FactorPrediction;
  };
  causalityAnalysis: GrangerCausalityResult;
  anomalies: MultivariateAnomaly[];
  crossCorrelations: CrossCorrelationMatrix;
}

// モンテカルロシミュレーションによるリスク評価システム
interface MonteCarloSimulationResult {
  scenarios: SimulationScenario[];
  statistics: SimulationStatistics;
  riskMetrics: RiskMetrics;
  sensitivityAnalysis: SensitivityAnalysis;
  extremeEvents: ExtremeEvent[];
  confidenceLevels: ConfidenceLevel[];
}

interface SimulationScenario {
  scenarioId: string;
  inputParameters: { [key: string]: number };
  outputValues: { [key: string]: number };
  probability: number;
  riskLevel: 'low' | 'medium' | 'high' | 'extreme';
}

interface RiskMetrics {
  valueAtRisk: { [confidence: string]: number }; // VaR
  conditionalValueAtRisk: { [confidence: string]: number }; // CVaR
  maximumDrawdown: number;
  probabilityOfLoss: number;
  expectedShortfall: number;
  tailRisk: number;
}

class MonteCarloRiskAnalysisSystem {
  private randomGenerator: RandomNumberGenerator;
  private distributionFitter: DistributionFitter;
  private correlationModeler: CorrelationModeler;
  private sensitivityAnalyzer: SensitivityAnalyzer;
  private extremeValueAnalyzer: ExtremeValueAnalyzer;

  constructor() {
    this.initializeSimulationComponents();
  }

  private initializeSimulationComponents(): void {
    // 高品質乱数生成器（Mersenne Twister）
    this.randomGenerator = new MersenneTwisterGenerator({
      seed: Date.now(),
      threadSafe: true
    });

    // 確率分布フィッティング
    this.distributionFitter = new DistributionFitter({
      supportedDistributions: [
        'normal', 'lognormal', 'exponential', 'gamma',
        'beta', 'uniform', 'triangular', 'weibull'
      ],
      fittingMethod: 'maximum_likelihood',
      goodnessOfFit: ['ks_test', 'anderson_darling', 'chi_square']
    });

    // 相関・依存関係モデリング
    this.correlationModeler = new CorrelationModeler({
      copulaTypes: ['gaussian', 'student_t', 'clayton', 'gumbel'],
      dependenceMetrics: ['pearson', 'spearman', 'kendall']
    });

    // 感度分析
    this.sensitivityAnalyzer = new SensitivityAnalyzer({
      methods: ['sobol', 'morris', 'fast', 'delta_moment'],
      interactionEffects: true
    });

    // 極値分析
    this.extremeValueAnalyzer = new ExtremeValueAnalyzer({
      distribution: 'generalized_extreme_value',
      threshold: 'auto',
      returnPeriods: [10, 50, 100, 500, 1000]
    });
  }

  // モンテカルロシミュレーションの実行
  async runMonteCarloSimulation(
    model: SimulationModel,
    parameters: ModelParameters,
    numSimulations: number = 10000
  ): Promise<MonteCarloSimulationResult> {
    // パラメータの確率分布を推定
    const parameterDistributions = await this.fitParameterDistributions(
      parameters
    );

    // 変数間の相関関係をモデル化
    const correlationStructure = await this.modelCorrelations(
      parameters.historicalData
    );

    // シミュレーション実行
    const scenarios = await this.generateScenarios(
      model,
      parameterDistributions,
      correlationStructure,
      numSimulations
    );

    // 統計量の計算
    const statistics = this.calculateStatistics(scenarios);

    // リスク指標の算出
    const riskMetrics = this.calculateRiskMetrics(scenarios);

    // 感度分析
    const sensitivityAnalysis = await this.performSensitivityAnalysis(
      model,
      parameters,
      scenarios
    );

    // 極値イベントの分析
    const extremeEvents = await this.analyzeExtremeEvents(scenarios);

    // 信頼水準別結果
    const confidenceLevels = this.calculateConfidenceLevels(
      scenarios,
      [0.90, 0.95, 0.99, 0.999]
    );

    return {
      scenarios: scenarios.slice(0, 1000), // サンプル結果のみ返却
      statistics: statistics,
      riskMetrics: riskMetrics,
      sensitivityAnalysis: sensitivityAnalysis,
      extremeEvents: extremeEvents,
      confidenceLevels: confidenceLevels
    };
  }

  // シナリオ生成
  private async generateScenarios(
    model: SimulationModel,
    distributions: ParameterDistribution[],
    correlations: CorrelationMatrix,
    numSimulations: number
  ): Promise<SimulationScenario[]> {
    const scenarios: SimulationScenario[] = [];

    // 並列処理でシミュレーション実行
    const batchSize = 1000;
    const numBatches = Math.ceil(numSimulations / batchSize);

    for (let batch = 0; batch < numBatches; batch++) {
      const batchSize = Math.min(1000, numSimulations - batch * 1000);
      const batchScenarios = await this.generateScenarioBatch(
        model,
        distributions,
        correlations,
        batchSize,
        batch * 1000
      );
      scenarios.push(...batchScenarios);
    }

    return scenarios;
  }

  // バッチシナリオ生成
  private async generateScenarioBatch(
    model: SimulationModel,
    distributions: ParameterDistribution[],
    correlations: CorrelationMatrix,
    batchSize: number,
    startIndex: number
  ): Promise<SimulationScenario[]> {
    const batchScenarios: SimulationScenario[] = [];

    for (let i = 0; i < batchSize; i++) {
      const scenarioId = `scenario_${startIndex + i}`;

      // 相関を考慮したランダムサンプリング
      const sampledParameters = await this.sampleCorrelatedParameters(
        distributions,
        correlations
      );

      // モデル実行
      const outputValues = await model.execute(sampledParameters);

      // 確率計算
      const probability = this.calculateScenarioProbability(
        sampledParameters,
        distributions
      );

      // リスクレベル判定
      const riskLevel = this.assessScenarioRisk(outputValues);

      batchScenarios.push({
        scenarioId,
        inputParameters: sampledParameters,
        outputValues,
        probability,
        riskLevel
      });
    }

    return batchScenarios;
  }

  // リスク指標の計算
  private calculateRiskMetrics(scenarios: SimulationScenario[]): RiskMetrics {
    // 結果値を昇順ソート
    const sortedValues = scenarios
      .map(s => s.outputValues.primaryMetric)
      .sort((a, b) => a - b);

    const n = sortedValues.length;

    // Value at Risk (VaR)
    const valueAtRisk = {
      '95%': sortedValues[Math.floor(n * 0.05)],
      '99%': sortedValues[Math.floor(n * 0.01)],
      '99.9%': sortedValues[Math.floor(n * 0.001)]
    };

    // Conditional Value at Risk (CVaR)
    const conditionalValueAtRisk = {
      '95%': this.calculateCVaR(sortedValues, 0.05),
      '99%': this.calculateCVaR(sortedValues, 0.01),
      '99.9%': this.calculateCVaR(sortedValues, 0.001)
    };

    // Maximum Drawdown
    const maximumDrawdown = this.calculateMaximumDrawdown(sortedValues);

    // Probability of Loss
    const probabilityOfLoss = sortedValues.filter(v => v < 0).length / n;

    // Expected Shortfall
    const expectedShortfall = this.calculateExpectedShortfall(sortedValues);

    // Tail Risk
    const tailRisk = this.calculateTailRisk(sortedValues);

    return {
      valueAtRisk,
      conditionalValueAtRisk,
      maximumDrawdown,
      probabilityOfLoss,
      expectedShortfall,
      tailRisk
    };
  }

  // 感度分析の実行
  private async performSensitivityAnalysis(
    model: SimulationModel,
    parameters: ModelParameters,
    scenarios: SimulationScenario[]
  ): Promise<SensitivityAnalysis> {
    // Sobol感度指数の計算
    const sobolIndices = await this.calculateSobolIndices(
      model,
      parameters,
      1000
    );

    // Morris screening method
    const morrisIndices = await this.calculateMorrisIndices(
      model,
      parameters,
      500
    );

    // 相関ベース感度分析
    const correlationSensitivity = this.calculateCorrelationSensitivity(
      scenarios
    );

    // 回帰ベース感度分析
    const regressionSensitivity = await this.calculateRegressionSensitivity(
      scenarios
    );

    return {
      sobolIndices: sobolIndices,
      morrisIndices: morrisIndices,
      correlationSensitivity: correlationSensitivity,
      regressionSensitivity: regressionSensitivity,
      ranking: this.rankParameterImportance([
        sobolIndices,
        morrisIndices,
        correlationSensitivity
      ])
    };
  }

  // リアルタイムリスク監視
  monitorRealTimeRisk(
    dataStream: Observable<RealTimeData>,
    riskThresholds: RiskThreshold[]
  ): Observable<RiskAlert> {
    return dataStream.pipe(
      // 5分間のウィンドウ
      bufferTime(300000),
      filter(buffer => buffer.length > 0),

      // 高速モンテカルロシミュレーション
      concatMap(async (dataBuffer) => {
        const quickSimulation = await this.runQuickMonteCarloSimulation(
          dataBuffer,
          1000 // 少数のシミュレーション
        );

        // 閾値チェック
        const triggeredAlerts = this.checkRiskThresholds(
          quickSimulation.riskMetrics,
          riskThresholds
        );

        return triggeredAlerts;
      }),

      // アラートがある場合のみ通知
      filter(alerts => alerts.length > 0),

      // フラット化
      mergeMap(alerts => alerts),

      share()
    );
  }

  // ストレステストの実行
  async runStressTest(
    baseModel: SimulationModel,
    stressScenarios: StressScenario[]
  ): Promise<StressTestResult[]> {
    const stressTestResults = await Promise.all(
      stressScenarios.map(async (stressScenario) => {
        // ストレスシナリオの適用
        const stressedModel = this.applyStressScenario(
          baseModel,
          stressScenario
        );

        // ストレス下でのモンテカルロシミュレーション
        const stressSimulation = await this.runMonteCarloSimulation(
          stressedModel,
          stressScenario.parameters,
          5000
        );

        // ベースラインとの比較
        const baselineSimulation = await this.runMonteCarloSimulation(
          baseModel,
          stressScenario.parameters,
          5000
        );

        const impact = this.calculateStressImpact(
          baselineSimulation,
          stressSimulation
        );

        return {
          stressScenario: stressScenario,
          stressResult: stressSimulation,
          baselineResult: baselineSimulation,
          impact: impact,
          resilience: this.assessResilience(impact)
        };
      })
    );

    return stressTestResults;
  }
}

// 結果インターフェース
interface SensitivityAnalysis {
  sobolIndices: SobolIndex[];
  morrisIndices: MorrisIndex[];
  correlationSensitivity: CorrelationSensitivity;
  regressionSensitivity: RegressionSensitivity;
  ranking: ParameterRanking[];
}

interface StressTestResult {
  stressScenario: StressScenario;
  stressResult: MonteCarloSimulationResult;
  baselineResult: MonteCarloSimulationResult;
  impact: StressImpact;
  resilience: ResilienceAssessment;
}

// スマートシティ統合デジタルツインシステム
interface CityDigitalTwinResult {
  cityState: CityState;
  subsystemStates: SubsystemState[];
  predictions: CityPrediction[];
  optimizations: OptimizationRecommendation[];
  alertsAndWarnings: CityAlert[];
  performanceMetrics: CityPerformanceMetrics;
}

interface CityState {
  timestamp: Date;
  population: PopulationMetrics;
  traffic: TrafficMetrics;
  energy: EnergyMetrics;
  environment: EnvironmentalMetrics;
  infrastructure: InfrastructureMetrics;
  economy: EconomicMetrics;
}

interface SubsystemState {
  subsystemType: 'traffic' | 'energy' | 'water' | 'waste' | 'public_safety';
  status: 'optimal' | 'normal' | 'warning' | 'critical';
  utilization: number;
  efficiency: number;
  predictedIssues: PredictedIssue[];
}

class SmartCityDigitalTwinSystem {
  private trafficSimulator: TrafficFlowSimulator;
  private energySimulator: EnergyGridSimulator;
  private populationSimulator: PopulationDynamicsSimulator;
  private environmentSimulator: EnvironmentalSimulator;
  private economicSimulator: EconomicActivitySimulator;
  private integrationEngine: SystemIntegrationEngine;
  private optimizationEngine: CityOptimizationEngine;
  private alertSystem: CityAlertSystem;

  constructor() {
    this.initializeCitySimulators();
    this.setupRealTimeIntegration();
  }

  private initializeCitySimulators(): void {
    // 交通流シミュレーション
    this.trafficSimulator = new TrafficFlowSimulator({
      roadNetwork: this.loadRoadNetworkData(),
      trafficLights: this.loadTrafficLightData(),
      publicTransport: this.loadPublicTransportData(),
      vehicleTypes: ['car', 'bus', 'bike', 'pedestrian'],
      realTimeUpdates: true
    });

    // エネルギーグリッドシミュレーション
    this.energySimulator = new EnergyGridSimulator({
      powerPlants: this.loadPowerPlantData(),
      distributionNetwork: this.loadDistributionNetworkData(),
      renewableEnergy: this.loadRenewableEnergyData(),
      energyStorage: this.loadEnergyStorageData(),
      demandProfiles: this.loadEnergyDemandData()
    });

    // 人口動態シミュレーション
    this.populationSimulator = new PopulationDynamicsSimulator({
      demographics: this.loadDemographicData(),
      migrationPatterns: this.loadMigrationData(),
      economicFactors: this.loadEconomicData(),
      socialFactors: this.loadSocialData()
    });

    // 環境シミュレーション
    this.environmentSimulator = new EnvironmentalSimulator({
      airQuality: this.loadAirQualityData(),
      waterQuality: this.loadWaterQualityData(),
      noiseLevel: this.loadNoiseLevelData(),
      greenSpaces: this.loadGreenSpaceData(),
      weatherData: this.loadWeatherData()
    });

    // 経済活動シミュレーション
    this.economicSimulator = new EconomicActivitySimulator({
      businessDistricts: this.loadBusinessData(),
      employmentData: this.loadEmploymentData(),
      realEstateData: this.loadRealEstateData(),
      tourismData: this.loadTourismData()
    });

    // システム統合エンジン
    this.integrationEngine = new SystemIntegrationEngine({
      subsystems: [
        this.trafficSimulator,
        this.energySimulator,
        this.populationSimulator,
        this.environmentSimulator,
        this.economicSimulator
      ],
      interactionRules: this.loadInteractionRules(),
      feedbackLoops: this.loadFeedbackLoops()
    });

    // 最適化エンジン
    this.optimizationEngine = new CityOptimizationEngine({
      objectives: [
        'traffic_efficiency',
        'energy_efficiency',
        'air_quality',
        'economic_growth',
        'quality_of_life'
      ],
      constraints: this.loadOptimizationConstraints(),
      optimizationMethods: ['genetic_algorithm', 'simulated_annealing', 'reinforcement_learning']
    });
  }

  // リアルタイム都市シミュレーション
  runRealTimeCitySimulation(): Observable<CityDigitalTwinResult> {
    return interval(60000).pipe( // 1分間隔
      concatMap(async () => {
        // リアルタイムデータの取得
        const realTimeData = await this.collectRealTimeData();

        // 各サブシステムのシミュレーション実行
        const subsystemResults = await this.runSubsystemSimulations(
          realTimeData
        );

        // システム間相互作用の計算
        const integrationResult = await this.integrationEngine.integrate(
          subsystemResults
        );

        // 統合都市状態の算出
        const cityState = this.calculateCityState(integrationResult);

        // 将来予測の実行
        const predictions = await this.generateCityPredictions(
          cityState,
          24 // 24時間先
        );

        // 最適化推奨の生成
        const optimizations = await this.generateOptimizationRecommendations(
          cityState,
          predictions
        );

        // アラート・警告の生成
        const alerts = this.generateCityAlerts(cityState, predictions);

        // 性能指標の計算
        const performanceMetrics = this.calculateCityPerformanceMetrics(
          cityState
        );

        return {
          cityState,
          subsystemStates: subsystemResults,
          predictions,
          optimizations,
          alertsAndWarnings: alerts,
          performanceMetrics
        };
      }),

      share()
    );
  }

  // サブシステムシミュレーション実行
  private async runSubsystemSimulations(
    realTimeData: RealTimeCityData
  ): Promise<SubsystemState[]> {
    // 並列でサブシステムシミュレーション実行
    const [
      trafficState,
      energyState,
      environmentState,
      populationState,
      economicState
    ] = await Promise.all([
      this.simulateTrafficSystem(realTimeData.traffic),
      this.simulateEnergySystem(realTimeData.energy),
      this.simulateEnvironmentalSystem(realTimeData.environment),
      this.simulatePopulationSystem(realTimeData.population),
      this.simulateEconomicSystem(realTimeData.economic)
    ]);

    return [
      trafficState,
      energyState,
      environmentState,
      populationState,
      economicState
    ];
  }

  // 交通システムシミュレーション
  private async simulateTrafficSystem(
    trafficData: TrafficData
  ): Promise<SubsystemState> {
    // 現在の交通状況の分析
    const currentTrafficFlow = await this.trafficSimulator.analyzeCurrentFlow(
      trafficData
    );

    // 交通渋滞の予測
    const congestionPrediction = await this.trafficSimulator.predictCongestion(
      currentTrafficFlow,
      2 // 2時間先
    );

    // 信号制御の最適化
    const signalOptimization = await this.trafficSimulator.optimizeSignalTiming(
      currentTrafficFlow,
      congestionPrediction
    );

    // 公共交通機関の効率評価
    const publicTransportEfficiency = await this.trafficSimulator.evaluatePublicTransport(
      trafficData.publicTransport
    );

    // 交通システム状態の評価
    const systemStatus = this.evaluateTrafficSystemStatus({
      currentFlow: currentTrafficFlow,
      congestionLevel: congestionPrediction.averageCongestionLevel,
      signalEfficiency: signalOptimization.efficiency,
      publicTransportPerformance: publicTransportEfficiency.performance
    });

    return {
      subsystemType: 'traffic',
      status: systemStatus.status,
      utilization: systemStatus.utilization,
      efficiency: systemStatus.efficiency,
      predictedIssues: congestionPrediction.predictedIssues
    };
  }

  // エネルギーシステムシミュレーション
  private async simulateEnergySystem(
    energyData: EnergyData
  ): Promise<SubsystemState> {
    // 現在のエネルギー需給バランス
    const supplyDemandBalance = await this.energySimulator.analyzeSupplyDemand(
      energyData
    );

    // 再生可能エネルギーの発電予測
    const renewableForecast = await this.energySimulator.forecastRenewableGeneration(
      energyData.weather,
      24 // 24時間先
    );

    // エネルギー貯蔵の最適化
    const storageOptimization = await this.energySimulator.optimizeEnergyStorage(
      supplyDemandBalance,
      renewableForecast
    );

    // グリッド安定性の評価
    const gridStability = await this.energySimulator.assessGridStability(
      supplyDemandBalance,
      storageOptimization
    );

    const systemStatus = this.evaluateEnergySystemStatus({
      supplyDemandRatio: supplyDemandBalance.ratio,
      renewableShare: renewableForecast.renewableShare,
      storageUtilization: storageOptimization.utilization,
      gridStability: gridStability.stabilityScore
    });

    return {
      subsystemType: 'energy',
      status: systemStatus.status,
      utilization: systemStatus.utilization,
      efficiency: systemStatus.efficiency,
      predictedIssues: gridStability.predictedIssues
    };
  }

  // シナリオベース都市計画シミュレーション
  async simulateUrbanPlanningScenarios(
    planningScenarios: UrbanPlanningScenario[]
  ): Promise<UrbanPlanningSimulationResult[]> {
    const results = await Promise.all(
      planningScenarios.map(async (scenario) => {
        // シナリオに基づく都市構造の変更
        const modifiedCityModel = this.applyCityStructureChanges(
          this.baseCityModel,
          scenario.infrastructureChanges
        );

        // 長期シミュレーション（5-10年）
        const longTermSimulation = await this.runLongTermCitySimulation(
          modifiedCityModel,
          scenario.timeHorizonYears
        );

        // 経済的影響の評価
        const economicImpact = await this.evaluateEconomicImpact(
          scenario,
          longTermSimulation
        );

        // 社会的影響の評価
        const socialImpact = await this.evaluateSocialImpact(
          scenario,
          longTermSimulation
        );

        // 環境的影響の評価
        const environmentalImpact = await this.evaluateEnvironmentalImpact(
          scenario,
          longTermSimulation
        );

        return {
          scenario: scenario,
          simulationResult: longTermSimulation,
          economicImpact: economicImpact,
          socialImpact: socialImpact,
          environmentalImpact: environmentalImpact,
          overallScore: this.calculateOverallScenarioScore({
            economicImpact,
            socialImpact,
            environmentalImpact
          })
        };
      })
    );

    // シナリオ間の比較分析
    const comparativeAnalysis = this.compareUrbanPlanningScenarios(results);

    return results.map(result => ({
      ...result,
      comparativeAnalysis: comparativeAnalysis
    }));
  }

  // 緊急事態シミュレーション
  async simulateEmergencyScenarios(
    emergencyTypes: EmergencyType[]
  ): Promise<EmergencySimulationResult[]> {
    return Promise.all(
      emergencyTypes.map(async (emergencyType) => {
        // 緊急事態の発生をシミュレーション
        const emergencyEvent = this.generateEmergencyEvent(emergencyType);

        // 緊急事態下での都市システムの応答
        const systemResponse = await this.simulateEmergencyResponse(
          emergencyEvent,
          this.currentCityState
        );

        // 避難計画の評価
        const evacuationPlan = await this.evaluateEvacuationPlan(
          emergencyEvent,
          systemResponse
        );

        // 復旧計画の生成
        const recoveryPlan = await this.generateRecoveryPlan(
          emergencyEvent,
          systemResponse
        );

        return {
          emergencyType: emergencyType,
          emergencyEvent: emergencyEvent,
          systemResponse: systemResponse,
          evacuationPlan: evacuationPlan,
          recoveryPlan: recoveryPlan,
          estimatedImpact: this.estimateEmergencyImpact(systemResponse),
          preparednessLevel: this.assessPreparednessLevel(systemResponse)
        };
      })
    );
  }
}

// 結果インターフェース
interface UrbanPlanningSimulationResult {
  scenario: UrbanPlanningScenario;
  simulationResult: LongTermCitySimulation;
  economicImpact: EconomicImpactAssessment;
  socialImpact: SocialImpactAssessment;
  environmentalImpact: EnvironmentalImpactAssessment;
  overallScore: number;
  comparativeAnalysis?: ComparativeAnalysis;
}

interface EmergencySimulationResult {
  emergencyType: EmergencyType;
  emergencyEvent: EmergencyEvent;
  systemResponse: EmergencySystemResponse;
  evacuationPlan: EvacuationPlan;
  recoveryPlan: RecoveryPlan;
  estimatedImpact: EmergencyImpactEstimate;
  preparednessLevel: PreparednessAssessment;
}