From fce6dd6635859d5238277fef442bb6430a4e5703 Mon Sep 17 00:00:00 2001 From: YannAhlgrim Date: Fri, 30 May 2025 12:16:41 +0200 Subject: [PATCH] init simulationsstudie --- .../yann_ahlgrim_simulationsstudie.ipynb | 350 ++++++++++++++++++ 1 file changed, 350 insertions(+) create mode 100644 1_data_science/simulationsstudie/yann_ahlgrim_simulationsstudie.ipynb diff --git a/1_data_science/simulationsstudie/yann_ahlgrim_simulationsstudie.ipynb b/1_data_science/simulationsstudie/yann_ahlgrim_simulationsstudie.ipynb new file mode 100644 index 0000000..0f850c6 --- /dev/null +++ b/1_data_science/simulationsstudie/yann_ahlgrim_simulationsstudie.ipynb @@ -0,0 +1,350 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7ba05a39", + "metadata": {}, + "source": [ + "# Simulationsstudie: Diebstahlerkennung eines Autos in Echtzeit\n", + "\n", + "**Name:** Yann Ahlgrim \n", + "\n", + "In dieser Simulationsstudie wird ein Modell entwickelt, das anhand von Fahrverhaltensdaten erkennt, ob ein anderer Fahrer als üblich am Steuer sitzt. Ziel ist es, eine Wahrscheinlichkeit für einen Fahrerwechsel zu schätzen und bei hoher Wahrscheinlichkeit eine Diebstahlwarnung auszugeben. Die Studie umfasst die Generierung einer synthetischen Grundgesamtheit, die Simulation der Data Scientist Perspektive und die Analyse der Modellgüte." + ] + }, + { + "cell_type": "markdown", + "id": "f8d794f6", + "metadata": {}, + "source": [ + "## 1. Themenbeschreibung und Zielsetzung\n", + "\n", + "Im Fahrzeug werden zahlreiche Daten zum Fahrverhalten erfasst, z.B. Schaltpunkte, Drehzahlen, Fahrzeiten, Strecken und Fahrdauern. Ziel ist es, ein Modell zu entwickeln, das anhand dieser Daten erkennt, ob ein anderer als der übliche Fahrer am Steuer sitzt. Bei hoher Wahrscheinlichkeit soll eine Diebstahlwarnung erfolgen. Die Variablen werden so gewählt, dass sie das Fahrverhalten möglichst realistisch abbilden." + ] + }, + { + "cell_type": "markdown", + "id": "99146c58", + "metadata": {}, + "source": [ + "## 2. Definition der Variablen\n", + "\n", + "Für die Simulationsstudie werden folgende Variablen definiert:\n", + "\n", + "- **Zielvariable (y):** Fahrerwechsel (0 = üblicher Fahrer, 1 = anderer Fahrer)\n", + "- **Erklärende Variablen:**\n", + " 1. Schaltverhalten (nominal, 3 Ausprägungen: früh, normal, spät)\n", + " 2. Fahrzeitpunkt (ordinal, 4 Ausprägungen: Nacht, Morgen, Nachmittag, Abend)\n", + " 3. Durchschnittliche Drehzahl (metrisch)\n", + " 4. Durchschnittliche Geschwindigkeit (metrisch)\n", + "- **Weitere Variablen:**\n", + " 5. Außentemperatur (metrisch)\n", + " 6. Streckenlänge (metrisch)\n", + " 7. Anzahl Stopps (diskret)\n", + " 8. Fahrtdauer (metrisch)\n", + "\n", + "Zwischen \"Durchschnittliche Geschwindigkeit\" und \"Streckenlänge\" sowie zwischen \"Fahrtdauer\" und \"Anzahl Stopps\" werden inhaltlich sinnvolle Abhängigkeiten modelliert." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "97ba29a9", + "metadata": {}, + "outputs": [], + "source": [ + "# Import der benötigten Bibliotheken\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from scipy.stats import norm, multinomial, randint\n", + "\n", + "np.random.seed(11)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61247da5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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shift_behaviorfahrzeitpunktdrehzahlgeschwindigkeitaussentemperaturstreckenlaengeanzahl_stoppsunterwegszeitfahrerwechsel
0frühMorgen2557.80027467.05453016.62330027.687826333.5133231
1frühNachmittag2860.37032163.43402414.98237526.801979130.8106791
2normalMorgen2549.53581849.98298631.09134928.343674121.5831730
3normalAbend2258.83967655.05745613.14286433.245207016.5831981
4normalNacht2108.22553973.7673281.65439042.152370219.7752071
\n", + "
" + ], + "text/plain": [ + " shift_behavior fahrzeitpunkt drehzahl geschwindigkeit \\\n", + "0 früh Morgen 2557.800274 67.054530 \n", + "1 früh Nachmittag 2860.370321 63.434024 \n", + "2 normal Morgen 2549.535818 49.982986 \n", + "3 normal Abend 2258.839676 55.057456 \n", + "4 normal Nacht 2108.225539 73.767328 \n", + "\n", + " aussentemperatur streckenlaenge anzahl_stopps unterwegszeit \\\n", + "0 16.623300 27.687826 3 33.513323 \n", + "1 14.982375 26.801979 1 30.810679 \n", + "2 31.091349 28.343674 1 21.583173 \n", + "3 13.142864 33.245207 0 16.583198 \n", + "4 1.654390 42.152370 2 19.775207 \n", + "\n", + " fahrerwechsel \n", + "0 1 \n", + "1 1 \n", + "2 0 \n", + "3 1 \n", + "4 1 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "N = 1_000_000\n", + "\n", + "# 1. Schaltverhalten (nominal, 3 Ausprägungen)\n", + "p_shift = [0.3, 0.5, 0.2]\n", + "shift_behavior = np.random.choice(['früh', 'normal', 'spät'], size=N, p=p_shift)\n", + "\n", + "# 2. Fahrzeitpunkt (ordinal, 4 Ausprägungen)\n", + "p_time = [0.15, 0.35, 0.35, 0.15] # Nacht, Morgen, Nachmittag, Abend\n", + "fahrzeitpunkt = np.random.choice(['Nacht', 'Morgen', 'Nachmittag', 'Abend'], size=N, p=p_time)\n", + "\n", + "# 3. Durchschnittliche Drehzahl (metrisch, normalverteilt)\n", + "drehzahl = np.random.normal(loc=2500, scale=400, size=N)\n", + "\n", + "# 4. Durchschnittliche Geschwindigkeit (metrisch, normalverteilt)\n", + "geschwindigkeit = np.random.normal(loc=60, scale=10, size=N)\n", + "\n", + "# 5. Außentemperatur (metrisch, normalverteilt, unabhängig)\n", + "aussentemperatur = np.random.normal(loc=15, scale=10, size=N)\n", + "\n", + "# 6. Streckenlänge (metrisch, abhängig von Geschwindigkeit)\n", + "streckenlaenge = np.random.normal(loc=geschwindigkeit*0.5, scale=5)\n", + "\n", + "# 7. Anzahl Stopps (diskret, poissonverteilt, abhängig von unterwegszeit)\n", + "unterwegszeit = np.random.normal(loc=35, scale=8, size=N)\n", + "unterwegszeit = np.clip(unterwegszeit, 1, None) # Negative unterwegszeitn vermeiden\n", + "anzahl_stopps = np.random.poisson(lam=unterwegszeit/15)\n", + "\n", + "# 8. unterwegszeit (metrisch, normalverteilt)\n", + "# (bereits oben erzeugt)\n", + "\n", + "# Zielvariable: Funktional abhängig von 4 erklärenden Variablen\n", + "# Logistische Funktion mit Betas\n", + "beta_0 = -3\n", + "beta_shift = {'früh': 0.5, 'normal': 0, 'spät': 1.0}\n", + "beta_time = {'Nacht': 1.2, 'Morgen': 0, 'Nachmittag': 0.3, 'Abend': 0.7}\n", + "beta_drehzahl = 0.0005\n", + "beta_geschw = 0.02\n", + "\n", + "lin_pred = (\n", + " beta_0\n", + " + np.vectorize(beta_shift.get)(shift_behavior)\n", + " + np.vectorize(beta_time.get)(fahrzeitpunkt)\n", + " + beta_drehzahl * drehzahl\n", + " + beta_geschw * geschwindigkeit\n", + " + np.random.normal(0, 0.5, N) # Störterm\n", + ")\n", + "\n", + "p_fahrerwechsel = 1 / (1 + np.exp(-lin_pred))\n", + "y = np.random.binomial(1, p_fahrerwechsel)\n", + "\n", + "# DataFrame\n", + "data = pd.DataFrame({\n", + " 'shift_behavior': shift_behavior,\n", + " 'fahrzeitpunkt': fahrzeitpunkt,\n", + " 'drehzahl': drehzahl,\n", + " 'geschwindigkeit': geschwindigkeit,\n", + " 'aussentemperatur': aussentemperatur,\n", + " 'streckenlaenge': streckenlaenge,\n", + " 'anzahl_stopps': anzahl_stopps,\n", + " 'unterwegszeit': unterwegszeit,\n", + " 'fahrerwechsel': y\n", + "})\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "46ab8c99", + "metadata": {}, + "source": [ + "### Beschreibung und Begründung zur Erzeugung der Grundgesamtheit\n", + "\n", + "Diese Daten liegen dem offiziellen **Mobilitätsbericht** aus 2023 zugrunde (https://www.mobilitaet-in-deutschland.de/pdf/MiD2023_Kurzbericht.pdf):\n", + "- **Unterwegszeit**: metrisch; Mittelwert: 28,4min; Median: 15min\n", + "- **Streckenlänge**: metrisch, Mittelwert: 11,9km; Median: 3,8km\n", + "- **Durchschnittliche Geschwindigkeit**: Die mittlere Geschwindigkeit ergibt sich aus der Summe aller Streckenlängen geteilt durch die Summe aller unterwegszeiten: \n", + "$\\overline{v} = \\frac{\\sum \\text{Streckenlänge}}{\\sum \\text{Unterwegszeit}} \\approx$ 12 km/h\n", + "- **Fahrzeitpunkt:** Ordinalskaliert, 7 Ausprägungen: \n", + " - frühmorgens (5 bis vor 8 Uhr): ungefähr 30 Mio\n", + " - morgens (8 bis vor 10 Uhr): ungefähr 31 Mio \n", + " - vormittags (10 bis vor 13 Uhr): ungefähr 51 Mio \n", + " - mittags (13 bis vor 16 Uhr): ungefähr 59 Mio \n", + " - nachmittags (16 bis vor 19 Uhr): ungefähr 61 Mio \n", + " - abends (19 bis vor 22 Uhr): ungefähr 22 Mio \n", + " - nachts (22 bis vor 5 Uhr): ungefähr 7 Mio \n", + "\n", + "Diese Daten wurden hypothetisch basierend auf realistischen Annahmen für typische Fahrten erstellt:\n", + "- **Schaltverhalten:** Nominalskaliert, 3 Ausprägungen. Wahrscheinlichkeiten basieren auf Erfahrungswerten (früh: 30%, normal: 50%, spät: 20%).\n", + "- **Drehzahl, Außentemperatur:** Metrisch, normalverteilt. Parameter basieren auf realistischen Annahmen für typische Fahrten.\n", + "- **Anzahl Stopps:** Abhängig von Fahrtdauer, da längere Fahrten tendenziell mehr Stopps beinhalten.\n", + "- **Zielvariable:** Funktional abhängig von Schaltverhalten, Fahrzeitpunkt, Drehzahl und Geschwindigkeit. Die logistische Funktion stellt sicher, dass die Annahmen des logistischen Regressionsmodells erfüllt sind, weil wir eine Klassifizierung haben. Ein Störterm sorgt für realistische Varianz." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a08fa8f8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Grafische Darstellung der Verteilungen\n", + "fig, axs = plt.subplots(2, 3, figsize=(18, 10))\n", + "sns.countplot(x=data['shift_behavior'], ax=axs[0,0])\n", + "axs[0,0].set_title('Schaltverhalten')\n", + "sns.countplot(x=data['fahrzeitpunkt'], ax=axs[0,1])\n", + "axs[0,1].set_title('Fahrzeitpunkt')\n", + "sns.histplot(data['drehzahl'], bins=50, ax=axs[0,2])\n", + "axs[0,2].set_title('Drehzahl')\n", + "sns.histplot(data['geschwindigkeit'], bins=50, ax=axs[1,0])\n", + "axs[1,0].set_title('Geschwindigkeit')\n", + "sns.histplot(data['streckenlaenge'], bins=50, ax=axs[1,1])\n", + "axs[1,1].set_title('Streckenlänge')\n", + "sns.histplot(data['unterwegszeit'], bins=50, ax=axs[1,2])\n", + "axs[1,2].set_title('unterwegszeit')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}