reutlingen-university/1_data_science/simulationsstudie/yann_ahlgrim_simulationsstudie.ipynb

{
 "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": "f04292ef",
   "metadata": {},
   "source": [
    "## Erzeugung der Grundgesamtheit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5fe9381",
   "metadata": {},
   "source": [
    "### Datengrundlage\n",
    "\n",
    "#### Erklärende Variablen:\n",
    "1. **Durchschnittsgeschwindigkeit (metrisch):** Durchschnittliche Fahrgeschwindigkeit (z.B. über eine Fahrt oder ein Zeitfenster). Natürliche Verteilung z. B. etwa normal um ungefähr 47 km/h (29 mph) mit breiter Streuung. Aggressive Fahrer neigen tendenziell zu höheren Mittelwerten. Diese Variable ist voraussichtlich prädiktiv, da ein anderer Fahrer oft abweichende Geschwindigkeitsprofile hat\n",
    "    \n",
    "    → Die Verteilung der Variable wurde basierend auf dieser Studie in USA erstellt: https://www.nhtsa.gov/sites/nhtsa.gov/files/100carmain.pdf (100 Autos, 241 Fahrer)\n",
    "    \n",
    "2. **Längsbeschleunigung (metrisch):** Maß für das Beschleunigungsverhalten (z.B. Wurzel aus mittlerem Quadrat der Längsbeschleunigung über die Fahrt). Naturalistisch gesammelte Daten zeigen, dass Beschleunigungsprofile relevant für Fahrstil sind. Bei älteren Fahrern wurden insbesondere Längsbeschleunigungsmuster als informativer befunden. Wir simulieren etwa eine Verteilung um Null mit positiver Spitze (Fahrerspezifikationen). Moderate Abhängigkeit zu Bremsereignissen durch aggressives Fahrverhalten.\n",
    "→ Die Erkenntnisse basieren auf diesem Report vom Louisiana Transportation Research Center: https://www.ltrc.lsu.edu/pdf/2017/FR_580.pdf\n",
    "3. **Harte Bremsmanöver (metrisch):** Harte Bremsmanöver sind Fahrmanöver mit hoher negativer Beschleunigung (oft >0,3g). Tesla definiert beispielsweise „Hard Braking“ ab etwa 0,3g Bremsverzögerung (https://www.findmyelectric.com/blog/tesla-safety-score-beta-explained). Wir modellieren diese Zahl z.B. Poisson-verteilt mit kleinem Mittelwert (einige Ereignisse pro 100 km zb). Harte Bremsungen korrelieren moderat mit aggressiven Kurvenfahren. Diese Variable ist prädiktiv (abweichender Fahrer bremst anders).\n",
    "4. **Aggressive Kurvenfahrten (metrisch):** Anzahl oder Rate von extremen Kurvenmanövern (z.B. seitliche Beschleunigung >0,4g). Tesla definiert so „aggressives Kurvenfahren“ (https://www.findmyelectric.com/blog/tesla-safety-score-beta-explained/). Wir simulieren z.B. eine Poisson-Verteilung mit sehr kleinem Mittelwert (im Schnitt wenig Kurven mit >0,4g). Diese Variable ist prädiktiv, da ein fremder Fahrer oft anders (bzw aggressiver) durch Kurven fährt.\n",
    "\n",
    "#### Nicht Erklärende Variablen:\n",
    "5. **Straßentyp (nominal, 3 Kategorien):**\n",
    "    1. auf den deutschen Autobahnen wird ungefähr 30-33% der gesamten Kraftfahrzeugleistung abgewickelt (https://www.udv.de/resource/blob/130998/103ded9eaf113e61851a464843e306d2/79-vs-auf-bab-data.pdf)\n",
    "    2.  43 % der Pkw-Kilometer werden außerorts zurückgelegt (https://openumwelt.de/server/api/core/bitstreams/07bd23f9-ff0a-41e5-b89b-8d7baf0a5c36/content)\n",
    "    3. demnach bleiben ungefähr 24-27% als innerorts zurückgelegte Strecke\n",
    "    \n",
    "    Wir modellieren dies als nominale Zufallsvariable (z.B. multinomial mit diesen Anteilen). Straßentyp beeinflusst zwar Tempo und Beschleunigung, ist aber selbst nur eine Kontextinformation, **kein direkter Prädiktor** für einen Fahrertausch.\n",
    "    \n",
    "6. **Wetterbedingungen (nominal, 3 Kategorien):** Diese Kontextvariable beschreibt die Witterungsverhältnisse während einer Fahrt – trocken, nass oder winterlich (Schnee/Eis). Sie wird als **nicht erklärende Variable** im Modell geführt, da sie nicht direkt auf die Identität des Fahrers hinweist, aber realistische Umweltbedingungen abbildet. Die Einteilung basiert auf Daten des Statistischen Bundesamtes: Etwa 70–75 % aller Fahrten finden unter trockenen Bedingungen statt, 20–25 % bei Nässe und unter 5 % bei Schnee oder Glätte.\n",
    "\n",
    "Von der Mobilitätsstudie 2017 in Deutschland sind folgende Variablen abzuleiten (https://www.bmv.de/SharedDocs/DE/Anlage/G/mid-ergebnisbericht.pdf):\n",
    "\n",
    "1. **Fahrstrecke (metrisch):** Länge der Fahrt in Kilometern. Aus dem Mobilitätsverhalten in Deutschland beträgt die durchschnittliche Weglänge ca. 12 km. Wir simulieren die Fahrstrecke z.B. lognormal um diesen Mittelwert. Diese Variable steht nur indirekt mit „Fahrverhalten“ im Sinne von Nutzung  in Zusammenhang und ist **nicht erklärend** für den Fahrertausch.\n",
    "2. **Wochentag (nominal, 2–3 Kategorien):** Wochentag oder Woche vs. Wochenende. Verkehrsdaten zeigen z.B. an Wochentagen ca. 3,7 Wege pro Person/Tag, an Sonntagen nur etwa 2,1 Wege. Man kann Kategorien *Werktag* vs *Wochenende* (oder Mo/Di–Fr/Sa/So) bilden. Diese Variable ist für einen Fahrerwechsel irrelevant (sie gehört zum Fahrtenkontext, nicht zur Fahrverhalten)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1468207b",
   "metadata": {},
   "source": [
    "### Bibliotheken Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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, poisson, lognorm, bernoulli\n",
    "\n",
    "np.random.seed(11)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27947656",
   "metadata": {},
   "source": [
    "### Erklärende Variablen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61247da5",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 1_000_000\n",
    "\n",
    "# 1. Durchschnittsgeschwindigkeit (normalverteilt um 47 km/h)\n",
    "speed_mean = 47\n",
    "speed_std = 10\n",
    "avg_speed = np.random.normal(loc=speed_mean, scale=speed_std, size=N)\n",
    "\n",
    "# 2. Längsbeschleunigung (symmetrisch um 0, leicht positiv verzerrt)\n",
    "long_acc = np.random.normal(loc=0.2, scale=0.5, size=N)\n",
    "\n",
    "# 3. Harte Bremsmanöver (Poisson, durchschnittlich 3 pro 100km)\n",
    "hard_brakes = np.random.poisson(lam=2.5, size=N)\n",
    "\n",
    "# 4. Aggressive Kurvenfahrten (Poisson, kleiner Mittelwert)\n",
    "aggressive_curves = np.random.poisson(lam=0.8, size=N)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ee5dad",
   "metadata": {},
   "source": [
    "### Nicht Erklärende Variablen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d6b78d6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. Straßentyp (Multinomial: 33% Autobahn, 43% außerorts, 24% innerorts)\n",
    "road_type_probs = [0.33, 0.43, 0.24]\n",
    "road_type = np.random.choice(['Autobahn', 'Ausserorts', 'Innerorts'], size=N, p=road_type_probs)\n",
    "\n",
    "# 6. Wetter (Multinomial: 72% trocken, 23% nass, 5% Schnee)\n",
    "weather_probs = [0.72, 0.23, 0.05]\n",
    "weather = np.random.choice(['Trocken', 'Nass', 'Schnee'], size=N, p=weather_probs)\n",
    "\n",
    "# 7. Fahrstrecke (Lognormal mit Mittelwert ca. 12km)\n",
    "mu = np.log(12) - 0.5 * 0.4**2\n",
    "sigma = 0.4\n",
    "trip_distance = np.random.lognormal(mean=mu, sigma=sigma, size=N)\n",
    "\n",
    "# 8. Wochentag (Werktag oder Wochenende)\n",
    "weekday = np.random.choice(['Werktag', 'Wochenende'], size=N, p=[0.7, 0.3])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1b403f2",
   "metadata": {},
   "source": [
    "### Abhängigkeiten zwischen erklärenden Variablen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f10e1366",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Korrelation 1: Geschwindigkeit beeinflusst aggressive Kurven leicht\n",
    "aggressive_curves = np.random.poisson(lam=0.005 * avg_speed + 0.5)\n",
    "\n",
    "# Korrelation 2: Harte Bremsen beeinflussen Längsbeschleunigung\n",
    "long_acc = 0.2 + 0.1 * hard_brakes + np.random.normal(0, 0.3, size=N)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10570201",
   "metadata": {},
   "source": [
    "### Zielvariable: Fahrerwechsel (1 = ja / 0 = nein)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3791cb88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regressionskoeffizienten (logistische Regression)\n",
    "beta_0 = -3  # Basiswahrscheinlichkeit\n",
    "beta_speed = 0.05\n",
    "beta_acc = 0.4\n",
    "beta_brake = 0.3\n",
    "beta_curve = 0.6\n",
    "\n",
    "# Lineare Kombination + Störterm\n",
    "logit = (\n",
    "    beta_0\n",
    "    + beta_speed * avg_speed\n",
    "    + beta_acc * long_acc\n",
    "    + beta_brake * hard_brakes\n",
    "    + beta_curve * aggressive_curves\n",
    ")\n",
    "\n",
    "# Wahrscheinlichkeiten mit Sigmoid\n",
    "pi = 1 / (1 + np.exp(-logit))\n",
    "\n",
    "# Zielvariable: Fahrerwechsel\n",
    "driver_change = np.random.binomial(n=1, p=pi)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be2c0596",
   "metadata": {},
   "source": [
    "### Als Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ab858b82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_speed</th>\n",
       "      <th>long_acc</th>\n",
       "      <th>hard_brakes</th>\n",
       "      <th>aggressive_curves</th>\n",
       "      <th>road_type</th>\n",
       "      <th>weather</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>weekday</th>\n",
       "      <th>driver_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64.494547</td>\n",
       "      <td>0.472002</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Autobahn</td>\n",
       "      <td>Trocken</td>\n",
       "      <td>9.538071</td>\n",
       "      <td>Wochenende</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.139270</td>\n",
       "      <td>0.296863</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Innerorts</td>\n",
       "      <td>Trocken</td>\n",
       "      <td>11.746167</td>\n",
       "      <td>Werktag</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42.154349</td>\n",
       "      <td>0.806673</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Innerorts</td>\n",
       "      <td>Trocken</td>\n",
       "      <td>4.822888</td>\n",
       "      <td>Werktag</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.466814</td>\n",
       "      <td>0.512172</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Innerorts</td>\n",
       "      <td>Trocken</td>\n",
       "      <td>12.270326</td>\n",
       "      <td>Werktag</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46.917154</td>\n",
       "      <td>0.621950</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Ausserorts</td>\n",
       "      <td>Trocken</td>\n",
       "      <td>16.073552</td>\n",
       "      <td>Wochenende</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_speed  long_acc  hard_brakes  aggressive_curves   road_type  weather  \\\n",
       "0  64.494547  0.472002            1                  1    Autobahn  Trocken   \n",
       "1  44.139270  0.296863            2                  0   Innerorts  Trocken   \n",
       "2  42.154349  0.806673            4                  2   Innerorts  Trocken   \n",
       "3  20.466814  0.512172            1                  1   Innerorts  Trocken   \n",
       "4  46.917154  0.621950            3                  2  Ausserorts  Trocken   \n",
       "\n",
       "   trip_distance     weekday  driver_change  \n",
       "0       9.538071  Wochenende              1  \n",
       "1      11.746167     Werktag              0  \n",
       "2       4.822888     Werktag              1  \n",
       "3      12.270326     Werktag              0  \n",
       "4      16.073552  Wochenende              1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'avg_speed': avg_speed,\n",
    "    'long_acc': long_acc,\n",
    "    'hard_brakes': hard_brakes,\n",
    "    'aggressive_curves': aggressive_curves,\n",
    "    'road_type': road_type,\n",
    "    'weather': weather,\n",
    "    'trip_distance': trip_distance,\n",
    "    'weekday': weekday,\n",
    "    'driver_change': driver_change\n",
    "})\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6366cd7c",
   "metadata": {},
   "source": [
    "### Visualisierung der metrischen erklärenden Variablen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a08fa8f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
    "\n",
    "sns.histplot(df['avg_speed'], bins=50, kde=True, ax=axes[0, 0], color='skyblue')\n",
    "axes[0, 0].set_title('Verteilung: Durchschnittsgeschwindigkeit')\n",
    "\n",
    "sns.histplot(df['long_acc'], bins=50, kde=True, ax=axes[0, 1], color='lightgreen')\n",
    "axes[0, 1].set_title('Verteilung: Längsbeschleunigung')\n",
    "\n",
    "sns.histplot(df['hard_brakes'], bins=30, kde=False, ax=axes[1, 0], color='salmon')\n",
    "axes[1, 0].set_title('Verteilung: Harte Bremsmanöver')\n",
    "\n",
    "sns.histplot(df['aggressive_curves'], bins=30, kde=False, ax=axes[1, 1], color='orange')\n",
    "axes[1, 1].set_title('Verteilung: Aggressive Kurvenfahrten')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "777f980e",
   "metadata": {},
   "source": [
    "### Zielvariable Verteilung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f3b72ccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='driver_change', data=df, palette='Set2')\n",
    "plt.title('Verteilung der Zielvariable: Fahrerwechsel')\n",
    "plt.xlabel('Fahrerwechsel (0 = nein, 1 = ja)')\n",
    "plt.ylabel('Anzahl')\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
}