diff --git a/project/report.ipynb b/project/report.ipynb
index e13cfc1b10..28e688de78 100644
--- a/project/report.ipynb
+++ b/project/report.ipynb
@@ -24,10 +24,6 @@
    "execution_count": 1,
    "id": "2c45372e-2f45-44fd-989e-8d9d67f9679a",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-10T02:27:57.951748Z",
-     "start_time": "2024-01-10T02:27:54.307158Z"
-    },
     "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
@@ -64,10 +60,6 @@
    "execution_count": 2,
    "id": "b6f54d3a60042094",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-10T02:28:00.714312Z",
-     "start_time": "2024-01-10T02:27:57.952475Z"
-    },
     "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
@@ -160,15 +152,10 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "f62695a3-f2ee-4166-8132-60384804d154",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-10T02:28:00.719167Z",
-     "start_time": "2024-01-10T02:28:00.715937Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "ride_frac = 0.1\n",
+    "ride_frac = 0.01\n",
     "ride_seed = 24\n",
     "cpus = 8\n",
     "\n",
@@ -190,12 +177,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "edce8ccb-822e-4f5e-88b2-3b75a9e8be8b",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-10T02:28:58.996073Z",
-     "start_time": "2024-01-10T02:28:00.722468Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "engine = sa.engine.create_engine(conn_string, echo=False, future=True)\n",
@@ -218,12 +200,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "fed81d9c-1f1a-42b4-b006-bd5c6d62c2d3",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-10T02:28:59.009495Z",
-     "start_time": "2024-01-10T02:28:58.996914Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "extract_ride_pipeline = Pipeline().register(RideExtractorBlock(url=ride_url, frac=ride_frac, seed=ride_seed)).register(\n",
@@ -249,22 +226,8 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "dac381df-f7c9-4534-8cad-139b759c80ba",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-10T02:46:07.047051Z",
-     "start_time": "2024-01-10T02:28:59.002790Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "start adding node-mappings\n",
-      "[{'id': 0, 'start_time': Timestamp('2022-10-08 03:02:00'), 'end_time': Timestamp('2022-10-08 03:24:00'), 'start_lat': 48.13289, 'start_lon': 11.52836, 'end_lat': 48.10425, 'end_lon': 11.56044, 'rental_is_station': False, 'rental_station_name': nan, 'return_is_station': False, 'return_station_name': nan, 'nodes': [<src.model.entities.Node object at 0x149c02950>, <src.model.entities.Node object at 0x14f186b10>, <src.model.entities.Node object at 0x1371d1c10>, <src.model.entities.Node object at 0x131391e90>, <src.model.entities.Node object at 0x131392550>, <src.model.entities.Node object at 0x131392450>, <src.model.entities.Node object at 0x131392bd0>, <src.model.entities.Node object at 0x14b570050>, <src.model.entities.Node object at 0x14b570650>, <src.model.entities.Node object at 0x131392310>, <src.model.entities.Node object at 0x14b570e50>, <src.model.entities.Node object at 0x14b5715d0>, <src.model.entities.Node object at 0x14b5718d0>, <src.model.entities.Node object at 0x14b571c90>, <src.model.entities.Node object at 0x14b571e10>, <src.model.entities.Node object at 0x14b571f50>, <src.model.entities.Node object at 0x14b572090>, <src.model.entities.Node object at 0x14b572290>, <src.model.entities.Node object at 0x14b572450>, <src.model.entities.Node object at 0x14b572790>, <src.model.entities.Node object at 0x14b572910>, <src.model.entities.Node object at 0x14b572b50>, <src.model.entities.Node object at 0x14b572fd0>, <src.model.entities.Node object at 0x14b573550>, <src.model.entities.Node object at 0x14b5737d0>, <src.model.entities.Node object at 0x14b573a10>, <src.model.entities.Node object at 0x14b573c10>, <src.model.entities.Node object at 0x14b573f10>, <src.model.entities.Node object at 0x14b571210>, <src.model.entities.Node object at 0x14b573c50>, <src.model.entities.Node object at 0x14b5701d0>, <src.model.entities.Node object at 0x14b5707d0>, <src.model.entities.Node object at 0x14b570990>, <src.model.entities.Node object at 0x14b570b90>, 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<src.model.entities.Node object at 0x14b576010>, <src.model.entities.Node object at 0x14b576a90>, <src.model.entities.Node object at 0x14b577010>]}, {'id': 4, 'start_time': Timestamp('2022-06-28 17:25:00'), 'end_time': Timestamp('2022-06-28 17:31:00'), 'start_lat': 48.14475, 'start_lon': 11.56007, 'end_lat': 48.15018, 'end_lon': 11.56677, 'rental_is_station': False, 'rental_station_name': nan, 'return_is_station': False, 'return_station_name': nan, 'nodes': [<src.model.entities.Node object at 0x14b577550>, <src.model.entities.Node object at 0x14b5779d0>, <src.model.entities.Node object at 0x14b577c90>, <src.model.entities.Node object at 0x14b574210>, <src.model.entities.Node object at 0x14b574410>, <src.model.entities.Node object at 0x14b575410>, <src.model.entities.Node object at 0x14b575690>, <src.model.entities.Node object at 0x14b575910>, <src.model.entities.Node object at 0x14b575cd0>, <src.model.entities.Node object at 0x14b575010>, <src.model.entities.Node object at 0x14b576c10>, <src.model.entities.Node object at 0x14b577790>, <src.model.entities.Node object at 0x14b577ed0>, <src.model.entities.Node object at 0x14b575a90>, <src.model.entities.Node object at 0x14b574290>]}]\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "ride_df, = extract_ride_pipeline.invoke()\n",
     "path_df, = extract_path_pipeline.invoke()\n",
@@ -289,12 +252,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "f10e58d6-0e8d-41e2-9478-f50893e9276d",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-10T02:46:17.259126Z",
-     "start_time": "2024-01-10T02:46:06.803263Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -462,10 +420,6 @@
    "execution_count": 8,
    "id": "3eb8f8e9e8690581",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-10T02:46:17.903519Z",
-     "start_time": "2024-01-10T02:46:12.649105Z"
-    },
     "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
@@ -609,10 +563,6 @@
    "execution_count": 9,
    "id": "9d3ce0c5bb11efdf",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-10T02:46:22.053936Z",
-     "start_time": "2024-01-10T02:46:13.603121Z"
-    },
     "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
@@ -715,10 +665,6 @@
    "execution_count": 10,
    "id": "c9f966e79f1a1608",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-10T02:46:32.202048Z",
-     "start_time": "2024-01-10T02:46:20.647150Z"
-    },
     "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
@@ -769,23 +715,20 @@
     "\n",
     "### a) Average Rides per Bicycle Path and Road per Month\n",
     "\n",
-    "This metric denotes the average number of MVG bike rides allocated to an infrastructure path in a given month. This metric distinguishes between bike paths and bike roades, providing insights into the distribution of MVG bike rides across these two types of cycling infrastructure."
+    "This metric denotes the average number of MVG bike rides allocated to an infrastructure path in a given month. Thereby, it distinguishes between bike paths and bike roades, providing insights into the distribution of MVG bike rides across these two types of cycling infrastructure.\n",
+    "\n",
+    "One notable challenge in the metric analysis pertains to the distortion of an absolute metric due to a minor fraction of the dataset. This issue compromises the precision and reliability of the findings, potentially engendering misleading interpretations. To address this concern and bolster the robustness of the metrics, a proposed solution involves the implementation of a weighting mechanism contingent on the ride_frac parameter. By incorporating this weighting factor, the influence of the problematic dataset fraction is intended to be mitigated, thus ensuring a more representative and accurate assessment of this metric. This strategic adjustment not only rectifies the current discrepancy but also contributes to the overall reliability and validity of the analytical approach."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "a6e7df1e-03e6-408b-868e-3b5651894459",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-10T02:51:46.691174Z",
-     "start_time": "2024-01-10T02:51:08.904547Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -860,18 +803,14 @@
    "source": [
     "### b) Average Share of Bicycle Paths and Roads per Ride per Month\n",
     "\n",
-    "This metric indicates the average proportion of MVG bike rides attributed to bike roades or bike paths, categorized by months."
+    "This metric indicates the average proportion of MVG bike rides attributed to bike roades or bike paths, categorized by months. In this case, there is no need for a weighting because it only measures a percentage value that wouldn't profit from an equal weighting of the numerator and denominator."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "61448149470d0ae0",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-01-10T02:53:13.440973Z",
-     "start_time": "2024-01-10T02:52:34.835160Z"
-    },
     "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
@@ -880,9 +819,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1000x400 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -907,7 +846,7 @@
     "        GROUP BY r.id, p.id, r.start_time\n",
     "        HAVING COUNT(*) = 2) AS pr\n",
     "    GROUP BY pr.ride_id, pr.start_time\n",
-    "\"\"\", con=engine)\n",
+    "\"\"\", index_col=\"ride_id\", con=engine)\n",
     "num_bicycle_roads_per_ride_df = pd.read_sql_query(\"\"\"\n",
     "    SELECT pr.ride_id, pr.start_time, COUNT(pr.path_id) AS num_paths\n",
     "    FROM (\n",
@@ -921,7 +860,7 @@
     "        GROUP BY r.id, p.id, r.start_time\n",
     "        HAVING COUNT(*) = 2) AS pr\n",
     "    GROUP BY pr.ride_id, pr.start_time\n",
-    "\"\"\", con=engine)\n",
+    "\"\"\", index_col=\"ride_id\", con=engine)\n",
     "num_edges_per_ride_df = pd.read_sql_query(\"\"\"\n",
     "    SELECT r.id AS ride_id, r.start_time, COUNT(rnm.node_id) - 1 AS num_edges\n",
     "    FROM ride r JOIN ride_node_mapping rnm ON r.id = rnm.ride_id\n",
@@ -934,7 +873,7 @@
     "half_bar_width = 0.2\n",
     "x_axis_months = np.arange(len(month_list))\n",
     "\n",
-    "plt.figure(figsize=(10, 4))\n",
+    "plt.figure(figsize=(10, 6))\n",
     "plt.bar(x_axis_months - half_bar_width, avg_share_bicycle_paths_per_ride_per_month_list, half_bar_width * 2, label=\"Bicycle Path\")\n",
     "plt.bar(x_axis_months + half_bar_width, avg_share_bicycle_roads_per_ride_per_month_list, half_bar_width * 2, label=\"Bicycle Road\")\n",
     "plt.xticks(x_axis_months, month_list)\n",
@@ -958,7 +897,19 @@
    "source": [
     "## Conclusion\n",
     "\n",
-    "As illustrated earlier, it is evident that metric a) yields better results than metric b). However, metric b) possesses greater significance in terms of assessing the impact of infrastructure on the utilization of MVG bikes. Therefore, further exploration of the dataset is warranted, with a focus on addressing potential issues related to the underlying mappings. Additionally, enhancing performance, possibly through the adoption of a more robust API or library, could prove beneficial. In conclusion, the question of whether there is a correlation between Munich's bicycle infrastructure and the usage of MVG bikes cannot be conclusively answered at this point."
+    "In summary, the exploration of Munich's regional bicycle infrastructure and its impact on the use of the MVG rental bike service has yielded significant insights. Metric a) underscores a robust correlation between the provided infrastructure, comprising bike paths and bike lanes in Munich, and the prevalence of MVG Rad rides. This highlights the pivotal role of dedicated cycling infrastructure in shaping the adoption patterns of bike-sharing systems within urban contexts.\n",
+    "\n",
+    "Additionally, Metric a) illuminates a temporal concentration of MVG Rad usage during the summer months. This observed seasonal variation aligns intuitively with higher bicycle usage in warmer weather, as reflected in the MVG dataset. The correlation between seasonal patterns and bike-sharing underscores the nuanced interplay between environmental factors and mobility preferences.\n",
+    "\n",
+    "Contrastingly, Metric b) reveals a relatively low percentage of MVG Rad routes traversing bike paths or especially bike roads. This suggests potential preferences for the calculated routes that tend to utilize regular streets or points to an inadequacy in Munich's infrastructure in providing dedicated cycling paths.\n",
+    "\n",
+    "However, these interpretations are subject to several limitations. The analysis relies on a significantly reduced MVG dataset due to computational constraints, limiting the depth of our insights. The calculation of the shortest route for individual MVG Rad rides is based solely on estimated travel time and start/end points, lacking precise route information. Furthermore, potential inaccuracies may arise from the conversion of UTM coordinates in the Path dataset and the calculation of the nearest OSM nodes based on longitude and latitude coordinates.\n",
+    "\n",
+    "It is essential to note that the metrics establish a connection between a ride and a path only when the entire path is traversed, potentially leading to disparities between the OSM graph and the data provided by the Baureferat. Additionally, the analysis is confined solely to Munich's data, limiting the generalizability of findings to other cities and bike-sharing service providers.\n",
+    "\n",
+    "Looking ahead, addressing these limitations would be imperative for refining the comprehensiveness of future investigations. Incorporating the whole MVG dataset and employing advanced methodologies could enhance the depth and accuracy of our analyses. Furthermore, extending the study to encompass data from other cities and diverse bike-sharing providers would contribute to a more comprehensive understanding of the universal and context-specific aspects of bike-sharing utilization.\n",
+    "\n",
+    "In conclusion, while this study sheds light on the intricate relationship between urban bicycle infrastructure and the MVG rental bike service in Munich, ongoing research endeavors are essential for refining our understanding and advancing the broader discourse on sustainable urban mobility. This study lays the groundwork for future investigations, emphasizing the importance of considering nuanced environmental and infrastructural factors in shaping urban mobility patterns."
    ]
   }
  ],
diff --git a/project/src/util/share_paths_per_ride_analysis.py b/project/src/util/share_paths_per_ride_analysis.py
index b675725094..e2c6ad3c2e 100644
--- a/project/src/util/share_paths_per_ride_analysis.py
+++ b/project/src/util/share_paths_per_ride_analysis.py
@@ -16,7 +16,10 @@ def calc_share_paths_per_ride(num_paths_per_ride_df: pd.DataFrame, num_edges_per
         num_edges_per_month_df = num_edges_per_ride_df[num_edges_months_sr == m]
         rides_per_month_df = ride_df[ride_months_sr == m]
 
-        num_edges_per_month_sub_df = num_edges_per_month_df.loc[num_paths_per_month_df["ride_id"]]
+        num_edges_per_month_sub_df = num_edges_per_month_df.loc[num_paths_per_month_df.index]
+
+        num_paths_per_month_df = num_paths_per_month_df[num_edges_per_month_sub_df["num_edges"] != 0]
+        num_edges_per_month_sub_df = num_edges_per_month_sub_df[num_edges_per_month_sub_df["num_edges"] != 0]
 
         ride_share_paths_per_ride_per_month_sr = (
                 num_paths_per_month_df["num_paths"] / num_edges_per_month_sub_df["num_edges"])