diff --git a/contribs/small-scale-traffic-generation/src/main/python/analyze_kid.py b/contribs/small-scale-traffic-generation/src/main/python/analyze_kid.py
new file mode 100644
index 00000000000..61e8bd32be1
--- /dev/null
+++ b/contribs/small-scale-traffic-generation/src/main/python/analyze_kid.py
@@ -0,0 +1,190 @@
+# -*- coding: utf-8 -*-
+
+from datetime import timedelta
+
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+# %%
+
+sns.set_style("ticks")
+sns.set_context("paper", font_scale=1.2, rc={"grid.linewidth": 1, "lines.linewidth": 2})
+# sns.set_palette("Set2")
+
+plt.rcParams['figure.dpi'] = 350
+plt.rcParams['pdf.fonttype'] = 42
+plt.rcParams['ps.fonttype'] = 42
+plt.rcParams['font.family'] = 'Arial'
+
+palette = sns.color_palette("Set2")
+
+# %%
+
+d = "/Users/rakow/Development/shared-svn/studies/countries/de/KiD_2002/Daten/"
+
+Fahrten = pd.read_csv(d + "KiD_2002_(Einzel)Fahrten-Datei.txt", delimiter="\t", encoding="ISO-8859-1")
+Ketten = pd.read_csv(d + "KiD_2002_Fahrtenketten-Datei.txt", delimiter="\t", encoding="ISO-8859-1")
+Fahrzeug = pd.read_csv(d + "KiD_2002_Fahrzeug-Datei.txt", delimiter="\t", encoding="ISO-8859-1")
+
+# %%
+
+Fahrten["start"] = pd.to_datetime(Fahrten["F04"], errors="coerce")
+Fahrten["end"] = pd.to_datetime(Fahrten["F10a"], errors="coerce")
+
+# %%
+
+# Filter commercial trips
+df = Fahrten[Fahrten["F07b"] == 1]
+
+df = df.merge(Fahrzeug, on="K00")
+df = df.rename(columns={"K91": "w", "F14": "dist"})
+
+df["type"] = df["F07a"].map(
+    {1: "goodsTraffic", 2: "commercialPersonTraffic", 3: "commercialPersonTraffic", 4: "commercialPersonTraffic",
+     5: "returnDepot"})
+df = df[["K00", "F00", "start", "end", "w", "dist", "type"]]
+
+df["start"] = df["start"].dt.hour * 60 + df["start"].dt.minute
+df["end"] = df["end"].dt.hour * 60 + df["end"].dt.minute
+
+# Filter valid ranges
+df = df[df.start <= df.end]
+
+df["duration"] = df.end - df.start
+
+df = df.dropna()
+
+purpose = df.groupby("K00").apply(
+    lambda x: "goodsTraffic" if "goodsTraffic" in set(x["type"]) else "commercialPersonTraffic")
+
+# %%
+
+durations = [0, 10, 20, 30, 40, 50, 60, 75, 90, 105, 120, 150, 180, 240, 300, 420, 540, 660, np.inf]
+
+df["dur_group"] = pd.cut(df.duration, durations)
+
+starts = [0, 4 * 60, 5 * 60, 6 * 60, 7 * 60, 8 * 60, 9 * 60, 10 * 60, 11 * 60, 12 * 60, 13 * 60, 14 * 60, 15 * 60,
+          16 * 60, 17 * 60, 18 * 60, 19 * 60, np.inf]
+start_labels = ["0-4", "4-5", "5-6", "6-7", "7-8", "8-9", "9-10", "10-11", "11-12", "12-13", "13-14", "14-15", "15-16",
+                "16-17", "17-18", "18-19", "19-24"]
+
+df["start_group"] = pd.cut(df.start, starts)
+
+# %%
+
+x = np.arange(start=0, stop=24 * 60, step=15, dtype=np.float64)
+
+# %%
+
+y = np.zeros_like(x)
+
+for t in df.itertuples():
+    idx = np.searchsorted(x, [t.start, t.end])
+    idx[1] += 1
+
+    y[slice(*idx)] += t.w
+
+y /= np.max(y / 1000)
+
+# %%
+
+fig, ax = plt.subplots(figsize=(8, 4))
+
+sns.lineplot(x=x, y=y, ax=ax)
+
+sns.despine()
+
+ax.xaxis.set_major_locator(ticker.FixedLocator(np.arange(start=0, stop=24 * 60, step=120, dtype=np.float64)))
+ax.xaxis.set_major_formatter(lambda x, y: str(timedelta(minutes=x))[:-3])
+
+# %%
+
+
+sns.histplot(data=df, x="duration", bins=durations)
+
+# %%
+
+# Only commercial tours
+
+Ketten["start"] = pd.to_datetime(Ketten["T04"], errors="coerce")
+
+tf = Ketten[Ketten.T07 == 1]
+
+tf = tf.merge(Fahrzeug, on="K00").set_index("K00")
+tf["type"] = purpose
+
+tf["start"] = tf["start"].dt.hour * 60 + tf["start"].dt.minute
+
+tf = tf.rename(columns={"K91": "w", "T01": "duration", "T05": "dist", "K03": "vWeight"})
+
+tf = tf[tf.duration > 0]
+
+tf = tf[["start", "w", "duration", "dist", "vWeight", "type"]]
+
+tf = tf.dropna()
+
+# %%
+
+vehicles = [0, 2800, 3500, 7500, 12000, 100000]
+
+tf["vClass"] = pd.cut(tf.vWeight, vehicles, labels=["vehType1", "vehType2", "vehType3", "vehType4", "vehType5"])
+
+t_durations = np.hstack(([0, 30, 60, 90], np.arange(120, 15 * 60, step=60, dtype=np.float64), [18 * 60]))
+
+tf["dur_group"] = pd.cut(tf.duration, t_durations)
+tf["start_group"] = pd.cut(tf.start, starts, labels=start_labels)
+
+tf = tf.dropna()
+
+# %%
+
+aggr = tf.groupby(["type", "start_group", "dur_group"]).agg(p=("w", "sum"))
+
+for group in ("goodsTraffic", "commercialPersonTraffic"):
+    sub = aggr.loc[group, :]
+    sub.p /= sub.p.sum()
+
+aggr = aggr.reset_index()
+
+target = "goodsTraffic"
+for a in aggr.itertuples():
+
+    if a.type != target or a.p <= 0:
+        continue
+
+    f, t = a.start_group.split("-")
+    lower, upper = a.dur_group.left, a.dur_group.right
+
+    print(f"Pair.create(new TourStartAndDuration({f}, {t}, {lower}, {upper}), {a.p}),")
+
+# %%
+
+fig, ax = plt.subplots(figsize=(8, 4))
+
+sns.histplot(data=tf, x="start", bins=starts, ax=ax)
+
+sns.despine()
+
+ax.xaxis.set_major_locator(ticker.FixedLocator(np.arange(start=0, stop=24 * 60, step=120, dtype=np.float64)))
+ax.xaxis.set_major_formatter(lambda x, y: str(timedelta(minutes=x))[:-3])
+
+# %%
+
+fig, ax = plt.subplots(figsize=(8, 4))
+
+sns.histplot(data=tf, x="duration", bins=t_durations, ax=ax)
+sns.despine()
+
+ax.xaxis.set_major_formatter(lambda x, y: int(x // 60))
+ax.xaxis.set_major_locator(ticker.FixedLocator(np.arange(start=0, stop=24 * 60, step=60, dtype=np.float64)))
+
+# %%
+
+grid = sns.FacetGrid(tf, col="start_group", col_wrap=6, palette="tab20c")
+
+grid.map_dataframe(sns.histplot, x="duration", bins=t_durations, stat="percent")
+
+# %%