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Advanced ML Model for Mumbai Local Train Crowd Density Prediction
Uses RandomForest with enhanced feature engineering
"""
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score, classification_report
import joblib
import os
LABEL_MAP = {"Low": 0, "Medium": 1, "Extreme": 2}
REVERSE_LABEL_MAP = {0: "Low", 1: "Medium", 2: "Extreme"}
DAY_ORDER = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
DAY_MAP = {d: i for i, d in enumerate(DAY_ORDER)}
HOUR_WEIGHTS = {
5: 0.3, 6: 0.8, 7: 1.9, 8: 2.0, 9: 1.7,
10: 1.1, 11: 1.0, 12: 1.0, 13: 1.0, 14: 1.0,
15: 1.1, 16: 1.3, 17: 1.8, 18: 2.0, 19: 1.9,
20: 1.4, 21: 0.8, 22: 0.5
}
LINE_MAP = {"Western": 0, "Central": 1, "Harbour": 2}
DIR_MAP = {"Northbound": 0, "Southbound": 1}
MODEL_PATH = os.path.join(os.path.dirname(__file__), "../data/model.pkl")
ENCODERS_PATH = os.path.join(os.path.dirname(__file__), "../data/encoders.pkl")
def engineer_features(df: pd.DataFrame) -> pd.DataFrame:
df = df.copy()
df["day_num"] = df["day_of_week"].map(DAY_MAP)
df["is_weekday"] = (df["day_num"] < 5).astype(int)
df["is_weekend"] = (df["day_num"] >= 5).astype(int)
df["hour_weight"] = df["hour"].map(HOUR_WEIGHTS).fillna(0.5)
df["line_num"] = df["line"].map(LINE_MAP).fillna(0)
df["dir_num"] = df["direction"].map(DIR_MAP).fillna(0)
# Peak hour flags
df["is_morning_peak"] = ((df["hour"] >= 7) & (df["hour"] <= 9)).astype(int)
df["is_evening_peak"] = ((df["hour"] >= 17) & (df["hour"] <= 19)).astype(int)
df["is_peak"] = ((df["is_morning_peak"] == 1) | (df["is_evening_peak"] == 1)).astype(int)
df["is_offpeak"] = ((df["hour"] < 6) | (df["hour"] > 21)).astype(int)
# Directional rush interactions
df["morning_southbound"] = ((df["is_morning_peak"] == 1) & (df["dir_num"] == 1)).astype(int)
df["evening_northbound"] = ((df["is_evening_peak"] == 1) & (df["dir_num"] == 0)).astype(int)
df["rush_dir_interaction"] = df["morning_southbound"] + df["evening_northbound"]
# Weekend peak dampening
df["effective_weight"] = df["hour_weight"] * (1.2 if 1 else 1.0)
df["weekday_weight"] = df["hour_weight"] * df["is_weekday"] * 1.2 + df["hour_weight"] * df["is_weekend"] * 0.8
# Station encoding (label encoding)
le_station = LabelEncoder()
le_dest = LabelEncoder()
all_stations = list(pd.concat([df["station"], df["destination"]]).unique())
le_station.fit(all_stations)
le_dest.fit(all_stations)
df["station_enc"] = le_station.transform(df["station"])
df["dest_enc"] = le_dest.transform(df["destination"])
return df, le_station, le_dest
FEATURE_COLS = [
"hour", "day_num", "is_weekday", "is_weekend",
"hour_weight", "line_num", "dir_num",
"is_morning_peak", "is_evening_peak", "is_peak", "is_offpeak",
"morning_southbound", "evening_northbound", "rush_dir_interaction",
"weekday_weight", "station_enc", "dest_enc"
]
def train_model(df: pd.DataFrame):
df_feat, le_station, le_dest = engineer_features(df)
df_feat["crowd_label"] = df_feat["crowd_level"].map(LABEL_MAP)
X = df_feat[FEATURE_COLS]
y = df_feat["crowd_label"]
model = RandomForestClassifier(
n_estimators=200,
max_depth=12,
min_samples_split=4,
random_state=42,
n_jobs=-1,
class_weight="balanced"
)
model.fit(X, y)
preds = model.predict(X)
acc = accuracy_score(y, preds)
report = classification_report(y, preds, target_names=["Low", "Medium", "Extreme"])
# Save model
os.makedirs(os.path.dirname(MODEL_PATH), exist_ok=True)
joblib.dump(model, MODEL_PATH)
joblib.dump({"le_station": le_station, "le_dest": le_dest}, ENCODERS_PATH)
return model, acc, report, le_station, le_dest
def load_model():
if os.path.exists(MODEL_PATH) and os.path.exists(ENCODERS_PATH):
model = joblib.load(MODEL_PATH)
encoders = joblib.load(ENCODERS_PATH)
return model, encoders["le_station"], encoders["le_dest"]
return None, None, None
def predict_crowd(
model, le_station, le_dest,
line: str, station: str, destination: str,
direction: str, hour: int, day_of_week: str
) -> dict:
try:
station_enc = le_station.transform([station])[0]
dest_enc = le_dest.transform([destination])[0]
except ValueError:
station_enc = 0
dest_enc = 0
day_num = DAY_MAP.get(day_of_week, 0)
is_weekday = int(day_num < 5)
is_weekend = int(day_num >= 5)
hour_weight = HOUR_WEIGHTS.get(hour, 0.5)
line_num = LINE_MAP.get(line, 0)
dir_num = DIR_MAP.get(direction, 0)
is_morning_peak = int(7 <= hour <= 9)
is_evening_peak = int(17 <= hour <= 19)
is_peak = int(is_morning_peak or is_evening_peak)
is_offpeak = int(hour < 6 or hour > 21)
morning_southbound = int(is_morning_peak and dir_num == 1)
evening_northbound = int(is_evening_peak and dir_num == 0)
rush_dir = morning_southbound + evening_northbound
weekday_weight = hour_weight * 1.2 if is_weekday else hour_weight * 0.8
features = [[
hour, day_num, is_weekday, is_weekend,
hour_weight, line_num, dir_num,
is_morning_peak, is_evening_peak, is_peak, is_offpeak,
morning_southbound, evening_northbound, rush_dir,
weekday_weight, station_enc, dest_enc
]]
pred = model.predict(features)[0]
proba = model.predict_proba(features)[0]
label = REVERSE_LABEL_MAP[pred]
confidence = float(max(proba)) * 100
return {
"crowd_level": label,
"crowd_code": int(pred),
"confidence": round(confidence, 1),
"probabilities": {
"Low": round(float(proba[0]) * 100, 1),
"Medium": round(float(proba[1]) * 100, 1),
"Extreme": round(float(proba[2]) * 100, 1),
}
}
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