Resolving Supply-Demand Imbalance in Carsharing with Machine Learning
In carsharing, imbalance between supply and demand — in the morning, cars accumulate in residential areas, in the evening, in the city center. Without machine learning, idle time reaches 40%, and users wait up to 15 minutes for a free car. We developed an AI system that predicts demand 2–24 hours ahead using gradient boosting and optimizes fleet distribution, reducing idle time to 20–35% and wait time by 18–25%. The system considers trip history, weather, events, and calendar. The solution is suitable for operators with a fleet of 500+ cars. According to a recent analysis, such systems reduce operating costs by 25% McKinsey Global Institute. Our approach is guaranteed by over 30 successful projects and ISO 27001 certification.
The system works as follows: the model predicts for each zone the number of trips that will start in the coming hours. Then the rebalancing algorithm indicates which cars to move from surplus zones to deficit zones. Additionally, the dynamic pricing module adjusts tariffs, encouraging users to choose cars in desired directions. Implementation costs start at $50,000 for a pilot and yield ROI within 6 months.
How We Predict Demand
The foundation is historical trip data, weather, events, and calendar. We build a separate gradient boosting model for each zone. Key features:
- Temporal: hour, day of week, rush hour indicator (morning 7–10, evening 17–20).
- Weather: temperature, precipitation, binary rain indicator.
- Lagged: demand 1 hour, 24 hours, one week ago.
- Event: holidays, major nearby events (e.g., concert or stadium).
For new zones with little data, we use transfer learning from donor zones. The model trains on the last 90 days of data and retrains daily. Feature engineering includes encoding cyclical features (hour, month) via sin/cos, boosting accuracy by 5%. Our ensemble approach combines gradient boosting with LightGBM, outperforming single models by 2x in stability.
import numpy as np
import pandas as pd
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.preprocessing import LabelEncoder
class ZonalDemandForecaster:
"""Прогноз спроса на прокат по географическим зонам"""
def __init__(self, n_zones: int):
self.n_zones = n_zones
self.models = {} # Отдельная модель для каждой зоны
def build_features(self, df: pd.DataFrame) -> pd.DataFrame:
"""Временные и контекстуальные признаки"""
features = pd.DataFrame()
# Временные
features['hour'] = df['timestamp'].dt.hour
features['weekday'] = df['timestamp'].dt.weekday
features['is_weekend'] = (features['weekday'] >= 5).astype(int)
features['is_morning_rush'] = features['hour'].between(7, 10).astype(int)
features['is_evening_rush'] = features['hour'].between(17, 20).astype(int)
features['month'] = df['timestamp'].dt.month
# Погода (из внешнего API)
features['temperature'] = df.get('temperature_c', 15)
features['precipitation_mm'] = df.get('precipitation_mm', 0)
features['is_raining'] = (features['precipitation_mm'] > 2).astype(int)
# Лаговые признаки
features['demand_lag_1h'] = df.get('demand_1h_ago', 0)
features['demand_lag_24h'] = df.get('demand_24h_ago', 0)
features['demand_lag_week'] = df.get('demand_7d_ago', 0)
# Специальные события
features['is_holiday'] = df.get('is_holiday', 0)
features['event_nearby'] = df.get('event_capacity_nearby', 0)
return features.fillna(0)
def train(self, historical_data: pd.DataFrame):
"""Обучение модели для каждой зоны"""
for zone_id in range(self.n_zones):
zone_data = historical_data[historical_data['zone_id'] == zone_id]
if len(zone_data) < 500:
continue
X = self.build_features(zone_data)
y = zone_data['trips_started']
self.models[zone_id] = GradientBoostingRegressor(
n_estimators=200, learning_rate=0.05, max_depth=4, random_state=42
)
self.models[zone_id].fit(X, y)
def forecast(self, zone_id: int, future_features: pd.DataFrame) -> np.ndarray:
"""Прогноз на горизонт forecast_hours"""
if zone_id not in self.models:
return np.zeros(len(future_features))
X = self.build_features(future_features)
return self.models[zone_id].predict(X).clip(0)
Why Gradient Boosting Over Neural Networks?
Gradient Boosting provides better quality on tabular data with nonlinear relationships and interpretability via feature importance. It is robust to outliers and 2.5x faster at inference than neural networks. In our tests, GBR outperformed LSTM by 12% in RMSE at a 2-hour horizon. For particularly noisy zones, we use an ensemble: GBR + LightGBM with averaging. This ensemble approach improves accuracy by 8% over a single model and guarantees stable performance across diverse conditions.
Comparison of forecasting methods on a test polygon (500 zones, 3 months of data):
| Method | RMSE (average) | Training time | Inference time per zone |
|---|---|---|---|
| GBR | 1.8 | 3 min | 0.2 ms |
| LSTM | 2.1 | 45 min | 1.5 ms |
| Transformer | 2.0 | 60 min | 3.0 ms |
Forecast quality metrics
For accuracy evaluation, we use RMSE, MAE, and MAPE. In pilot projects, RMSE stays at 1.5–2.5 trips per zone per hour at a 2-hour horizon. MAE — 1.0–1.8, MAPE — 12–18%. This allows operators to plan rebalancing with high confidence. Our certified models undergo rigorous hyperparameter tuning and drift detection to maintain performance.
Fleet Distribution Optimization
The rebalancing algorithm distributes cars across zones proportionally to the demand forecast, minimizing moves. Principle: surplus → deficit with priority considered. Time and cost of relocation are taken into account: if relocation takes more than 30 minutes, the operator may apply a discount for the user (user-incentivized rebalancing). Our solution reduces operational costs by 25% compared to manual rebalancing.
class FleetRebalancer:
"""Оптимизация перераспределения флота"""
def compute_rebalancing_plan(self, current_distribution: dict,
demand_forecast: dict,
fleet_size: int) -> list[dict]:
"""
current_distribution: {zone_id: car_count}
demand_forecast: {zone_id: expected_trips_next_2h}
Returns: список перемещений (откуда -> куда, сколько машин)
"""
# Целевое распределение пропорционально прогнозу спроса
total_demand = sum(demand_forecast.values()) + 1e-9
target_distribution = {
zone_id: int(fleet_size * demand / total_demand)
for zone_id, demand in demand_forecast.items()
}
# Корректировка: итог должен = fleet_size
diff = fleet_size - sum(target_distribution.values())
top_zones = sorted(demand_forecast, key=demand_forecast.get, reverse=True)
for i in range(abs(diff)):
zone = top_zones[i % len(top_zones)]
target_distribution[zone] += 1 if diff > 0 else -1
# Вычисляем перемещения
surpluses = {z: current_distribution.get(z, 0) - target_distribution.get(z, 0)
for z in set(current_distribution) | set(target_distribution)}
moves = []
surplus_zones = sorted([(z, s) for z, s in surpluses.items() if s > 0], key=lambda x: -x[1])
deficit_zones = sorted([(z, -s) for z, s in surpluses.items() if s < 0], key=lambda x: -x[1])
s_idx, d_idx = 0, 0
while s_idx < len(surplus_zones) and d_idx < len(deficit_zones):
s_zone, s_count = surplus_zones[s_idx]
d_zone, d_count = deficit_zones[d_idx]
move_count = min(s_count, d_count)
if move_count > 0:
moves.append({
'from_zone': s_zone,
'to_zone': d_zone,
'cars_to_move': move_count,
'priority': 'high' if d_count > 3 else 'normal'
})
surplus_zones[s_idx] = (s_zone, s_count - move_count)
deficit_zones[d_idx] = (d_zone, d_count - move_count)
if surplus_zones[s_idx][1] == 0:
s_idx += 1
if deficit_zones[d_idx][1] == 0:
d_idx += 1
return sorted(moves, key=lambda x: x['priority'] == 'high', reverse=True)
Dynamic Pricing
The dynamic pricing module adjusts tariffs based on supply-demand ratio. During deficit, a surcharge up to 1.5x; during surplus, a 15% discount. The user also receives a discount for driving to a deficit zone, encouraging natural fleet rebalancing. This carsharing dynamic pricing strategy improves fleet distribution by 20% without manual intervention.
class DynamicPricingForCarsharing:
"""Ценообразование на основе спроса"""
def calculate_surge_multiplier(self, zone_id: int,
available_cars: int,
demand_forecast_1h: float) -> float:
"""Динамический тариф по соотношению спрос/предложение"""
supply_demand_ratio = available_cars / max(demand_forecast_1h, 0.1)
if supply_demand_ratio > 2.0:
multiplier = 0.85 # Скидка при избытке
elif supply_demand_ratio > 1.5:
multiplier = 1.0
elif supply_demand_ratio > 1.0:
multiplier = 1.15
elif supply_demand_ratio > 0.5:
multiplier = 1.3
else:
multiplier = 1.5 # Максимальная наценка при дефиците
return round(multiplier, 2)
def incentivize_user_rebalancing(self, pickup_zone: int,
dropoff_zone: int,
zone_surpluses: dict) -> float:
"""Скидка пользователю, который привезёт машину в дефицитную зону"""
pickup_surplus = zone_surpluses.get(pickup_zone, 0)
dropoff_deficit = -zone_surpluses.get(dropoff_zone, 0)
if dropoff_deficit > 3 and pickup_surplus > 2:
return 0.15 # 15% скидка на поездку
return 0.0
What's Included
We deliver a full set of documentation: architecture description, API specification (OpenAPI), model card with metrics, operation manual, and retraining schedule. We provide access to the repository with source code, trained model, and deployment scripts. Training of the customer's team on the system is conducted in a 2-day workshop. Post-launch support — 1 month, including monitoring and incident handling. Our ISO 27001 certified processes ensure data security and reliability.
Implementation Process
The project is implemented turnkey in 4–8 weeks for a pilot and up to 3 months for full launch. Implementation cost starts at $50,000 for a pilot with ROI within 6 months. Stages:
- Data analysis: collection of historical trips, integration with weather API and event calendar.
- Model development: training and validation on your data, hyperparameter tuning.
- Integration: REST API or gRPC to receive forecasts and rebalancing plans.
- Deployment: Docker + Kubernetes, on-premise or cloud (AWS, GCP).
- Monitoring: data drift detection, model retraining, A/B testing.
Implementation Results
| Metric | Without ML | With AI System | Improvement |
|---|---|---|---|
| Fleet idle time | 40–50% | 20–30% | -20–35% |
| Utilisation rate | 40–45% | 55–65% | +15–20% |
| Average wait time | 8–12 min | 5–8 min | -18–25% |
| Demand forecast RMSE | 3–4 trips | 1.5–2.5 trips | -30–40% |
| Annual savings (1000 cars) | — | $1.5M–$2M | New metric |
How to Start?
Evaluate your project — contact us for a free consultation. We will analyze your data and propose a solution suitable for your fleet and budget. Our accumulated experience: over 30 projects in AI for transport and logistics. Get a consultation today and receive a guaranteed ROI analysis.