Problem Statement

Context and objectives of the Demand Forecasting System

What this solves: Bike-sharing systems lose revenue and customers when bikes aren’t where riders need them. This system uses weather-aware AI to predict demand hours in advance, enabling proactive fleet management.

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Business Context

Effective demand forecasting is the cornerstone of efficient urban mobility systems. For bike-sharing networks, matching supply with demand is critical to user satisfaction and operational efficiency.

Show Fleet Rebalancing

Ensuring bikes are available at high-demand stations while preventing full docks at destination hubs.

Show Operational Efficiency

Optimizing the schedules of redistribution trucks to move bikes proactively based on predicted spikes.

Show Service Availability

Minimizing “loss of service” events where users cannot find a bike or a dock during peak hours.

This system is designed to transition forecasting from static averages to a weather-aware MLOps workflow.

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Business Impact

Reduced Stockouts

Proactive rebalancing can reduce “no bike available” events by up to 35% during peak hours.

Operational Savings

Predictive scheduling optimizes redistribution truck routes, cutting unnecessary trips and fuel costs.

Data-Driven Decisions

Real-time dashboards give operations teams hourly demand visibility across the entire network.

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Who Benefits

Stakeholder	What They Get
Operations Managers	Hourly demand forecasts for proactive fleet rebalancing
Data Scientists	Reproducible ML pipelines with experiment tracking and model versioning
Engineering Teams	Production-ready API, Docker-based infrastructure, and CI/CD-ready architecture
Business Leaders	Measurable KPIs (prediction accuracy, uptime) and cost reduction visibility

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Objectives

1. Analyze Utilization: Ingest historical ride data to understand system-wide usage patterns.
2. Pattern Recognition: Identify complex dependencies between demand and environmental factors like Temperature, Humidity, and Hour of Day.
3. Predictive Accuracy: Deploy a CatBoost regressor to forecast the total count (cnt) of bike rentals for upcoming hours.
4. Operational Resilience: Ensure 99.9% pipeline uptime with automated self-healing and drift detection.

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Technical Goals

• Robust ML Pipeline: modular app-ml service using Kedro for reproducible processing.
• Scalable Orchestration: Distributed task execution via Celery Strategy.
• Production Readiness: Zero-dependency host setup via Docker Compose.

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Related Pages

Glossary

Plain-English explanations of every technical term used in these docs.

System Overview

How all the components connect in the full architecture.