Spatial Dependency Patterns in Weather-Enhanced Bike-Share Demand Forecasting_RK

📌 Overview This repository contains the full implementation of a spatially-adapted permutation-based dependency analysis framework for urban bike-sharing demand, applied to Washington DC Capital Bikeshare trip data from January 2026. It accompanies the paper: "Beyond Proximity: Spatial Dependency in Washington DC Bike-Sharing — Weather-Augmented ConvLSTM Evidence"Findings Journal — Transport Research (Submitted 2026) The core question: do spatial dependencies in bike-sharing demand follow geographic proximity, or are they governed by functional urban linkages? We answer this by training ConvLSTM models for member and casual users separately, augmenting them with real weather data, and computing a 64×64 permutation-based spatial dependency matrix Φ for each user type. 🎯 Aim & Scope Study area Washington DC, USA Data source Capital Bikeshare Open Data Period January 2026 (251,633 trips) Spatial resolution 8×8 regular grid (64 cells, ≈1,500 m each) Temporal resolution 30-minute intervals User types Member (205,329 trips) and Casual (46,304 trips) Weather source Open-Meteo Historical Archive API What this project establishes: Whether spatial influence in bike-sharing conforms to the geographic proximity assumption How member and casual users differ in their spatial dependency structures Whether weather augmentation improves winter demand prediction beyond spatiotemporal models alone A replication and extension of Miao et al. (2025) from NYC to a structurally distinct city How the Script Works The pipeline runs in 18 sequential cells in Google Colab: DATA FEATURES MODEL ANALYSIS │ │ │ │ ▼ ▼ ▼ ▼ Capital Weather API ConvLSTM Permutation Bikeshare ──► (Open-Meteo) ──► (3 layers) ──► Dependency Trip CSV + Temporal + Dense Matrix Φ Features Branch (64×64 per (7 inputs) Separate user type) Member/Casual 🧠 Model Architecture Spatial Input External Input [batch, 4, 8, 8, 2] [batch, 7] │ │ ConvLSTM2D (64) Dense (32, ReLU) BatchNorm + Dropout(0.2) BatchNorm │ Dense (64, ReLU) ConvLSTM2D (64) │ BatchNorm + Dropout(0.2) Reshape (1,1,64) │ UpSampling2D (8×8) ConvLSTM2D (64) │ BatchNorm │ └──────────┬────────────────────┘ │ Concatenate │ Conv2D(2, 1×1, ReLU) │ Output [batch, 8, 8, 2] (pickups + dropoffs)