Replication Data for: Spatial Platoon Context vs. Architectural Complexity: Predicting Headway Degradation in High-Frequency Bus Networks

This repository contains the dataset, evaluation code, and result outputs associated with the study "Spatial Platoon Context vs. Architectural Complexity: Predicting Headway Degradation in High-Frequency Bus Networks."

The core dataset encompasses over five months (October 2025 to March 2026) of continuous, high-resolution General Transit Feed Specification Realtime (GTFS-RT) telemetry from 30 diverse high-frequency bus routes in Melbourne, Australia.

Unlike traditional transit datasets that treat vehicles in isolation, this data has been heavily engineered to include a Platoon Spatial Matrix. Every row captures synchronous 5-minute snapshots of the physical kinematic state of an interacting transit platoon. The target variable is defined as Kinematic Convergence (Δ Gap): the physical change in the spatial gap between consecutive buses exactly 15 minutes into the future.

The repository provides the exact Python framework used to evaluate whether predicting this headway degradation requires deep historical sequence memory (Long Short-Term Memory, Multi-Headed Transformers) or if a simple instantaneous snapshot of the platoon (Deep Neural Networks, Linear Regression) is sufficient.

Keywords (Tags)
Bus Bunching; GTFS-Realtime; Headway Prediction; Deep Learning; Spatial Interaction; Public Transport; Transit Telemetry

本仓库包含与研究《队列空间上下文与架构复杂度：预测高频公交网络中的行车间隔衰减（Spatial Platoon Context vs. Architectural Complexity: Predicting Headway Degradation in High-Frequency Bus Networks）》相关的数据集、评估代码与结果输出。

核心数据集涵盖澳大利亚墨尔本30条多样化高频公交路线，包含2025年10月至2026年3月共计5个多月的连续高分辨率通用公交馈送规范实时版（General Transit Feed Specification Realtime，GTFS-RT）遥测数据。

与传统将车辆视为独立个体的公交数据集不同，本数据集经过深度工程化处理，纳入了队列空间矩阵（Platoon Spatial Matrix）。每一行数据均捕获了交互公交队列的物理运动学状态的同步5分钟快照。目标变量定义为运动学收敛度（Kinematic Convergence，Δ Gap）：即未来恰好15分钟时，连续公交车辆之间的空间间距的物理变化量。

本仓库提供了与研究中完全一致的Python框架，可用于验证预测该类行车间隔衰减是否需要深度历史序列记忆（长短期记忆网络（Long Short-Term Memory）、多头Transformer（Multi-Headed Transformers）），还是仅需公交队列的简单瞬时快照即可通过深度神经网络（Deep Neural Networks）、线性回归（Linear Regression）实现。

关键词（标签）：公交串车（Bus Bunching）；通用公交馈送规范实时版（GTFS-Realtime）；行车间隔预测（Headway Prediction）；深度学习（Deep Learning）；空间交互（Spatial Interaction）；公共交通（Public Transport）；公交遥测（Transit Telemetry）