Estimating time of arrival of vehicle fleets with GCN based traffic prediction

TL;DR

This paper introduces a GCN-based framework for estimating vehicle arrival times in transit systems with sparse data, combining road segmentation and traffic delay modeling to improve accuracy.

Contribution

It proposes a constrained road segmentation method and an augmented T-GCN model that considers complex road network influences for better traffic delay prediction.

Findings

Enhanced accuracy in vehicle arrival time estimation.

Effective handling of sparse position data.

Improved traffic delay predictions through network augmentation.

Abstract

This paper presents an effective framework for estimating time of arrival of vehicles (buses) in an Intelligent Transit Management System (ITMS) having sparse position updates. Our contributions towards this is firstly in implementing a constrained optimization based road linestring segmenting framework ensuring ideal segment lengths and segments with sufficient density of vehicle position measurements which will result in valid statistics for scenarios involving sparse position measurements. Over this we propose a comprehensive approach for predicting traffic delays and estimated time of vehicle arrival addressing both the spatial and temporal dependencies of traffic. The traffic delay model is built on top of the T-GCN architecture on which we optimally augment an adjacency matrix which models a complexly connected road network considering the degree of influence between road segments, enabling the traffic delay model to look beyond physical road connectivity in predicting traffic delays and therefore producing better estimates of arrival times to points along the designated route of the vehicles.