Less is More: Non-uniform Road Segments are Efficient for Bus Arrival Prediction

TL;DR

This paper introduces a reinforcement learning approach to adaptively segment roads into non-uniform parts for improved bus arrival time prediction, outperforming traditional uniform segmentation methods in efficiency and accuracy.

Contribution

The paper presents a novel RL-based method for learning non-uniform road segments, enhancing prediction accuracy and computational efficiency over uniform segmentation strategies.

Findings

RL-based segmentation improves prediction accuracy

Non-uniform segments outperform uniform segments

Method is computationally efficient and scalable

Abstract

In bus arrival time prediction, the process of organizing road infrastructure network data into homogeneous entities is known as segmentation. Segmenting a road network is widely recognized as the first and most critical step in developing an arrival time prediction system, particularly for auto-regressive-based approaches. Traditional methods typically employ a uniform segmentation strategy, which fails to account for varying physical constraints along roads, such as road conditions, intersections, and points of interest, thereby limiting prediction efficiency. In this paper, we propose a Reinforcement Learning (RL)-based approach to efficiently and adaptively learn non-uniform road segments for arrival time prediction. Our method decouples the prediction process into two stages: 1) Non-uniform road segments are extracted based on their impact scores using the proposed RL framework; and 2) A linear prediction model is applied to the selected segments to make predictions. This method ensures optimal segment selection while maintaining computational efficiency, offering a significant improvement over traditional uniform approaches. Furthermore, our experimental results suggest that the linear approach can even achieve better performance than more complex methods. Extensive experiments demonstrate the superiority of the proposed method, which not only enhances efficiency but also improves learning performance on large-scale benchmarks. The dataset and the code are publicly accessible at: https://github.com/pangjunbiao/Less-is-More.