Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Models

TL;DR

This paper introduces a novel deep learning framework combining capsule networks and nested LSTM models for accurate traffic speed forecasting in complex transportation networks, outperforming existing methods.

Contribution

It is the first to integrate CapsNet and NLSTM for traffic forecasting, capturing complex spatiotemporal dependencies more effectively.

Findings

Outperforms multiple state-of-the-art models on Beijing network data.

Demonstrates the effectiveness of CapsNet and NLSTM through visualization and quantitative analysis.

Shows superior ability to model complicated traffic patterns.

Abstract

Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies of roadway links and the dynamic temporal patterns of traffic states make it particularly challenging. To address these challenges, we propose a new capsule network (CapsNet) to extract the spatial features of traffic networks and utilize a nested LSTM (NLSTM) structure to capture the hierarchical temporal dependencies in traffic sequence data. A framework for network-level traffic forecasting is also proposed by sequentially connecting CapsNet and NLSTM. On the basis of literature review, our study is the first to adopt CapsNet and NLSTM in the field of traffic forecasting. An experiment on a Beijing transportation network with 278 links shows that the proposed framework with the capability of capturing complicated spatiotemporal traffic patterns outperforms multiple state-of-the-art traffic forecasting baseline models. The superiority and feasibility of CapsNet and NLSTM are also demonstrated, respectively, by visualizing and quantitatively evaluating the experimental results.