**Context-Aware Link Embedding with Reachability and Flow Centrality Analysis for Accurate Speed Prediction for Large-Scale Traffic Networks**

### **Chanjae Lee 1 and Young Yoon 2,3,\***


Received: 26 September 2020; Accepted: 28 October 2020; Published: 29 October 2020

**Abstract:** This paper presents a novel method for predicting the traffic speed of the links on large-scale traffic networks. We first analyze how traffic flows in and out of every link through the lowest cost reachable paths. We aggregate the traffic flow conditions of the links on every hop of the inbound and outbound reachable paths to represent the traffic flow dynamics. We compute a new measure called traffic flow centrality (i.e., the *Z* value) for every link to capture the inherently complex mechanism of the traffic links influencing each other in terms of traffic speed. We combine the features regarding the traffic flow centrality with the external conditions around the links, such as climate and time of day information. We model how these features change over time with recurrent neural networks and infer traffic speed at the subsequent time windows. Our feature representation of the traffic flow for every link remains invariant even when the traffic network changes. Furthermore, we can handle traffic networks with thousands of links. The experiments with the traffic networks in the Seoul metropolitan area in South Korea reveal that our unique ways of embedding the comprehensive spatio-temporal features of links outperform existing solutions.

**Keywords:** link embedding; traffic speed prediction; traffic flow centrality; reachability analysis; spatio-temporal data; artificial neural network; deep learning; context-awareness
