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.
Highlights
Accurate prediction of speed on traffic networks helps improve traffic management strategies and generate efficient routing plans
We model the correlation between the input feature and each link speed with a Long Short-Term Memory (LSTM) algorithm
We evaluate our approach using the data from TOPIS (Seoul Traffic Operation and Information service) https://topis.seoul.go.kr/, which contain hourly average speed information for the 4670 major traffic links in the Seoul metropolitan area
Summary
Accurate prediction of speed on traffic networks helps improve traffic management strategies and generate efficient routing plans. Precisely estimating the traffic speed in advance has been a non-trivial task since various factors determine traffic flows in many ways. Various approaches have been used for traffic speed prediction using statistical methods [1,2,3,4,5,6,7]. The existing solutions are mostly limited to estimating traffic speed for either a single road link or a small-scale sub-network with only a handful of traffic links (e.g., a crossroad). A road system is a large-scale connected graph with the traffic links affecting each other’s traffic flow over time in a more complicated fashion that cannot be explained with a simple statistical model. The works without a broader view of the traffic links may not adequately unravel the hidden, but critical speed prediction factors
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