Trajectory Length Prediction for Intelligent Traffic Signaling: A Data-Driven Approach

Shaojun Gan, Shan Liang, Kang Li, Jing Deng, Tingli Cheng

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Ship trajectory length prediction is vital for intelligent traffic signaling in the controlled waterways of the Yangtze River. In current intelligent traffic signaling systems (ITSSs), ships are supposed to travel exactly along the central line of the Yangtze River, which is often not a valid assumption and has caused a number of problems. Over the past few years, traffic data have been accumulated exponentially, leading to the big data era. This trend allows more accurate prediction of ships' travel trajectory length based on historical data. In this paper, ships' historical trajectories are first grouped by using the fuzzy c-means clustering algorithm. The relationship between some known factors (i.e., ship speed, loading capacity, self-weight, maximum power, ship length, ship width, ship type, and water level) and the resultant memberships are then modeled using artificial neural networks. The trajectory length is then estimated by the sum of the predicted probabilities multiplied by the trajectory cluster centers' length. To the best of our knowledge, this is the first time to predict the overall trajectory length of manually controlled ships. The experimental results show that the proposed method can reduce the probability of generating incorrect traffic control signals by 74.68% over existing ITSSs. This will significantly improve the efficiency of the Yangtze River traffic management system and increase the traffic capacity by reducing the traveling time.
Original languageEnglish
Pages (from-to)1-10
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number99
Early online date02 Jun 2017
DOIs
Publication statusEarly online date - 02 Jun 2017

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