Open AccessHybrid short‐term prediction of traffic volume at ferry terminal based on data fusion
Author(s) -
Zhang Weibin,
Tang Jinjun,
Kristian Henrickson,
Zou Yajie,
Wang Yinhai
Publication year - 2016
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2015.0212
Subject(s) - traffic flow (computer networking) , traffic volume , traffic congestion reconstruction with kerner's three phase theory , term (time) , traffic generation model , floating car data , transport engineering , computer science , volume (thermodynamics) , real time computing , engineering , traffic congestion , computer network , physics , quantum mechanics
Ferry traffic prediction is an important consideration for urban transportation authorities. A novel methodology which considers cyclical patterns in ferry on‐board vehicle traffic and improves on this method by incorporating traffic data from nearby freeway loop detectors is proposed. Since vehicles arriving in ferry terminals often originate from nearby freeways, the study of freeway traffic flow can improve ferry traffic prediction, especially during peak travel periods. In this study, the authors first identify periodic patterns in ferry traffic through analysis in both the time and frequency domain, and propose a modelling methodology which considers these patterns in short‐term traffic prediction. In the proposed model, the ferry traffic volume is divided into periodic and dynamic components, with the periodic component approximated by trigonometric functions. Second, this model is expanded to incorporate measured traffic data on nearby freeways. Specifically, the correlation between freeway traffic and ferry traffic volume is studied, and an enhanced model is proposed which integrates the predictions obtained through historical ferry data and from freeway traffic during peak travel time periods. Validation results demonstrate that consideration of periodic patterns increases prediction accuracy and that the proposed model further improves the ferry traffic prediction accuracy during peak time periods.