Forecasting of traffic flows at ferry terminals. A hybrid model

Iñigo L. Ansorena

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Forecasting of traffic flows at ferry terminals. A hybrid model

机译：轮渡码头的交通流量预测。混合模型

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This paper presents an agile approach for managing ferry terminals. We propose a double-stage model that forecasts traffic. The hybrid model is based on quantitative tools. First, we search for associations in data and then, we develop a time series analysis. The specific objectives of the study are firstly to survey and collect data at a disaggregated level in terms of passenger and vehicle flows and time periods; and secondly, to develop forecasts on short-term using a combination of the proposed techniques. We think that the simultaneous application of historical analogy and time series forecasting is a powerful tool to predict the future traffic. Additionally, the methodological procedure proposed in this paper is general enough to be applied to other traffics and ports.

机译：本文提出了一种管理渡轮码头的敏捷方法。我们提出了一种预测流量的双阶段模型。混合模型基于定量工具。首先，我们搜索数据中的关联，然后开发时间序列分析。该研究的具体目标是：首先按照旅客和车辆的流量和时间段，按分类水平调查和收集数据；其次，结合所提出的技术来发展短期预测。我们认为，历史类比和时间序列预测的同时应用是预测未来流量的强大工具。此外，本文提出的方法程序具有足够的通用性，可以应用于其他流量和端口。

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《International journal of agile systems and management》 |2019年第1期|27-43|共17页
作者
Iñigo L. Ansorena;
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作者单位

ETS Ingenieros de Caminos, Canales y Puertos, Technical University of Madrid;

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正文语种 eng
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关键词
forecast; time series; association discovery; ferry terminal; port management; hybrid model; traffic flows;

机译：预报;时间序列;关联发现;轮渡码头;端口管理;混合模型;交通流量;

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2. Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling [J] . Moretti Fabio, Pizzuti Stefano, Panzieri Stefano, Neurocomputing . 2015,第nova1期

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3. Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm [J] . Chan K. Y. Intelligent Transportation Systems, IEEE Transactions on . 2012,第2期

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4. Urban Traffic Flow Forecasting Using Neural-Statistic Hybrid Modeling [C] . M. Annunziato, F. Moretti, S. Pizzuti International Conference on Soft Computing Models in Industrial Applications . 2013

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5. Modeling and forecasting vehicular traffic flow as a seasonal stochastic time series process. [D] . Williams, Billy M. 1999

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6. Hybrid Dynamic Traffic Model for Freeway Flow Analysis Using a Switched Reduced-Order Unknown-Input State Observer [O] . Yuqi Guo, Bin Li, Matthew Daniel Christie, 2020

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7. Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm [O] . Kit Yan Chan, Tharam S. Dillon, Jaipal Singh, 2012

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8. Modeling and Forecasting Vehicular Traffic Flow as a Seasonal Stochastic Time211 Series Process [R] . Williams, B. M., Hoel, L. A. 1999

机译：车辆交通流建模与预测作为季节性随机时间211系列过程

Forecasting of traffic flows at ferry terminals. A hybrid model

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