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首页> 外文期刊>International journal of synthetic emotions >Long-Term and Short-Term Traffic Forecasting Using Holt-Winters Method: A Comparability Approach with Comparable Data in Multiple Seasons
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Long-Term and Short-Term Traffic Forecasting Using Holt-Winters Method: A Comparability Approach with Comparable Data in Multiple Seasons

机译：Holt-Winters方法进行的长期和短期交通量预测：具有多个季节可比数据的可比性方法

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摘要

The need of faster life has caused the exponential growth in No. of vehicles on streets. The adverse effects include frequent traffic congestion, less time efficiency, unnecessary fuel consumption, pollution, accidents, etc. One of most important solution for resolving these problems is efficient transportation management system. Data science introduces different techniques and tools for overcoming these problems and to improve the data quality and forecasting inferences. The proposed long-term forecasting model can predict numerical values of effective attributes for a particular day on half-hourly basis, at least 24 hours prior to the time of prediction. The proposed forecasting model for short-term analysis will be having access to data as close as 30-minute difference from the time of prediction. Our proposed solution has integrated use of Holt-Winters (HW) method along with comparability schemes for seasonal approach.
机译：对更快生活的需求已导致街头车辆数量呈指数增长。不利影响包括频繁的交通拥堵，时间效率降低，不必要的燃料消耗，污染，事故等。解决这些问题的最重要解决方案之一是高效的运输管理系统。数据科学引入了各种技术和工具来克服这些问题，并提高数据质量和预测推断。所提出的长期预测模型可以在预测时间之前至少24小时每半小时预测一天中有效属性的数值。所提出的用于短期分析的预测模型将可以访问与预测时间相差30分钟的数据。我们提出的解决方案集成了Holt-Winters（HW）方法以及季节性方法的可比性方案。

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