Knowing daily traffic for the current year is recognized as being essential in many fields of transport analysis and practice, and short-term forecasting models offer a set of tools to meet these needs. This paper exa...Knowing daily traffic for the current year is recognized as being essential in many fields of transport analysis and practice, and short-term forecasting models offer a set of tools to meet these needs. This paper examines and compares the accuracy of three representative parametric and non-parametric prediction models, selected by the analysis of the numerous methods proposed in the literature for their good combi- nation of forecast accuracy and ease of calibration, using real-life data on Italian motorway stretches. Non-parametric K-NN regression model, Gaussian maximum likelihood model and double seasonality Holt-Winters exponential smoothing model confirm their goodness to predict the weekly and monthly fluctuations of average daily traffic with varying degrees of performance, while maintaining an easy use in professional practice, i.e. requiring ordinary professional skills and conventional analysis tools. Since combining several prediction models can give, on average, more accuracy than that of the individual models, the paper compares two weighting methods of easy implementation and susceptible to a direct use, namely the widely used information entropy method and the less widespread Shapley value method. Despite being less common than the information entropy method, the Shapley value method proves to be more capable in better combining single forecasts and produces improvements in the predictions for test data. With these remarks, the paper might be of interest to traffic technicians or analysts, in various and not uncommon tasks they might find in their work.展开更多
为探究公交站点之间的关联度并对公交客流进行更精准的实时预测,本文提出基于Attention的交通预测核心算法(Traffic Forecast Model Based Attention,TFMA),结合数据预处理和站点信息编码完成基于站点实时关联度的短时公交客流预测方法...为探究公交站点之间的关联度并对公交客流进行更精准的实时预测,本文提出基于Attention的交通预测核心算法(Traffic Forecast Model Based Attention,TFMA),结合数据预处理和站点信息编码完成基于站点实时关联度的短时公交客流预测方法。该方法首先创新性地提出了站点实时关联度,可实现对目标站点客流量更精准的预测;其次,在公交站点的编码信息中融入线路站点信息、客流变化率、天气、日期等关联因素;接着,该方法依靠Attention机制计算站点实时关联度;核心算法中使用multi-headed机制、增加通道和残差连接进一步提升预测能力;最后,以苏州市公交数据进行验证。结果显示:在准确率上,对比多元线性回归的53.8%、GRU(Gated Recurrent Unit)的66.9%和LightGBM(Light Gradient Boosting Machine)的81.2%,本文提出的基于站点实时关联度的短时公交客流预测方法的准确率在90%以上,表明该方法具备优秀的短时公交客流预测能力。展开更多
文摘Knowing daily traffic for the current year is recognized as being essential in many fields of transport analysis and practice, and short-term forecasting models offer a set of tools to meet these needs. This paper examines and compares the accuracy of three representative parametric and non-parametric prediction models, selected by the analysis of the numerous methods proposed in the literature for their good combi- nation of forecast accuracy and ease of calibration, using real-life data on Italian motorway stretches. Non-parametric K-NN regression model, Gaussian maximum likelihood model and double seasonality Holt-Winters exponential smoothing model confirm their goodness to predict the weekly and monthly fluctuations of average daily traffic with varying degrees of performance, while maintaining an easy use in professional practice, i.e. requiring ordinary professional skills and conventional analysis tools. Since combining several prediction models can give, on average, more accuracy than that of the individual models, the paper compares two weighting methods of easy implementation and susceptible to a direct use, namely the widely used information entropy method and the less widespread Shapley value method. Despite being less common than the information entropy method, the Shapley value method proves to be more capable in better combining single forecasts and produces improvements in the predictions for test data. With these remarks, the paper might be of interest to traffic technicians or analysts, in various and not uncommon tasks they might find in their work.
文摘为探究公交站点之间的关联度并对公交客流进行更精准的实时预测,本文提出基于Attention的交通预测核心算法(Traffic Forecast Model Based Attention,TFMA),结合数据预处理和站点信息编码完成基于站点实时关联度的短时公交客流预测方法。该方法首先创新性地提出了站点实时关联度,可实现对目标站点客流量更精准的预测;其次,在公交站点的编码信息中融入线路站点信息、客流变化率、天气、日期等关联因素;接着,该方法依靠Attention机制计算站点实时关联度;核心算法中使用multi-headed机制、增加通道和残差连接进一步提升预测能力;最后,以苏州市公交数据进行验证。结果显示:在准确率上,对比多元线性回归的53.8%、GRU(Gated Recurrent Unit)的66.9%和LightGBM(Light Gradient Boosting Machine)的81.2%,本文提出的基于站点实时关联度的短时公交客流预测方法的准确率在90%以上,表明该方法具备优秀的短时公交客流预测能力。
