Predicting tourism traffic demand accurately plays an important role in making effective policies for tourist administration. It helps to distribute the resources reasonably and avoid the tourism congestions. This pap...Predicting tourism traffic demand accurately plays an important role in making effective policies for tourist administration. It helps to distribute the resources reasonably and avoid the tourism congestions. This paper considered the noise interference and proposed a hybrid model, combining ensemble empirical mode decomposition (EEMD), deep belief network (DBN) and Google trends, for tourism traffic demand prediction. This model firstly applied dislocation weighted synthesis method to combine Google trends into a search composite index, and then it denoised the series with EEMD. EEMD extracted the high frequency noise from the original series. The low frequency series of search composite index would be used to forecast the low frequency tourism traffic series. Taking the inbound tourism in Shanghai as an example, this paper trained the model and predicted the next 12 months tourism arrivals. The conclusion demonstrated that the forecast error of EEMD-DBN model is lower remarkably than the baselines of ARIMA, GM(1,1), FTS, SVM, CES and DBN model. This revealed that nosing processing is necessary and EEMD-DBN forecast model can improve the prediction accuracy.展开更多
街景影像覆盖面广,能提供城市级别的交通场景信息,对开展交通研究分析提供了大规模数据源的支持和新的研究方法。为了探究街景影像在交通研究中的应用情况,从Web of Science核心集合数据库筛选了2011—2022年街景图像在交通研究中应用...街景影像覆盖面广,能提供城市级别的交通场景信息,对开展交通研究分析提供了大规模数据源的支持和新的研究方法。为了探究街景影像在交通研究中的应用情况,从Web of Science核心集合数据库筛选了2011—2022年街景图像在交通研究中应用相关的143篇论文,借助CiteSpace文献计量分析软件从年发文量、作者合作图谱、国家与机构合作图谱、关键词共现、关键词聚类和主题突发检测等方面进行归纳分析。在此基础上总结街景影像在交通基础设施、交通安全感知、出行辅助和出行环境感知四方面的应用研究进展,并对未来的研究方向提出展望。文献综述结果表明:(1)街景影像数据已被广泛应用于交通领域不同维度的研究,大多数研究通过卷积神经网络模型提取街景影像信息以反映交通场景特征;(2)由于街景数据采集时间跨度大,致使当前基于街景影像数据的交通方面应用主要集中在空间维度研究,缺乏动态时间维度的分析;(3)街景影像与交通领域知识数据进行融合分析建模是街景影像数据在交通领域应用的发展趋势。展开更多
文摘Predicting tourism traffic demand accurately plays an important role in making effective policies for tourist administration. It helps to distribute the resources reasonably and avoid the tourism congestions. This paper considered the noise interference and proposed a hybrid model, combining ensemble empirical mode decomposition (EEMD), deep belief network (DBN) and Google trends, for tourism traffic demand prediction. This model firstly applied dislocation weighted synthesis method to combine Google trends into a search composite index, and then it denoised the series with EEMD. EEMD extracted the high frequency noise from the original series. The low frequency series of search composite index would be used to forecast the low frequency tourism traffic series. Taking the inbound tourism in Shanghai as an example, this paper trained the model and predicted the next 12 months tourism arrivals. The conclusion demonstrated that the forecast error of EEMD-DBN model is lower remarkably than the baselines of ARIMA, GM(1,1), FTS, SVM, CES and DBN model. This revealed that nosing processing is necessary and EEMD-DBN forecast model can improve the prediction accuracy.
文摘街景影像覆盖面广,能提供城市级别的交通场景信息,对开展交通研究分析提供了大规模数据源的支持和新的研究方法。为了探究街景影像在交通研究中的应用情况,从Web of Science核心集合数据库筛选了2011—2022年街景图像在交通研究中应用相关的143篇论文,借助CiteSpace文献计量分析软件从年发文量、作者合作图谱、国家与机构合作图谱、关键词共现、关键词聚类和主题突发检测等方面进行归纳分析。在此基础上总结街景影像在交通基础设施、交通安全感知、出行辅助和出行环境感知四方面的应用研究进展,并对未来的研究方向提出展望。文献综述结果表明:(1)街景影像数据已被广泛应用于交通领域不同维度的研究,大多数研究通过卷积神经网络模型提取街景影像信息以反映交通场景特征;(2)由于街景数据采集时间跨度大,致使当前基于街景影像数据的交通方面应用主要集中在空间维度研究,缺乏动态时间维度的分析;(3)街景影像与交通领域知识数据进行融合分析建模是街景影像数据在交通领域应用的发展趋势。
