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
In the early nineties of the last century, the transportation system in Gaza Strip was born and new infrastructure projects, especially road networks, were constructed. However, the construction lacked efficient appli...In the early nineties of the last century, the transportation system in Gaza Strip was born and new infrastructure projects, especially road networks, were constructed. However, the construction lacked efficient application of a transportation planning process. Transportation planning relies on traffic demand forecasting process. The conventional process is impeded by extensive amount of socioeconomic data. One of the most widely-used models which mitigate this problem is the TransCAD Model. This model is rarely used in Gaza Strip for traffic demand forecasting, and most of the practices depend mainly on a constant growth rate of traffic. Therefore, the main objective of this research is to apply this model in Gaza City for traffic estimation. This model estimates the origin-destination matrix based on traffic count. The traffic count was carried out at 36 intersections distributed around Gaza City. The results of traffic flow estimation obtained from TransCAD are assigned to the Gaza maps using the GIS techniques for spatial analysis. It is shown that the most congested area at present is the middle of the city especially at Aljala-Omer Almokhtar intersection. Therefore, improvement scenarios of this area should be carried out. The results of calibration of traffic flow estimation show that the differences between the estimated and the actual flows were less than 10%. In addition, network evaluation results show that the network is expected to be more congested in 2015. This work can be used by transportation planners for testing any network improvement scenarios and for studying their network performance.展开更多
基于宁波市公共自行车刷卡数据、POI(Point of Interest)数据、气象和空气质量等数据,从数据驱动视角,深入挖掘公共自行车使用的时空特征及站点租还车需求预测。在时间上,采用KMeans算法,将站点聚为5类,探讨各类站点的时变需求规律及影...基于宁波市公共自行车刷卡数据、POI(Point of Interest)数据、气象和空气质量等数据,从数据驱动视角,深入挖掘公共自行车使用的时空特征及站点租还车需求预测。在时间上,采用KMeans算法,将站点聚为5类,探讨各类站点的时变需求规律及影响因素;在空间上,提出基于POI数据的站点用地类型识别方法,将站点分为居住类、交通设施类、办公类和商业休闲类。构建以15,30,60 min为间隔,以租还车需求为目标变量的随机森林预测模型,并与常用的BP(Back Propagation)神经网络、K最近邻方法进行比较。结果表明,随机森林模型的精度更高,适用性更强。以30 min为间隔的站点租还车需求预测精度最高,考虑站点土地利用类型后能有效提高模型的预测精度。本文结果可作为未来站点平衡调度的依据并推广应用于共享单车系统,为改善服务水平提供技术和理论支撑。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In the early nineties of the last century, the transportation system in Gaza Strip was born and new infrastructure projects, especially road networks, were constructed. However, the construction lacked efficient application of a transportation planning process. Transportation planning relies on traffic demand forecasting process. The conventional process is impeded by extensive amount of socioeconomic data. One of the most widely-used models which mitigate this problem is the TransCAD Model. This model is rarely used in Gaza Strip for traffic demand forecasting, and most of the practices depend mainly on a constant growth rate of traffic. Therefore, the main objective of this research is to apply this model in Gaza City for traffic estimation. This model estimates the origin-destination matrix based on traffic count. The traffic count was carried out at 36 intersections distributed around Gaza City. The results of traffic flow estimation obtained from TransCAD are assigned to the Gaza maps using the GIS techniques for spatial analysis. It is shown that the most congested area at present is the middle of the city especially at Aljala-Omer Almokhtar intersection. Therefore, improvement scenarios of this area should be carried out. The results of calibration of traffic flow estimation show that the differences between the estimated and the actual flows were less than 10%. In addition, network evaluation results show that the network is expected to be more congested in 2015. This work can be used by transportation planners for testing any network improvement scenarios and for studying their network performance.
文摘针对高密度固定站点式共享自行车系统启停点分布复杂、区域内停放量变化难以监控、区域间流动特征复杂的问题,提出一种聚类算法和时序预测模型组合的需求预测模型。首先,使用基于平衡迭代降维的层次聚类算法(Balanced Iterative Reducing and Clustering using Hierarchies,BIRCH)对共享自行车的启停点进行聚类分析,完成虚拟站点构造和区域划分;其次,对虚拟站点的行程数据进行集计,获得的站点净流入量和站点间流量序列作为输入,使用三次指数平滑法(Triple Exponential Smoothing,TES)进行需求预测;最后,选取纽约和旧金山湾区数据集进行对比和验证。对比结果表明,需求预测模型可有效减少预测单位数量,并准确预测共享自行车在不同区域的供需平衡状态和区域间流动状态。验证结果表明,在2种数据集上,BIRCH算法的聚类质量和耗时均优于K均值算法、层次聚类(离差平方和最小化原则)算法、基于密度带噪声应用的空间聚类和高斯混合模型算法;使用TES模型时预测误差基本小于历史平均模型和自回归移动平均模型。
