日前,信息技术研究和顾问公司Gartner公布最新预测,2020年全球IT支出将达到3.9万亿美元,比2019年增长3.4%。预计2021年全球IT支出将突破4万亿美元大关。Gartner研究副总裁John David Lovelock表示:“虽然政治不确定性因素一度将全球经...日前,信息技术研究和顾问公司Gartner公布最新预测,2020年全球IT支出将达到3.9万亿美元,比2019年增长3.4%。预计2021年全球IT支出将突破4万亿美元大关。Gartner研究副总裁John David Lovelock表示:“虽然政治不确定性因素一度将全球经济逼近衰退边缘,但在2019年最终没有发生这一结果,并且在2020年及以后也不太可能会发生这种情况。随着全球不确定性因素的减少,企业预计自身的收入将会增加,因此它们正在加倍对IT进行投资。同时,企业的支出模式仍在不断变化。”展开更多
In order to improve the accuracy of travel demand forecast and considering the distribution of travel behaviors within time dimension, a trip chaining pattern recognition model was established based on activity purpos...In order to improve the accuracy of travel demand forecast and considering the distribution of travel behaviors within time dimension, a trip chaining pattern recognition model was established based on activity purposes by applying three methods： the support vector machine （SVM） model, the radial basis function neural network （RBFNN） model and the multinomial logit （MNL） model. The effect of explanatory factors on trip chaining behaviors and their contribution to model performace were investigated by sensitivity analysis. Results show that the SVM model has a better performance than the RBFNN model and the MNL model due to its higher overall and partial accuracy, indicating its recognition advantage under a smai sample size scenario. It is also proved that the SVM model is capable of estimating the effect of multi-category factors on trip chaining behaviors more accurately. The different contribution of explanatory, factors to trip chaining pattern recognition reflects the importance of refining trip chaining patterns ad exploring factors that are specific to each pattern. It is shown that the SVM technology in travel demand forecast modeling and analysis of explanatory variable effects is practical.展开更多
文摘日前,信息技术研究和顾问公司Gartner公布最新预测,2020年全球IT支出将达到3.9万亿美元,比2019年增长3.4%。预计2021年全球IT支出将突破4万亿美元大关。Gartner研究副总裁John David Lovelock表示:“虽然政治不确定性因素一度将全球经济逼近衰退边缘,但在2019年最终没有发生这一结果,并且在2020年及以后也不太可能会发生这种情况。随着全球不确定性因素的减少,企业预计自身的收入将会增加,因此它们正在加倍对IT进行投资。同时,企业的支出模式仍在不断变化。”
基金The Fundamental Research Funds for the Central Universities,the Scientific Innovation Research of College Graduates in Jiangsu Province(No.KYLX_0177)
文摘In order to improve the accuracy of travel demand forecast and considering the distribution of travel behaviors within time dimension, a trip chaining pattern recognition model was established based on activity purposes by applying three methods： the support vector machine （SVM） model, the radial basis function neural network （RBFNN） model and the multinomial logit （MNL） model. The effect of explanatory factors on trip chaining behaviors and their contribution to model performace were investigated by sensitivity analysis. Results show that the SVM model has a better performance than the RBFNN model and the MNL model due to its higher overall and partial accuracy, indicating its recognition advantage under a smai sample size scenario. It is also proved that the SVM model is capable of estimating the effect of multi-category factors on trip chaining behaviors more accurately. The different contribution of explanatory, factors to trip chaining pattern recognition reflects the importance of refining trip chaining patterns ad exploring factors that are specific to each pattern. It is shown that the SVM technology in travel demand forecast modeling and analysis of explanatory variable effects is practical.
