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
To accurately analyze the fluctuation range of time-varying differences in metro-to-bus transfer passenger flows,the application of a probabilistic interval prediction model is proposed to predict transfer passenger f...To accurately analyze the fluctuation range of time-varying differences in metro-to-bus transfer passenger flows,the application of a probabilistic interval prediction model is proposed to predict transfer passenger flows.First,bus and metro data are processed and matched by association to construct the basis for public transport trip chain extraction.Second,a reasonable matching threshold method to discriminate the transfer relationship is used to extract the public transport trip chain,and the basic characteristics of the trip based on the trip chain are analyzed to obtain the metro-to-bus transfer passenger flow.Third,to address the problem of low accuracy of point prediction,the DeepAR model is proposed to conduct interval prediction,where the input is the interchange passenger flow,the output is the predicted median and interval of passenger flow,and the prediction scenarios are weekday,non-workday,and weekday morning and evening peaks.Fourth,to reduce the prediction error,a combined particle swarm optimization(PSO)-DeepAR model is constructed using the PSO to optimize the DeepAR model.Finally,data from the Beijing Xizhimen subway station are used for validation,and results show that the PSO-DeepAR model has high prediction accuracy,with a 90%confidence interval coverage of up to 93.6%.展开更多
在我国“碳达峰”“碳中和”战略目标下,科学准确地监测和评估个体出行碳足迹是推动城市交通低碳转型的基础,但同时也面临着巨大的挑战。因此,借助出行即服务(Mobility as a Service,MaaS)平台的数据开放与共享,旨在提出MaaS环境下城市...在我国“碳达峰”“碳中和”战略目标下,科学准确地监测和评估个体出行碳足迹是推动城市交通低碳转型的基础,但同时也面临着巨大的挑战。因此,借助出行即服务(Mobility as a Service,MaaS)平台的数据开放与共享,旨在提出MaaS环境下城市个体出行链碳足迹监测与评估方法。首先,设计基于MaaS平台的城市交通碳源监测指标体系,实现对个体出行链多模式交通特征的提取与融合;然后,分别建立针对机动车和轨道交通出行的碳排放计算模型,计算不同交通方式的出行段碳排放,累加得到个体全链出行碳足迹;最后,以小汽车出行为基准线情景,评估个体出行链碳减排量。对在北京市采集的1865条出行链数据进行实例分析,结果显示:以小汽车、常规公交、轨道交通及非机动车为主导的出行链的平均人公里碳排放量分别为0.2380,0.0310,0.0390,0.0017 kg·pkm^(-1),相对基准线的平均人公里减碳量分别为0.029,0.220,0.230,0.280 kg·pkm^(-1);出行链人公里碳减排量与出行链中绿色出行比例正相关;对MaaS平台车辆电动化可使得减碳效益提高52.5%。展开更多
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.
基金The National Key Research and Development Program of China(No.2019YFB160-0200)the National Natural Science Foundation of China(No.71871011,71890972/71890970)。
文摘To accurately analyze the fluctuation range of time-varying differences in metro-to-bus transfer passenger flows,the application of a probabilistic interval prediction model is proposed to predict transfer passenger flows.First,bus and metro data are processed and matched by association to construct the basis for public transport trip chain extraction.Second,a reasonable matching threshold method to discriminate the transfer relationship is used to extract the public transport trip chain,and the basic characteristics of the trip based on the trip chain are analyzed to obtain the metro-to-bus transfer passenger flow.Third,to address the problem of low accuracy of point prediction,the DeepAR model is proposed to conduct interval prediction,where the input is the interchange passenger flow,the output is the predicted median and interval of passenger flow,and the prediction scenarios are weekday,non-workday,and weekday morning and evening peaks.Fourth,to reduce the prediction error,a combined particle swarm optimization(PSO)-DeepAR model is constructed using the PSO to optimize the DeepAR model.Finally,data from the Beijing Xizhimen subway station are used for validation,and results show that the PSO-DeepAR model has high prediction accuracy,with a 90%confidence interval coverage of up to 93.6%.
文摘在我国“碳达峰”“碳中和”战略目标下,科学准确地监测和评估个体出行碳足迹是推动城市交通低碳转型的基础,但同时也面临着巨大的挑战。因此,借助出行即服务(Mobility as a Service,MaaS)平台的数据开放与共享,旨在提出MaaS环境下城市个体出行链碳足迹监测与评估方法。首先,设计基于MaaS平台的城市交通碳源监测指标体系,实现对个体出行链多模式交通特征的提取与融合;然后,分别建立针对机动车和轨道交通出行的碳排放计算模型,计算不同交通方式的出行段碳排放,累加得到个体全链出行碳足迹;最后,以小汽车出行为基准线情景,评估个体出行链碳减排量。对在北京市采集的1865条出行链数据进行实例分析,结果显示:以小汽车、常规公交、轨道交通及非机动车为主导的出行链的平均人公里碳排放量分别为0.2380,0.0310,0.0390,0.0017 kg·pkm^(-1),相对基准线的平均人公里减碳量分别为0.029,0.220,0.230,0.280 kg·pkm^(-1);出行链人公里碳减排量与出行链中绿色出行比例正相关;对MaaS平台车辆电动化可使得减碳效益提高52.5%。
