The conventional traffic demand forecasting methods based on revealed preference （RP） data are not able to predict the modal split. Passengers＇ stated intentions are indispensable for modal split forecasting and ev...The conventional traffic demand forecasting methods based on revealed preference （RP） data are not able to predict the modal split. Passengers＇ stated intentions are indispensable for modal split forecasting and evaluation of new traffic modes. This paper analyzed the biases and errors included in stated preference data, put forward the new stochastic utility functions, and proposed an unbiased disaggregate model and its approximate model based on the combination of RP and stated preference （SP） data, with analysis of the parameter estimation algorithm. The model was also used to forecast rail transit passenger volumes to the Beijing Capital International Airport and the shift ratios from current traffic modes to rail transit. Experimental results show that the model can greatly increase forecasting accuracy of the modal split ratio of current traffic modes and can accurately forecast the shift ratios from current modes to the new mode.展开更多
基于时空棱柱方法,将人的活动区域限制在一定的时空棱柱体内,根据出行者的活动计划,确定在一定时间限制下活动场所在空间上的可达性。提出了目的地选择行为分层的建模策略。引入小区吸引力因子,构建了非线性效用函数的目的地选择行为模...基于时空棱柱方法,将人的活动区域限制在一定的时空棱柱体内,根据出行者的活动计划,确定在一定时间限制下活动场所在空间上的可达性。提出了目的地选择行为分层的建模策略。引入小区吸引力因子,构建了非线性效用函数的目的地选择行为模型,探讨了小区吸引力因子的量化过程、参数标定及结果检验。结果表明:结合出行的时空约束和地理信息系统(Geographic information system,GIS)分析能够确定出行者的目的地选择集合。具有小区吸引力因子的非线性效用函数能够更准确地预测目的地选择行为。本文研究将出行理论与出行者的实际行为相结合,为目的地选择行为的建模提供了一种新思路。展开更多
The authors argue that travel forecasting models should be dynamic and disaggregate in their representation of demand, supply, and supply-demand interactions, and propose a framework for such models. The proposed fram...The authors argue that travel forecasting models should be dynamic and disaggregate in their representation of demand, supply, and supply-demand interactions, and propose a framework for such models. The proposed framework consists of disaggregate activity-based representation of travel choices of individual motorists on the demand side integrated with disaggregate dynamic modeling of network performance, through vehicle-based traffic simulation models on the supply side. The demand model generates individual members of the population and assigns to them socioeconomic characteristics. The generated motorists maintain these characteristics when they are loaded on the network by the supply model. In an equilibrium setting, the framework lends itself to a fixed-point formulation to represent and resolve demand-supply interactions. The paper discusses some of the remaining development challenges and presents an example of an existing travel forecasting model system that incorporates many of the proposed elements.展开更多
Traditional trip generation forecasting methods use unified average trip generation rates to determine trip generation volumes in various traffic zones without considering the individual characteristics of each traffi...Traditional trip generation forecasting methods use unified average trip generation rates to determine trip generation volumes in various traffic zones without considering the individual characteristics of each traffic zone. Therefore, the results can have significant errors. To reduce the forecasting error produced by uniform trip generation rates for different traffic zones, the behavior of each traveler was studied instead of the characteristics of the traffic zone. This paper gives a method for calculating the trip efficiency and the effect of traffic zones combined with a destination selection model based on disaggregate theory for trip generation. Beijing data is used with the trip generation method to predict trip volumes. The results show that the disaggregate model in this paper is more accurate than the traditional method. An analysis of the factors influencing traveler behavior and destination selection shows that the attractiveness of the traffic zone strongly affects the trip generation volume.展开更多
Referring to the 1 248 survey data of rural population in 14 provinces of China, the influencing factors of trip time choice were analyzed. Based on the basic theory of disaggregate model and its modelling method, nin...Referring to the 1 248 survey data of rural population in 14 provinces of China, the influencing factors of trip time choice were analyzed. Based on the basic theory of disaggregate model and its modelling method, nine grades were selected as the alternatives of trip time, the variables affecting time choice and the method getting their values were determined, and a multinomial logit (MNL) model was developed. Another 1 200 trip data of rural population were selected to testify the model's validity. The result shows that the maximum absolute error of each period between calculated value and statistic is 3.6%, so MNL model has high calculation accuracy.展开更多
以硫磺素T为母体,合成和表征了一种苯并噻唑类双功能荧光螯合剂ZY5,并考察了它与金属-Aβ（amyloid-β）聚集体的相互作用。结果表明：ZY5的荧光强度在p H 4.0~10.0的范围内基本稳定,基本不受时间和光照的影响。ZY5符合严格的Lipinski...以硫磺素T为母体,合成和表征了一种苯并噻唑类双功能荧光螯合剂ZY5,并考察了它与金属-Aβ（amyloid-β）聚集体的相互作用。结果表明：ZY5的荧光强度在p H 4.0~10.0的范围内基本稳定,基本不受时间和光照的影响。ZY5符合严格的Lipinski类药标准,能识别和解聚金属-Aβ42聚集体,在一定程度上可抑制由Cu-Aβ42聚集体产生的毒性。展开更多
文摘The conventional traffic demand forecasting methods based on revealed preference （RP） data are not able to predict the modal split. Passengers＇ stated intentions are indispensable for modal split forecasting and evaluation of new traffic modes. This paper analyzed the biases and errors included in stated preference data, put forward the new stochastic utility functions, and proposed an unbiased disaggregate model and its approximate model based on the combination of RP and stated preference （SP） data, with analysis of the parameter estimation algorithm. The model was also used to forecast rail transit passenger volumes to the Beijing Capital International Airport and the shift ratios from current traffic modes to rail transit. Experimental results show that the model can greatly increase forecasting accuracy of the modal split ratio of current traffic modes and can accurately forecast the shift ratios from current modes to the new mode.
文摘基于时空棱柱方法,将人的活动区域限制在一定的时空棱柱体内,根据出行者的活动计划,确定在一定时间限制下活动场所在空间上的可达性。提出了目的地选择行为分层的建模策略。引入小区吸引力因子,构建了非线性效用函数的目的地选择行为模型,探讨了小区吸引力因子的量化过程、参数标定及结果检验。结果表明:结合出行的时空约束和地理信息系统(Geographic information system,GIS)分析能够确定出行者的目的地选择集合。具有小区吸引力因子的非线性效用函数能够更准确地预测目的地选择行为。本文研究将出行理论与出行者的实际行为相结合,为目的地选择行为的建模提供了一种新思路。
文摘The authors argue that travel forecasting models should be dynamic and disaggregate in their representation of demand, supply, and supply-demand interactions, and propose a framework for such models. The proposed framework consists of disaggregate activity-based representation of travel choices of individual motorists on the demand side integrated with disaggregate dynamic modeling of network performance, through vehicle-based traffic simulation models on the supply side. The demand model generates individual members of the population and assigns to them socioeconomic characteristics. The generated motorists maintain these characteristics when they are loaded on the network by the supply model. In an equilibrium setting, the framework lends itself to a fixed-point formulation to represent and resolve demand-supply interactions. The paper discusses some of the remaining development challenges and presents an example of an existing travel forecasting model system that incorporates many of the proposed elements.
基金the National Natural Science Foundation of China (No. 50478041)the Natural Science Foundation of Beijing (No. 8053019)
文摘Traditional trip generation forecasting methods use unified average trip generation rates to determine trip generation volumes in various traffic zones without considering the individual characteristics of each traffic zone. Therefore, the results can have significant errors. To reduce the forecasting error produced by uniform trip generation rates for different traffic zones, the behavior of each traveler was studied instead of the characteristics of the traffic zone. This paper gives a method for calculating the trip efficiency and the effect of traffic zones combined with a destination selection model based on disaggregate theory for trip generation. Beijing data is used with the trip generation method to predict trip volumes. The results show that the disaggregate model in this paper is more accurate than the traditional method. An analysis of the factors influencing traveler behavior and destination selection shows that the attractiveness of the traffic zone strongly affects the trip generation volume.
基金Project(51178158) supported by the National Natural Science Foundation of ChinaProjects(2010HGZY0010, 2011HGBZ0936) supported by the Fundamental Research Funds for the Central Universities of China
文摘Referring to the 1 248 survey data of rural population in 14 provinces of China, the influencing factors of trip time choice were analyzed. Based on the basic theory of disaggregate model and its modelling method, nine grades were selected as the alternatives of trip time, the variables affecting time choice and the method getting their values were determined, and a multinomial logit (MNL) model was developed. Another 1 200 trip data of rural population were selected to testify the model's validity. The result shows that the maximum absolute error of each period between calculated value and statistic is 3.6%, so MNL model has high calculation accuracy.
文摘以硫磺素T为母体,合成和表征了一种苯并噻唑类双功能荧光螯合剂ZY5,并考察了它与金属-Aβ（amyloid-β）聚集体的相互作用。结果表明：ZY5的荧光强度在p H 4.0~10.0的范围内基本稳定,基本不受时间和光照的影响。ZY5符合严格的Lipinski类药标准,能识别和解聚金属-Aβ42聚集体,在一定程度上可抑制由Cu-Aβ42聚集体产生的毒性。
