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
This paper discusses the disaggregation of the Federal Highway Administration’s Freight Analysis Framework(FAF)database(version 3.0)on freight origin-destination data and the development of linear regression equation...This paper discusses the disaggregation of the Federal Highway Administration’s Freight Analysis Framework(FAF)database(version 3.0)on freight origin-destination data and the development of linear regression equations to describe the relationships between commodity-based freight trip productions/attractions to specific economic variables.Instead of generating a production/attraction equation for each commodity,commodities are grouped in certain ways to simplify model development and application.We consider three grouping methods and two model selection criteria(with and without intercepts),which are compared in terms of goodness of fit with two data sets(FAF versions 2.0 and 3.0).Furthermore,the freight generation models are validated using county-level economic data in California and applied to predict year 2015 commodity outputs.The results of this study can help city,county,metropolitan and state level planning agencies develop their own customized freight demand generation models without performing costly large-scale surveys.展开更多
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.
文摘This paper discusses the disaggregation of the Federal Highway Administration’s Freight Analysis Framework(FAF)database(version 3.0)on freight origin-destination data and the development of linear regression equations to describe the relationships between commodity-based freight trip productions/attractions to specific economic variables.Instead of generating a production/attraction equation for each commodity,commodities are grouped in certain ways to simplify model development and application.We consider three grouping methods and two model selection criteria(with and without intercepts),which are compared in terms of goodness of fit with two data sets(FAF versions 2.0 and 3.0).Furthermore,the freight generation models are validated using county-level economic data in California and applied to predict year 2015 commodity outputs.The results of this study can help city,county,metropolitan and state level planning agencies develop their own customized freight demand generation models without performing costly large-scale surveys.
