An Improvement of the Binary Logit Model for Trip Generation Forecasting
This paper developed a hypothesis to overcome the disadvantage of the binary Iogit model used in resident trip generation. It proposed that random parameters β obeyed some distribution firstly. Combined the characters of trip generation and observable variables, it then put forward that β followed log normal distribution. The hypothesis was validated by demarcating the parameters in SAS software and forecasting trip generation volumes with two different models, both of which were based on PUMS investigation data in USA. The former result shows that all the parameters T values are out of -1,1 interval and proves the assumption is correct. The latter one shows that the improved models forecasting precision is much higher than the previous ones, and reveals that the application of the improved binary logit model in resident trip generation has notable effects.
Binary logit Log normal distribution SAS parameter estimation
Chunyan Li Jun Chen
College of transportation Southeast University Nanjing, China
国际会议
张家界
英文
462-465
2009-04-11（万方平台首次上网日期，不代表论文的发表时间）