旅次發生之推估在程序性運輸需求分析中是一相當重要的步驟。過去之研究均以社會經濟因素推估旅次發生，然而，大部份都是以一地區或一個家戶之社會經濟變數總體值為解釋變數，甚少考慮、社會經濟變數總體值之「組成結構」變化對旅次發生之影響。例如:人口年齡結構之老年化、女性就業人口之增加、都市三級產業之增加等均與過去有所不同，故若僅考慮人口數、就業人口數，而不考慮人口組織結構、男女就業人口比例及產業人口結構等，貝IJ無法瞭解旅次發生率改變的背後原因。基於此，本研究將針對數量最多且發生於尖峰時段之家一工作旅次進行研究，探討結構化之社會經濟變數對家一工作旅次發生之影響。此外，本研究亦嘗試以台北都會區之資料進行實證分析，並比較分析類神經網路法與多元迴歸法之分析結果。

Trip generation is a major step in four-step transportation demand analysis. In the past， the socioeconomic factors considered in trip generation model were income， population，employment， and number of vehicles in each traffic zone or household. However， the big changes in the socioeconomic structure such as the rise in elder people， the increase in the number of women at work and the growth in the service sector， etc.， has caused the change of trip generation. Therefore， this study tries to establish the trip generation model with structural socioeconomic factors for the largest and the most important home-based work trips. Besides， this study uses artificial neural network and regression analysis for the empirical study with Taipei Metropolitan Household Interview Survey data and compares their results.