A Method for Population Synthesis Using Mathematical Programming for Forecasting Travel Demand
Abstract:
In Transportation Master Plans (TMP), the detailed socioeconomic data of study area population are needed for forecasting travel demand. However, in Household Travel Survey (HTS), these data are collected only for a small sample of households. Since collection of such data for the entire population is too expensive, if not infeasible, it is necessary to produce synthetically such data using the macro-data of the population and micro-data of the sample. This process is known as population synthesis in the literature or as data expansion in Iran. It seems that the existing method of expansion did not result in valid assignment volume and trip matrix. Hence, a linear programming model is proposed for data expansion, which used variables at the household and individual levels simultaneously. This method estimates weights for households while minimizing the deviation of synthesized population socio-economics from observed ones obtained from census bureau. The comparisons between the results of the two methods (that are applied for the city of Mashhad) show that the coefficient of determination between the observed and estimated population of zones for the existing and the proposed methods are 0.79 and 0.97, respectively. Also, the proposed method resulted in a valid trip matrix and assignment volume. The percentage errors of screen lines daily volume are 25% in the existing method, whereas they are less than the acceptable 14% error in the proposed method. Root mean square error (RMSE) of links volume are 55% and 26% in the existing and the proposed methods, respectively.
Language:
Persian
Published:
Journal of Transportation Engineering, Volume:7 Issue:3, 2016
Pages:
419 - 434
magiran.com/p1560553  
روش‌های دسترسی به متن این مطلب
اشتراک شخصی
در سایت عضو شوید و هزینه اشتراک یک‌ساله سایت به مبلغ 300,000ريال را پرداخت کنید. همزمان با برقراری دوره اشتراک بسته دانلود 100 مطلب نیز برای شما فعال خواهد شد!
اشتراک سازمانی
به کتابخانه دانشگاه یا محل کار خود پیشنهاد کنید تا اشتراک سازمانی این پایگاه را برای دسترسی همه کاربران به متن مطالب خریداری نمایند!