an innovative methodology to extend intervening opportunity model for predicting home-based shopping trips

the conventional intervening opportunity model which is potentially a powerful analytical and probabilistic model, has been used in transportation planning to model trip distribution pattern. this model is basically suffering from two main limitations. these limitations are the assumptions that trip makers are fully aware of all available opportunities in the area and their trip lengths to all possible destinations and that they evaluate all the destinations during their decision making process. this paper has attempted to extend conventional intervening opportunity, omit the limitations inherent in it and maximize its compatibility with the common destination choice pattern of home-based shopping trips. the overall idea is that for the main parameters of the intervening opportunity model (i.e. the opportunity and destination ranking variables) some coefficients and weights may be considered in order to calibrate the model once these weights and coefficients are calculated and included. these weights have been considered as modification factors according to the knowledge of the trip makers about the opportunities at destinations and also the knowledge on the accessibility of the shopping destinations. in this paper, the proposed structure of extended intervening opportunity model (eiom) has been calibrated for a case study in qazvin city. so, the effectiveness of conventional intervening opportunity model and extended intervening opportunity model has been measured and compared. in addition to results of calibrations and validations for both models, relative improvement of proposed model in comparing to conventional intervening opportunity model has been shown