Two-stage travel itinerary recommendation optimization model considering stochastic traffic time

Abstract

In an increasingly competitive market, most online tour operators have launched personalized travel itinerary recommendation services. Different from previous studies, this research considers the travel itinerary planning problem under total time limit and uncertain traffic time. This problem requires two stages of decision-making: firstly, selecting the tourist attractions to visit from the candidate attractions based on maximizing the popularity utility of tourists; secondly, planning the tourist attraction visit sequence under stochastic traffic time to maximize the tourist activity utility. Therefore, a two-stage stochastic optimization model with chance constraint is constructed and then solved by the sample average approximation (SAA) method. In order to verify the effectiveness of our model, we introduced two benchmark models for comparative analysis. The results show that in the worst case, our model increases the reliability level of travel itineraries by almost 40% compared with the two benchmark models. In addition, case studies of two large cities, i.e., Nanjing and Beijing, indicate that a tour operator can optimize travel itinerary recommendations by improving tourists utility as well as their own profitability without much loss of reliability.