Abstract: Designing reasonable pricing for tourist buses has a positive impact on the development of tourist public transport. Through the analysis of tourists' travel preferences, considering that tourists of different ages have different choosing behaviors, a bi-level programming model of Tourism Bus pricing is established, the upper level is the most profitable model for bus enterprises, and the lower level is the stochastic user equilibrium of multi-population elastic demand. An improved particle swarm optimization algorithm is designed for model solving. The results of numerical experiments show that age characteristics will affect the optimal pricing strategy. Comfort sensitivity coefficient affects pricing, and tourist buses are more comfortable than conventional buses, which improves the competitiveness of enterprises. Compared with the standard particle swarm optimization, the improved particle swarm optimization algorithm has better solutionperformance and quality.

Key words: tourism public transport, age characteristics, tourist bus pricing, improved particle swarm optimization

摘要： 为旅游巴士设计合理的定价,对旅游公共交通的发展有着积极影响。通过对游客出行偏好的分析,考虑不同年龄阶段的游客在选择行为上有较大的差异,建立了上层以旅游巴士企业利润最大为目标,下层为多方式多人群弹性需求随机用户平衡的旅游巴士定价模型,并设计了改进粒子群算法求解问题。数值实验结果表明:1)年龄特征会影响最优定价策略,考虑游客年龄在选择行为上的差异得出的票价更优;2)舒适度敏感系数对定价有影响,且旅游巴士较常规公交,舒适度更好,一定程度上提高了旅游巴士企业的竞争力;3)改进粒子群算法较标准粒子群算法,有更好的求解性能和质量。

关键词: 旅游公共交通, 年龄特征, 旅游巴士定价问题, 改进粒子群算法