感知乘客心理的出租车动态合乘优化方法

Dynamic Shared Taxi Optimization Method Considering Passengers Perceptions

为研究考虑乘客感知的动态合乘问题,本文提出一种改进的算法框架。基于可行出行对概念,构建乘客满意度最大、出行时间最少的多目标线性规划问题,将合乘问题转化为车辆和乘客间的线性分配问题,并采用基于精英策略的人工蜂群算法(Elitism based Multi-Objective Artificial Bee Colony,EMOABC)求解。根据海口市出租车订单数据建立算例,实验结果表明,该算法框架能够实时提供优质动态合乘方案。相比单纯优化出行效率,考虑乘客心理的合乘策略,相对提高12%的乘客满意度,服务率等方面也有较好表现。

This paper proposes an improved algorithm framework to study the dynamic ride-sharing service optimization problem considering passengers'perceptions of service quality.The problem is modeled as a linear assignment problem between vehicles and passengers based on the concept of feasible trip pairs,which is formulated as a multi-objective linear programming model,with the objectives of maximizing passengers'satisfaction and minimizing their total travel time.An elitism-based multi-objective artificial bee colony(EMOABC)algorithm is developed to solve the model.A case study on the taxi order service in Haikou,China is conducted.The computation results indicate that the proposed framework could provide a high-quality scheme in real time.Compared with only optimizing trip efficiency,the ride-sharing strategy with perceiving passenger psychology can improve passenger satisfaction by 12%.The service rate,as well as other indicators,is also at a high level.