Multi-vehicle ride matching problem(MRMP) studies the problem of taking passengers as much as possible through optimizing vehicles' route and matching between vehicles and passengers. But at present, there are some problems in the researches such as models divorce from reality and low efficiency of algorithms. For this problem,this paper presented APSOCAttractive Particle Swarrn Optimization) to solve this problem. The MRMP is transferred to RMP through APSO to form the first matching result between vehicles and passengers. And the best sort order is looked through sorting the result,making use of priori clustering based on the result from first matching. And at last,we optimized the solution one times more through optimized rules. Contrast experiment shows that the method based on APSO can solve the problem on a high rate of matching and a low cost.%车辆合乘匹配问题是研究如何通过优化车辆路线及车辆-乘客匹配来搭乘尽量多的乘客的问题.目前国内外的研究多存在模型单一、脱离实际、算法效率不高等问题.针对该问题,提出一种基于吸引粒子群算法的问题求解方法.通过吸引粒子群算法进行多车辆问题向单车辆问题的转化,形成车辆同乘客之间的初次匹配.根据初次匹配结果利用先验聚类的思想将初次匹配结果进行排序,寻找较优需求序列排序方式.最后,通过相应的匹配再优化策略将需求序列进行再优化.对比实验表明,基于吸引粒子群算法的问题求解方式能以较高的搭乘成功率以及较低的花费完成车辆合乘匹配问题.
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