【摘要】 作为城市交通的基础设施,出租车在日常交通中起着重要作用。随着城市规模的扩大,人们的出行需求不断增加,然而出租车的分布与叫车需求分布之间难以匹配,从而导致打车困难问题的出现,这一现象在大型城市尤其明显。造成打车难的主要原因在于司乘之间信息不能互通,GPS、车联网等技术能够提供车辆位置、运行轨迹等信息,通过对这些信息数据进行处理,可以获得有价值的信息,将其提供至司乘双方能够提升出租车运营效率。现有信息处理方法较为简单,忽略了较多关键影响因素,难以达到理想的效果。因此,本文提出了一种基于出租车轨迹和路网数据来衡量打车难度的出租车流量模型,并通过综合时间、天气等因素对模型进行优化,提升了模型的实用性。基于该模型利用数据挖掘算法抽取有用信息,提供给出租车司机和乘客。最后,本文基于实际出租车数据对模型进行实验验证,结果证明了模型的有效性及实用性。更多还原

【Abstract】 As an infrastructure of urban transport, taxis play an important role in everyday traffic. With the expansion of the city, people’s travel demand continues to increase.However, it is difficult to match the distribution of taxis with the distribution of demand, which results in the difficult problem of taking taxi, especially in large cities. The main reason causing difficulty of taking taxi is that the information cannot be exchanged between drivers and passengers.Current technology such as GPS and Internet of vehicles can provide vehicle location, running track and other information.Valuable information can be generated through data processing, by which drivers and passengers can enhance their travel efficiency. Existing data processing method is not efficient enough because of ignoring some key factors. This paper presents a taxi traffic model based on the taxi track and road network data to measure the difficulty of a taking a vacant taxi. Then we optimize the model by considering the other factors such as time and weather. We use data mining algorithms to extract useful information in the proposed model and then providing these valuable information to the taxi drivers and passengers. At last, we verify the model by experiments based on actual data of taxis and the results demonstrate the effectiveness and practicality of the proposed model.更多还原