摘要：

针对目前交通拥堵致使出行打车难的问题，设计并实现一种基于云计算的手机智能出租车呼叫系统。该系统由云服务器和Android手机客户端组成，服务器利用云计算环境下的Map-Reduce并行编程模型对K-means聚类算法实施并行化，提高推送信息的质量和效率；客户端分别利用LocationClient、MapView和MKOfflineMap接口实现定位服务、图层展示更新和百度离线地图服务功能，通过Android智能手机平台为用户提供及时、准确的信息服务。在客户端和服务器之间，利用RPC服务推送Protocol Buffer协议序列化的信息。实验结果表明，与滴滴打车软件相比，该系统的搜索推荐效率提高了20%左右，离线地图展示及定位流量比在线方法减少了90%以上，快速响应性能较好。

关键词: 智能交通, 聚类, 云计算, 推送, Protocol Buffer协议, 百度离线地图

Abstract:

In view of the increasing difficulty in taking a taxi because of the traffic jam, an intelligent taxi calling system based on cloud computing is proposed, which consists of cloud server and Android ends. This paper adopts Map-Reduce model to process the K-means clustering algorithm in a parallel way to improve the quality and efficiency of pushing data on the cloud server side and uses LocationClient interface, MapView interface and MKOfflineMap interface to implement the location service, overlays display and update service and Baidu offline map service respectively on the Android smart ends. And the Remote Procedure Call(RPC) service is used to realize the push of the data information which is serialized by Protocol Buffer(PB) protocol between the cloud server and Android ends. The designed system, which gets about 20% improvement in searching and push efficiency and 90% reduction in data traffic and big improvement in response time.

Key words: intelligent transportation, clustering, cloud computing, push, Protocol Buffer(PB) protocol, Baidu offline map

中图分类号: 

• TP311