大数据背景下动态共乘的研究进展

发布时间：2018-04-10 05:31

  本文选题：共乘　切入点：动态共乘　出处：《计算机研究与发展》2017年01期

【摘要】：共乘也被称为"合乘"、"拼车"、"顺风车",通过有效整合运力资源减少路上行驶车辆数量,对缓解交通拥堵、降低出行费用、减轻环境污染都有重要意义.大数据背景下实时更新的车辆位置信息数据、城市交通数据、社交网络数据,为智能出行特别是共乘带来了全新的发展机遇.在车辆行驶中对乘客请求进行实时匹配的动态共乘,是大数据背景下智能出行发展趋势的代表.在统一归纳了解决动态共乘实时性的Filter and Refine框架基础上,介绍了动态共乘的各种类型;针对大数据背景下动态共乘问题遇到的问题,对Filter步骤中预先计算可行解、建立动态空间索引、基于请求分组预处理及并行优化方法,Refine步骤中简化计算模型、采用新型数据结构、利用启发式算法等优化方法进行了详细介绍;然后对大数据背景下保证动态共乘系统的价格机制、信用体系和人机接口等相关技术进行了分析;最后,总结展望了大数据背景下动态共乘中亟待解决的关键问题和未来的研究方向,以期为创造低碳生活、绿色出行,解决环境污染有所启示.
[Abstract]:Co-ride is also known as "co-ride", "carpool", "windmill", through the effective integration of capacity resources to reduce the number of vehicles on the road, to ease traffic congestion, reduce travel costs, reduce environmental pollution are of great significance.The real-time update of vehicle location data, urban traffic data and social network data under the background of big data has brought a new development opportunity for intelligent travel, especially co-riding.The dynamic co-riding of real-time matching passenger requests in vehicle driving is the representative of the development trend of intelligent travel under the background of big data.On the basis of generalizing the Filter and Refine framework to solve the real-time dynamic co-multiplication, various types of dynamic co-multiplication are introduced, and the feasible solution in the Filter step is calculated in advance for the problems encountered in the dynamic co-multiplication problem under the background of big data.Based on the preprocessing of request grouping and the parallel optimization method, the simplified computing model is established. A new data structure and heuristic algorithm are introduced in detail.Then the paper analyzes the price mechanism, credit system and man-machine interface of the dynamic co-multiplication system under big data background.The key problems and future research directions in dynamic comultiplication under big data background are summarized and prospected, in order to create low-carbon life, green travel and solve environmental pollution.
【作者单位】： 清华大学计算机科学与技术系;北德克萨斯州大学计算机科学与工程系;
【分类号】：U491;TP311.13
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本文编号：1729880