基于一卡通数据分析的通勤性定制公交线路规划

发布时间：2018-01-12 03:33

本文关键词：基于一卡通数据分析的通勤性定制公交线路规划　出处：《吉林大学》2017年硕士论文　论文类型：学位论文

【摘要】：近年来,大数据处理技术渐趋成熟,给交通行业的发展带来了变革,它多角度多维度的数据展现能力,使我们能够更加直观、清晰地认识居民出行特点、交通需求的时空分布特征等。如何利用大数据呈现的宏观数据特征,指导城市交通规划已然成为交通领域当前研究的热点。论文以一卡通数据为基础,运用大数据处理技术挖掘通勤性定制公交目标服务群体客流的时空分布特征,并以此为基础,运用单目标规划模型进行通勤性定制公交线路规划,旨在在新的数据环境下,更加经济、直观、准确地把握客流分布特征,精细化通勤性定制公交线路规划,从而为定制公交的推广使用提供理论基础。在一卡通数据的通勤出行信息识别方法的研究方面,以上海市一卡通数据以及公交车实时运行数据等数据为例,以ORACLE数据库为平台,设计了识别通勤群体、通勤出行起终点以及判断中途停驻点性质的方法,该方法适用于具有相同数据结构的其他一卡通数据。为了更好的把握公共交通通勤客流向定制公交转移的特点,分别建立了公交、地铁、公交地铁换乘向通勤性定制公交转移的Logit模型,并运用上海市P区的通勤性出行方式转移意向调查对模型进行参数标定,标定模型能够在一定程度上反映城市公共交通通勤群体向定制公交转移的客流关系。在通勤性定制公交线路规划方面,以一站直达优先、快速性、准时性、舒适性以及经济性为原则,建立了以经济性和快速性权衡最优为目标,以满载率、站点数目、到达时间等指标为约束的优化模型,并以标准遗传算法为基础,设计了适用于该问题求解的改进遗传算法,模型求解结果能够给出较为合理的通勤性定制公交规划线路。最后,以上海开放数据为基础,运用上述方法分析了上海市P区居民的通勤出行特点,并规划了通勤性定制公交线路,较为良好的实现了运用大数据支撑交通规划这个初衷。
[Abstract]:In recent years, big data processing technology gradually matured, to the development of the transportation industry has brought changes, its multi-angle and multi-dimensional data display ability, so that we can more intuitive, clear understanding of the characteristics of residents travel. How to use the macro-data features presented by big data to guide urban traffic planning has become the current research hotspot in the field of transportation. The paper is based on one-card data. Using big data processing technology to excavate the spatial and temporal distribution characteristics of commuting customized public transport target service group passenger flow, and on the basis of this, the single-objective programming model is used to carry out the commuting customized bus route planning. In the new data environment, it is more economical, intuitive and accurate to grasp the characteristics of passenger flow distribution, and to refine the commuting customized bus line planning. In order to provide a theoretical basis for the promotion and use of customized public transport. In the study of commute information identification method of one-card data, take Shanghai one-card data and bus real-time operation data as an example. Based on ORACLE database, this paper designs a method to identify commuting groups, start and finish commuting and judge the nature of stop-point. This method is suitable for other one-card data with the same data structure. In order to better grasp the characteristics of public transport commuter to customized public transport, respectively, the establishment of public transport, subway. The Logit model of transit transfer from transit to commuting customization is proposed and the parameters of the model are calibrated by the investigation of commuting mode transfer intention in P District of Shanghai. The calibration model can reflect to a certain extent the passenger flow relationship between the urban public transport commuter group and the customized public transport. In the aspect of commuting custom bus route planning, one station direct first, fast and punctual. Based on the principle of comfort and economy, the optimization model is established, which is based on standard genetic algorithm (GA). The optimization model is based on the criterion of full load rate, number of stations, arrival time and so on. An improved genetic algorithm is designed for solving the problem. The result of the model can give a reasonable transit planning route. Finally, based on Shanghai open data. This paper analyzes the commuting characteristics of residents in P District of Shanghai by using the above method, and plans the commuting customized bus lines, which realizes the original intention of using big data to support traffic planning.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U491.17

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