1. 毕业设计（论文）的内容和要求

近年来城市化进程加剧,在较短时间内城市人口急剧增长,这考验着城市各方面的承载能力,尤其对城市交通提出了更高的要求。

交通调查是交通理论研究和技术创新的基石,其中居民出行信息更是重要的调查内容。

目前广泛应用的居民出行调查法存在周期长、成本高、数据质量不高等问题,已逐渐不能满足大规模、高频率的居民出行调查的要求。

2. 参考文献

[1] Hashemi M, Karimi H A. A Machine Learning Approach to Improve the Accuracy of GPS-Based Map-Matching Algorithms (Invited Paper)[C]// IEEE, International Conference on Information Reuse and Integration. IEEE, 2016:77-86. [2] Zheng Y, Zhang L, Xie X, et al. Mining interesting locations and travel sequences from GPS trajectories[C]// International Conference on World Wide Web. ACM, 2009:791-800. [3] Zheng Y, Zhou X. Computing with Spatial Trajectories[M]. Springer New York, 2011. [4] Giannotti, Fosca, Nanni, et al. Trajectory pattern mining[J]. 2007:330-339. [5] State L, Cocianu C, Vlamos P, et al. PCA-based data mining probabilistic and fuzzy approaches with applications in pattern recognition[C]// Icsoft 2006, First International Conference on Software and Data Technologies, Set#250;bal, Portugal, September. DBLP, 2006:55-60. [6] Deng Z, Ji M. Deriving Rules for Trip Purpose Identification from GPS Travel Survey Data and Land Use Data: A Machine Learning Approach[C]// International Conference on Traffic and Transportation Studies. 2010:768-777. [7] Ashbrook D, Starner T. Using GPS to learn significant locations and predict movement across multiple users[J]. Personal Ubiquitous Computing, 2003, 7(5):275-286. [8] Zhou Z H, Li M. Semisupervised Regression with Cotraining-Style Algorithms[M]. IEEE Educational Activities Department, 2007. [9] Ellis K, Godbole S, Marshall S, et al. Identifying Active Travel Behaviors in Challenging Environments Using GPS, Accelerometers, and Machine Learning Algorithms[J]. Frontiers in Public Health, 2014, 2:36.. [10] H#250;sek D, Pokorny J, #344;ezankov#225; H, et al. Web Data Clustering[J]. Studies in Computational Intelligence, 2009, 204:325-353. [11] Woodleya R, Nolla W, Barkera J, et al. Automatic building identification using gps and machine learning[C]// Geoscience and Remote Sensing Symposium. IEEE, 2010:2739-2742. [12] Lu Y, Zhu S, Zhang L. Imputing Trip Purpose Based on GPS Travel Survey Data and Machine Learning Methods[C]// Transportation Research Board 92nd Annual Meeting. 2013. [13] 张钦礼. 基于支持向量机和模糊系统的机器学习方法及其应用研究[D]. 江南大学, 2009. [14] 纪思捷, 胡豪杰. 基于机器学习算法的大数据处理[J]. 电子技术与软件工程, 2015(23):202-202. [15] 吉根林, 赵斌. 时空轨迹大数据模式挖掘研究进展[J]. 数据采集与处理, 2015, 30(1):47-58. [16] 邓中伟, 季民河. GPS轨迹中出行目的提取的一种智能算法[J]. 2010. [17] 高强, 张凤荔, 王瑞锦,等. 轨迹大数据:数据处理关键技术研究综述[J]. 软件学报, 2017, 28(4):959-992.

3. 毕业设计（论文）进程安排

起讫日期 设计（论文）各阶段工作内容 2017-12-15～ 2017 -12-22 选题。

2017-12-23～2018-02-28 查阅文献资料，准备开题报告，正式开题。

2018-03-01～ 2018-03-15 预处理原始轨迹数据包 2018-03-16～ 2018-04-15 对轨迹数据进行分析特征提取 2018-04-16～ 2018-05-15 将轨迹分析结果可视化 2018-05-16～ 2018-05-31 认真撰写毕业设计论文； 完成英文文献的翻译工作。