|
|
基于出租车运营数据的载客区域聚类及热点特征分析
|
Abstract:
为优化出租车空间资源调度，针对路网密集、数据密度差异较小条件下的城市出租车载客区域聚类问题，本文提出了一种采用OPTICS算法的聚类模型。通过实验与传统DBSCAN算法下的聚类结果进行对比，发现OPTICS算法更能有效地避免参数设置对实验结果的影响，解决传统DBSCAN算法在此类应用中聚类划分模糊的问题，达到出租车资源调度精细化的效果，对提升出租车载客率、降低空载时间具有现实指导作用。
To optimize the taxi space resource dispatch, a clustering model using OPTICS algorithm is pro-posed for the clustering of taxi passenger carrying area under the condition of intensive road and small difference in data density. By comparing with the results of the traditional DBSCAN algorithm in experiment, it is found that the OPTICS algorithm can effectively eliminate the interference of parameter setting on the experimental results. It helps to the problem of traditional algorithm DBSCAN applied to such situation, and enhances the efficiency of taxi dispatch. This research has practical guiding effect on improving the load factor of taxi and reducing the time of idle load.
| [1] | 郑林江, 赵欣, 蒋朝辉, 邓建国, 夏冬, 刘卫宁. 基于出租车轨迹数据的城市热点出行区域挖掘[J]. 计算机应用与软件, 2018, 35(1): 1-8. |
| [2] | Ibrahim, R. and Omair Shafiq, M. (2019) Detecting Taxi Movements Using Random Swap Clustering and Sequential Pattern Mining. Journal of Big Data, 6, 1-26. https://doi.org/10.1186/s40537-019-0203-6 |
| [3] | 桂智明, 向宇, 李玉鉴. 基于出租车轨迹的并行城市热点区域发现[J]. 华中科技大学学报(自然科学版), 2012(S1): 187-190. |
| [4] | 姬波, 叶阳东, 肖煜. 基于信息瓶颈方法的出租车空载聚集区聚类算法[J]. 小型微型计算机系统, 2013, 34(9): 2139-2143. |
| [5] | 毕硕本, 万蕾, 杨树亮, 闫业超, Nkunzimana Athanase. 基于GPS数据的南京出租车上下客时间特征及热点时空分布[J]. 中国科技论文, 2018, 13(9): 1023-1028. |
| [6] | 曲昭伟, 王鑫, 宋现敏, 夏英集, 袁咪莉. 基于出租车GPS大数据的城市热点出行路段识别方法[J]. 交通运输系统工程与信息, 2019, 19(2): 238-246. |
| [7] | Tang, J.J., Liu, F., Wang, Y.H., et al. (2015) Un-covering Urban Human Mobility from Large Scale Taxi GPS Data. Physica A: Statistical Mechanics and its Applications, 438, 140-153. https://doi.org/10.1016/j.physa.2015.06.032 |
| [8] | Ester, M., Kriegel, H.P., Sander, J., et al. (1996) A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. International Conference on Knowledge Discovery & Data Mining, Portland, 2-4 August 1996, 226-231. |
| [9] | Ankerst, M., Breunig, M., Kriegel, H.P. and Sandler, J. (1999) OPTICS: Ordering Points to Identify the Clustering Structure. Proceedings of the Interna-tional Conference on Management of Data (SIGMOD’99), Philadelphia, 1-3 June 1999, 49-60. https://doi.org/10.1145/304182.304187 |
| [10] | Davies, D.L. and Bouldin, D.W. (1979) A Cluster Separation Meas-ure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1, 224-227. https://doi.org/10.1109/TPAMI.1979.4766909 |
