|
|
Identifying Abnormal Riding Behaviour in Urban Rail Transit with Multi-Source Data |
Xue Gang1,2,Liu Shifeng1,2,Gong Daqing1,2(),Zhang Pei1,2,Liu Zhongliang3 |
1School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China 2Beijing Logistics Informatization Research Base, Beijing 100044, China 3Beijing Jingtou Yiyajie Transportation Technology Co., Ltd., Beijing 100101, China |
|
|
Abstract [Objective] This study constructs data sets and algorithms to identify abnormal riding behaviour in urban rail transit (theft, begging, performing arts, and unauthorized advertisement distribution). [Methods] By constructing a spatiotemporal matrix, the passengers’ spatiotemporal trajectories are refined into the spatiotemporal feature map. All travel records are retained in the map without increasing complexity. Then, we used the spatiotemporal feature map as input to create an algorithm framework based on the attention mechanism and graph convolution neural networks. This algorithm can extract passengers’ key trajectory pattern features and identify abnormal behaviour from the regular passenger flow. [Results] Experimental results demonstrate the effectiveness of the proposed method, achieving a precision of 93.10%, a recall of 95.30%, and an F1 of 94.19%. All evaluation metrics have improved by over 3% compared to the baseline model. [Limitations] More research is needed to expand the sample size of the dataset and address the false positive issues. Our model cannot identify abnormal passengers who frequently change their smart cards. [Conclusions] This study constructs a dataset for abnormal commuting behavior with a larger sample size and reduced workload. The model can serve as a tool for accurately identifying abnormal commuting behavior in rail transit systems.
|
Received: 23 June 2022
Published: 07 September 2023
|
|
Fund:Beijing Natural Science Foundation(9222025);National Natural Science Foundation of China(62276020);National Social Science Fund of China(21FGLB059) |
Corresponding Authors:
Gong Daqing,ORCID:0000-0002-2405-851X,E-mail: dqgong@bjtu.edu.cn。
|
[1] |
黄海蕾. 5月1日起北京地铁乞讨卖艺最高罚1千元[N]. 京华时报, 2015-04-30 ( 1).
|
[1] |
(Huang Hailei. From May 1st, the Maximum Penalty of Begging at Beijing Metro is 1,000 Yuan[N]. Jinghua Times, 2015-04-30 ( 1).)
|
[2] |
Pan B, Zheng Y, Wilkie D, et al. Crowd Sensing of Traffic Anomalies Based on Human Mobility and Social Media[C]// Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York: ACM, 2013: 344-353.
|
[3] |
Hong L, Zheng Y, Yung D, et al. Detecting Urban Black Holes Based on Human Mobility Data[C]// Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York: ACM, 2015: 1-10.
|
[4] |
Jiang S, Ferreira J, Gonzalez M C. Discovering Urban Spatial-Temporal Structure from Human Activity Patterns[C]// Proceedings of the 2012 ACM SIGKDD International Workshop on Urban Computing. New York: ACM, 2012: 95-102.
|
[5] |
Wang H Y, Li L Y, Pan P J, et al. Early Warning of Burst Passenger Flow in Public Transportation System[J]. Transportation Research Part C: Emerging Technologies, 2019, 105: 580-598.
doi: 10.1016/j.trc.2019.05.022
|
[6] |
王玲, 代前进, 吴晓隽. 基于预警平台大数据的事件旅游客流时空分布研究[J]. 数据分析与知识发现, 2018, 2(8): 31-40.
|
[6] |
(Wang Ling, Dai Qianjin, Wu Xiaojun. The Study on the Temporal and Spatial Distribution of Event Tourism Based on Large-Scale Tourism Early Warning Platform[J]. Data Analysis and Knowledge Discovery, 2018, 2(8): 31-40.)
|
[7] |
李德龙, 刘德海. 引入人脸抓拍系统还是升级安检设备? : 有限资源下的地铁暴恐防御序贯博弈模型[J]. 中国管理科学, 2022, 30(12): 280-292.
|
[7] |
(Li Delong, Liu Dehai. Introduce Face Capture System or Upgrade Security Equipment? -Sequential Game Model of Subway Terrorism Defense under Limited Resources[J]. Chinese Journal of Management Science, 2022, 30(12): 280-292.)
|
[8] |
Bouman P, Van der Hurk E, Kroon L, et al. Detecting Activity Patterns froms Mart Card Data[C]// Proceedings of the 25th Benelux Conference on Artificial Intelligence. 2013.
|
[9] |
Ma X L, Wu Y J, Wang Y H, et al. Mining Smart Card Data for Transit Riders’ Travel Patterns[J]. Transportation Research Part C: Emerging Technologies, 2013, 36: 1-12.
doi: 10.1016/j.trc.2013.07.010
|
[10] |
Du B W, Liu C R, Zhou W J, et al. Detecting Pickpocket Suspects from Large-Scale Public Transit Records[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(3): 465-478.
doi: 10.1109/TKDE.69
|
[11] |
Zhao X, Zhang Y, Liu H, et al. Detecting Pickpocketing Gangs on Buses with Smart Card Data[J]. IEEE Intelligent Transportation Systems Magazine, 2019, 11(3): 181-199.
doi: 10.1109/MITS.2019.2919525
|
[12] |
Xue G, Liu S F, Gong D Q. Identifying Abnormal Riding Behavior in Urban Rail Transit: A Survey on “In-Out” in the Same Subway Station[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(4): 3201-3213.
doi: 10.1109/TITS.2020.3032843
|
[13] |
Xue G, Gong D Q, Zhang J H, et al. Passenger Travel Patterns and Behavior Analysis of Long-Term Staying in Subway System by Massive Smart Card Data[J]. Energies, 2020, 13(10): 2670.
doi: 10.3390/en13102670
|
[14] |
Zhao J J, Qu Q, Zhang F, et al. Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(11): 3135-3146.
doi: 10.1109/TITS.2017.2679179
|
[15] |
李建勋, 张锐军, SAFONOV Paul, 等. 基于Copula函数和M-K检验的时空数据异常识别方法[J]. 系统工程理论与实践, 2019, 39(12): 3229-3236.
doi: 10.12011/1000-6788-2017-2201-08
|
[15] |
(Li Jianxun, Zhang Ruijun, Safonov P, et al. Outlier Recognition Method for Spatio-Temporal Data Based-on Copula Function and M-K Test[J]. Systems Engineering-Theory & Practice, 2019, 39(12): 3229-3236.)
doi: 10.12011/1000-6788-2017-2201-08
|
[16] |
赖永炫, 张璐, 杨帆, 等. 基于时空相关属性模型的公交到站时间预测算法[J]. 软件学报, 2020, 31(3): 648-662.
|
[16] |
(Lai Yongxuan, Zhang Lu, Yang Fan, et al. Bus Arrival Time Prediction Algorithm Based on Spatio-Temporal Correlation Attribute Model[J]. Journal of Software, 2020, 31(3): 648-662.)
|
[17] |
He K M, Zhang X Y, Ren S Q, et al. Deep Residual Learning for Image Recognition[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2016: 770-778.
|
[18] |
Kipf T N, Welling M. Semi-Supervised Classification with Graph Convolutional Networks[OL]. arXiv Preprint, arXiv: 1609.02907.
|
[19] |
Woo S, Park J, Lee J Y, et al. CBAM: Convolutional Block Attention Module[C]// Proceedings of the 2018 European Conference on Computer Vision. Cham: Springer, 2018: 3-19.
|
[20] |
冯宁, 郭晟楠, 宋超, 等. 面向交通流量预测的多组件时空图卷积网络[J]. 软件学报, 2019, 30(3): 759-769.
|
[20] |
(Feng Ning, Guo Shengnan, Song Chao, et al. Multi-Component Spatial-Temporal Graph Convolution Networks for Traffic Flow Forecasting[J]. Journal of Software, 2019, 30(3): 759-769.)
|
[21] |
于瑞云, 林福郁, 高宁蔚, 等. 基于可变形卷积时空网络的乘车需求预测模型[J]. 软件学报, 2021, 32(12): 3839-3851.
|
[21] |
(Yu Ruiyun, Lin Fuyu, Gao Ningwei, et al. Passenger Demand Forecast Model Based on Deformable Convolution Spatial-Temporal Network[J]. Journal of Software, 2021, 32(12): 3839-3851.)
|
[22] |
Kipf T N, Welling M. Semi-Supervised Classification with Graph Convolutional Networks[C]// Proceedings of the International Conference on Learning Representations. 2016.
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|