A novel methodology to monitor passenger mobility performance in urban subway stations

Young Jo; Eunbi Jeong; Seolyoung Lee; Cheol Oh

doi:10.22712/susb.20210015

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2021 Vol.12, Issue 2 Preview Page Next Page

General Article

A novel methodology to monitor passenger mobility performance in urban subway stations

30 June 2021. pp. 186-203

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Abstract

Sustainable smart technology is important to prevent roaming passengers and improve pedestrian space environment by providing effective route information service in subway stations. A pedestrian trajectory collection system based on LiDAR sensors can continuously track individual passengers with a wide detection range and high accuracy for monitoring pedestrian space in a subway station. This study developed a methodology for detecting and classifying abnormal passenger trajectory patterns in a subway station using passenger trajectory data collected from LiDAR sensors. Further, feature vectors were extracted toward the reliable characterization of passenger walking trajectories. An ensemble method that combines decision tree, support vector machine, and artificial neural network was adopted to increase the classification accuracy for abnormal passenger trajectory patterns. The proposed ensemble method was able to obtain 94.4% classification accuracy, which was superior to that of a single classifier. In addition, this study devised a performance measure to identify the performance of passenger mobility in subway stations, referred to as passenger mobility index (PMI), based on the classification results. The proposed methodology is expected to be utilized to provide a passenger mobility evaluation system based on intelligent facilities such as smart construction and safety.

Keywords

machine learning

ensemble method

classification

LiDAR sensors

passenger mobility

References

F. Ganansia, C. Carincotte, A. Descamps, and C. Chaudy, A Promising Approach to People Flow Assessment in Railway Stations Using Standard CCTV Networks. In Transport Research Arena. (2014), Paris, France.

F.S. Hanseler, M. Bierlaire, and R. Scarinci, Assessing the Usage and Level-of-service of Pedestrian Facilities in Train Stations: A Swiss Case Study. Transportation Research Part A: Policy and Practice. 89 (2016), pp. 106-123. 10.1016/j.tra.2016.05.010

W. Luo, L. Sun, Y. Li, Y. Zhang, and J. Rong, A New Attempt for Pedestrian Level of Service in Subway Transfer Corridor Based on The Physiological Characteristics. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), IEEE. 2018. 10.1109/ITSC.2018.8569477

X.Y. Xu, J. Liu, H.Y. Li, and J.Q. Hu, Analysis of Subway Station Capacity with the Use of Queueing Theory. Transportation Research Part C: Emerging Technologies, 38 (2014), pp. 28-43. 10.1016/j.trc.2013.10.010

Highway Capacity Manual 2010, Transportation Research Board, National Research Council, Washington, DC, 2010.

C.B. Ai and Y.C.J. Tsai, An Automated Sign Retroreflectivity Condition Evaluation Methodology Using Mobile LIDAR and Computer Vision. Transportation Research Part C: Emerging Technologies. 63 (2016), pp. 96-113. 10.1016/j.trc.2015.12.002

X. Du, M.H. Ang, and D. Rus, Car Detection for Autonomous Vehicle: LIDAR and Vision Fusion Approach Through Deep Learning Framework. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2017. 10.1109/IROS.2017.8202234

A. Mukhtar, L. Xia, and T.B. Tang, Vehicle detection techniques for collision avoidance systems: A review. IEEE Transactions on Intelligent Transportation Systems, 16(5) (2015), pp. 2318-2338. 10.1109/TITS.2015.2409109

Y. Jo, E. Jeong, S.I. You, and C. Oh, Analysis of Pedestrian Trajectory Patterns Using LiDAR-based Pedestrian Tracking. Journal of Korean Society of Transportation. 36 (2018), pp. 503-518. 10.7470/jkst.2018.36.6.503

U. Wandinger, Introduction to Lidar in Lidar-Range-Resolved Optical Remote Sensing of the Atmosphere, In Claus Weitkamp. 2005, Springer.

LG Hitachi, Path analysis solution. [online]. Available at: http://www.lghitachi.co.kr/products/iot/lidar/index.jsp [Accessed 10/4/2021]

D. Martino-Saltzman, B.B. Blasch, R.D. Morris, and L.W. McNeal, Travel Behavior of Nursing Home Residents Perceived as Wanderers and Nonwanderers. The Gerontologist. 31(5) (1991), pp. 666-672. 10.1093/geront/31.5.666

7th Dimension of Human Body Report. Ministry of Trade, Industry and Energy, Republic of Korea, 2016.

O. Bagdadi and A. Várhelyi, Development of a method for detecting jerks in safety critical events. Accident Analysis & Prevention, 50 (2013), pp. 83-91. 10.1016/j.aap.2012.03.032

L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. Wadsworth Int. Group. 37(15) (1984), pp. 237-251.

C.J.C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery. 2(2) (1998), pp. 121-167. 10.1023/A:1009715923555

H. White, Learning in Artificial Neural Networks: A Statistical Perspective. Neural Computation. 1(4) (1989), pp. 425-464. 10.1162/neco.1989.1.4.425

L. Chen, M. Lv, and G. Chen, A system for destination and future route prediction based on trajectory mining. Pervasive and Mobile Computing. 6(6) (2010), pp. 657-676. 10.1016/j.pmcj.2010.08.004

F. Giannotti, M. Nanni, D. Pedreschi, F. Pinelli, C. Renso, S. Rinzivillo, and R. Trasarti, Unveiling the complexity of human mobility by querying and mining massive trajectory data. The VLDB Journal The International Journal on Very Large Data Bases. 20(5) (2011), pp. 695-719. 10.1007/s00778-011-0244-8

S. Qiao, C. Tang, H. Jin, T. Long, S. Dai, Y. Ku, and M. Chau, PutMode: prediction of uncertain trajectories in moving objects databases. Applied Intelligence. 33(3) (2010), pp. 370-386. 10.1007/s10489-009-0173-z

J.R. Quinlan, C4. 5: Programming for Machine Learning. Morgan Kauffmann. 38 (1993), pp. 48.

C. Cortes and V. Vapnik, Support-vector Networks. Machine Learning. 20(3) (1995), pp. 273-297. 10.1007/BF00994018

V. Vapnik, The nature of statistical learning theory. 2013, Springer science & business media.

C. Piciarelli, C. Micheloni, and G.L. Foresti, Trajectory-based anomalous event detection. IEEE Transactions on Circuits and Systems for video Technology. 18(11) (2008), pp. 1544-1554. 10.1109/TCSVT.2008.2005599

Z. Sun and X.J. Ban, Vehicle classification using GPS data. Transportation Research Part C: Emerging Technologies. 37 (2013), pp. 102-117. 10.1016/j.trc.2013.09.015

M. Dougherty, A Review of Neural Networks Applied to Transport. Transportation Research Part C: Emerging Technologies. 3(4) (1995), pp. 247-260. 10.1016/0968-090X(95)00009-8

W. Duch and N. Jankowski, Survey of Neural Transfer Functions. Neural Computing Surveys. 2(1) (1999), pp. 163-212.

J. Zhao, H. Xu, D. Wu, and H. Liu, An Artificial Neural Network to Identify Pedestrians and Vehicles from Roadside 360-Degree LiDAR Data (No. 18-03129), 2018.

J. Zhao, H. Xu, H. Liu, J. Wu, Y. Zheng, and D. Wu, Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors. Transportation Research Part C: Emerging Technologies. 100 (2019), pp. 68-87. 10.1016/j.trc.2019.01.007

D.R. Jones, A Taxonomy of Global Optimization Methods Based on Response Surfaces. Journal of Global Optimization. 21(4) (2001), pp. 345-383. 10.1023/A:1012771025575

J. Snoek, H. Larochelle, and R.P. Adams, Practical Bayesian Optimization of Machine Learning Algorithms. In Advances in Neural Information Processing Systems. (2012), pp. 2951-2959.

Z. Wang and N. de Freitas, Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-parameters. arXiv preprint arXiv:1406.7758, 2014.

M.A. Gelbart, J. Snoek, and R.P. Adams, Bayesian Optimization with Unknown Constraints. arXiv preprint arXiv:1403.5607, 2014.

T.T. Joy, S. Rana, S. Gupta and S. Venkatesh, A Flexible Transfer Learning Framework for Bayesian Optimization with Convergence Guarantee. Expert Systems with Applications. 115 (2019), pp. 656-672. 10.1016/j.eswa.2018.08.023

T.G. Dietterich, Machine-learning Research - Four Current Directions. Ai Magazine. 18(4) (1997), pp. 97-136.

D. Opitz and R. Maclin, Popular Ensemble Methods: An Empirical Study. Journal of Artificial Intelligence Research. 11 (1999), pp. 169-198. 10.1613/jair.614

M.M. Bejani and M. Ghatee, A Context Aware System for Driving Style Evaluation by an Ensemble Learning on Smartphone Sensors Data. Transportation Research Part C: Emerging Technologies. 89 (2018), pp. 303-320. 10.1016/j.trc.2018.02.009

S.V. Hernandez, A. Tok, and S.G. Ritchie, Integration of Weigh-in-Motion (WIM) and Inductive Signature Data for Truck Body Classification. Transportation Research Part C: Emerging Technologies. 68 (2016), pp. 1-21. 10.1016/j.trc.2016.03.003

D.H. Wolpert, Stacked generalization. Neural networks. 5(2) (1992), pp. 241-259. 10.1016/S0893-6080(05)80023-1

S.V. Hernandez, A. Tok, and S.G. Ritchie, Integration of Weigh-in-Motion (WIM) and inductive signature data for truck body classification. Transportation Research Part C: Emerging Technologies. 68 (2016), pp. 1-21. 10.1016/j.trc.2016.03.003

M.M. Bejani and M. Ghatee, A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data. Transportation Research Part C: Emerging Technologies. 89 (2018), pp. 303-320. 10.1016/j.trc.2018.02.009

R. Polikar, Ensemble Based Systems in Decision Making. IEEE Circuits and systems magazine. 6(3) (2006), pp. 21-45. 10.1109/MCAS.2006.1688199

K. Polat and S. Gunes, A Novel Hybrid Intelligent Method Based on C4.5 Decision Tree Classifier and One-against-all Approach for Multi-class Classification Problems. Expert Systems with Applications. 36(2) (2009), pp. 1587-1592. 10.1016/j.eswa.2007.11.051

Information

Publisher :Sustainable Building Research Center (ERC) Innovative Durable Building and Infrastructure Research Center
Publisher(Ko) :건설구조물 내구성혁신 연구센터
Journal Title :International Journal of Sustainable Building Technology and Urban Development
Volume : 12
No :2
Pages :186-203
Received Date : 2021-05-21
Accepted Date : 2021-06-30
DOI :https://doi.org/10.22712/susb.20210015

International Journal of Sustainable Building Technology and Urban Development ISSN:2093-761X(Print) 2093-7628(Online)

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