Browse > Article
http://dx.doi.org/10.9708/jksci.2022.27.08.061

Pedestrian GPS Trajectory Prediction Deep Learning Model and Method  

Yoon, Seung-Won (Dept. of Computer Science, Chung-nam National University)
Lee, Won-Hee (Dept. of Computer Science, Chung-nam National University)
Lee, Kyu-Chul (Dept. of Computer Science, Chung-nam National University)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.8, 2022 , pp. 61-68 More about this Journal
Abstract
In this paper, we propose a system to predict the GPS trajectory of a pedestrian based on a deep learning model. Pedestrian trajectory prediction is a study that can prevent pedestrian danger and collision situations through notifications, and has an impact on business such as various marketing. In addition, it can be used not only for pedestrians but also for path prediction of unmanned transportation, which is receiving a lot of spotlight. Among various trajectory prediction methods, this paper is a study of trajectory prediction using GPS data. It is a deep learning model-based study that predicts the next route by learning the GPS trajectory of pedestrians, which is time series data. In this paper, we presented a data set construction method that allows the deep learning model to learn the GPS route of pedestrians, and proposes a trajectory prediction deep learning model that does not have large restrictions on the prediction range. The parameters suitable for the trajectory prediction deep learning model of this study are presented, and the model's test performance are presented.
Keywords
Trajectory Prediction; GPS; Deep Learning Model; Pedestrian; Machine Learning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Varshneya, Daksh, and G. Srinivasaraghavan. "Human trajectory prediction using spatially aware deep attention models." arXiv preprint arXiv:1705.09436, May 2017. DOI: 10.48550/arXiv.1705.09436   DOI
2 Endo, Yuki, et al. "Predicting destinations from partial trajectories using recurrent neural network." Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham, April 2017. DOI: 10.1007/978-3-319-57454-7_13   DOI
3 Duives, Dorine C., Guangxing Wang, and Jiwon Kim. "Forecasting pedestrian movements using recurrent neural networks: An application of crowd monitoring data." Sensors19.2 : 382, January 2019. DOI: 10.3390/s19020382   DOI
4 Hassan, Md Rafiul, and Baikunth Nath. "Stock market forecasting using hidden Markov model: a new approach." 5th International Conference on Intelligent Systems Design and Applications (ISDA'05). IEEE, September 2005. DOI: 10.1109/isda.2005.85   DOI
5 Shaheen, Hera, Shikha Agarwal, and Prabhat Ranjan. "MinMaxScaler binary PSO for feature selection." First international conference on sustainable technologies for computational intelligence. Springer, Singapore, November 2020. DOI: 10.1007/978-981-15-0029-9_55   DOI
6 Cho, Kyunghyun, et al. "Learning phrase representations using RNN encoder-decoder for statistical machine translation." arXiv preprint arXiv:1406.1078, September 2014. DOI: 10.48550/arXiv.1406.1078   DOI
7 Giuliari, Francesco, et al. "Transformer networks for trajectory forecasting." 2020 25th international conference on pattern recognition (ICPR). IEEE, January 2021. DOI: 10.1109/ICPR48806.2021.9412190   DOI
8 LI, Zhe, et al. Grid map construction and terrain prediction for quadruped robot based on c-terrain path. IEEE Access, March 2020. DOI: 10.1109/ACCESS.2020.2977396   DOI
9 McLeod, Allan I., and William K. Li. "Diagnostic checking ARMA time series models using squared-residual autocorrelations." Journal of time series analysis 4.4: 269-273, July 1983. DOI:10.1111/j.1467-9892.1983.tb00373.x   DOI
10 Gun-Tae Son, Jeong-Hwa Jeong, and Min-Wook Park. "Study of Hidden Markov Model for Speech Recognition1." CSAM (Communications for Statistical Applications and Methods) 5.1 (1998): 155-165.
11 ANDLE, Joshua J., et al. The Stanford Drone Dataset is More Complex than We Think: An Analysis of Key Characteristics. IEEE Transactions on Intelligent Vehicles, April 2022. DOI: 10.1109/TIV.2022.3166642   DOI
12 Eddy, Sean R. "Accelerated profile HMM searches." PLoS computational biology 7.10: e1002195, October 2011. DOI: 10.1371/journal.pcbi.1002195   DOI
13 Piccolo, Domenico. "A distance measure for classifying ARIMA models." Journal of time series analysis 11.2: 153-164, March 1990. DOI: 10.1111/j.1467-9892.1990.tb00048.x   DOI
14 Geyer, Charles J. "Practical markov chain monte carlo." Statistical science: 473-483, November 1992. DOI: 10.1214/ss/1177011137   DOI
15 Cherkassky, Vladimir, and Yunqian Ma. "Practical selection of SVM parameters and noise estimation for SVM regression." Neural networks 17.1: 113-126, January 2004. DOI: 10.1016/S0893-6080(03)00169-2   DOI
16 Ouyang, Wanli, and Xiaogang Wang. "Single-pedestrian detection aided by multi-pedestrian detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 2013. DOI: 10.1109/cvpr.2013.411   DOI
17 Z. Yang, M. Sun, H. Ye, Z. Xiong, G. Zussman and Z. Kostic, "Bird's-eye View Social Distancing Analysis System," 2022 IEEE International Conference on Communications Workshops (ICC Workshops), May 2022, pp. 427-432, doi: 10.1109/ICCWorkshops53468.2022.9814627.   DOI
18 Qian, Huajie, et al. "RobustScaler: QoS-Aware Autoscaling for Complex Workloads." arXiv preprint arXiv:2204.07197, April 2022. DOI: 10.48550/arXiv.2204.07197   DOI
19 Hodson, Timothy O. "Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not." Geoscientific Model Development 15.14, July 2022. DOI: 10.5194/gmd-15-5481-2022   DOI
20 Yu, Yong, et al. "A review of recurrent neural networks: LSTM cells and network architectures." Neural computation 31.7: 1235-1270, July 2019. DOI: 10.1162/neco_a_01199   DOI