پیش بینی حالت های حمل ونقل از نقاط خط سیر با استفاده از روش های تقویت کننده و یادگیری عمیق در حمل ونقل هوشمند

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی نقشه‌برداری و اطلاعات مکانی، دانشگاه تهران، تهران، ایران

2 دانشکده مهندسی معدن، دانشگاه صنعتی سهند، تبریز، ایران

چکیده

: امروزه با گسترش شهرنشینی نیاز به حمل‌ونقل هوشمند به منظور تسهیل رفت و آمد شهروندان بیش از پیش مورد توجه قرار گرفته است. شناسایی و پیش‌بینی استفاده از حالات حمل‌ونقلی یکی از اساسی‌ترین پیش‌نیازها برای راه‌اندازی و استفاده از خدمات حمل‌ونقل هوشمند به شمار می‌آید. با پیشرفت فناوری‌های مکانی، ابزار و تلفن‌های هوشمند، اطلاعات زیادی با استفاده از سیستمهای تعیین موقعیت ماهواره ای (GNSS) توسط بسیاری از دستگاه‌ها تولید می‌شود. در این پژوهش، چهار ویژگی نقطه‌ای، 56 ویژگی سفر و سه ویژگی پیشرفته استخراج شده، چهار مدل کلاسه‌بندی GB، XGBoost، LightGBM و CatBoost زیر مجموعه روش تقویت‌کننده (Boostig) پس از انتخاب ویژگی ترکیبی به همراه سه مدل کلاسه‌بندی CNN، LSTM و ConvLSTM زیر مجموعه روش یادگیری عمیق پیاده‌سازی و بررسی شده تا بتوان حالات حمل‌ونقلی شامل: پیاده‌روی، استفاده از دوچرخه، استفاده از اتوبوس، استفاده از اتومبیل و استفاده از قطار را با استفاده از مجموعه داده‌های GeoLife پیش‌بینی کنند. نتایج نشان داد مدل LightGBM با کسب دقت بالاتر (49/95درصد) و پیچیدگی زمانی کمتر، بهترین مدل نسبت به مدل‌های دیگر است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Identifying Transportation Modes from Trajectory Dataset using Boosting and Deep Learning Methods in Intelligent Transportation Systems

نویسندگان [English]

  • Sajad Sowlati 1
  • Rahim Ali Abbaspour 1
  • Alireza Chehreghan 2
1 School of Surveying and Geospatial Engineering, University of Tehran, Tehran, Iran.
2 Assistant Professor, Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran.
چکیده [English]

, with the expansion of urbanization, the need for intelligent transportation to facilitate the movement of citizens has received more attention than before. Identifying and predicting the use of transportation modes is one of the most basic prerequisites for setting up and using intelligent transportation services. With the advancement of location technologies, agents, and smartphones, a lot of information is generated by many devices using Global Navigation Satellite Systems (GNSS). In this study, 4 Point features and 59 travel features and advanced features were extracted, four classification models GB, XGBoost, LightGBM and CatBoost subset of Boosting method after hybrid feature selecting with 3 classification models CNN, LSTM, and ConvLSTM subset of Deep Learning method Implemented and analyzed to predict transportation modes including walking, using bicycles, using buses, using cars, and using trains usage the GeoLife dataset. Finally, the LightGBM model predicted transportation modes with higher accuracy (95.49%) and less time complexity than other models.

کلیدواژه‌ها [English]

  • Intelligent Transportation System (ITS)
  • Transportation Modes
  • Trajectory Data
  • Deep Learning
-Adler, J. L. and V. J. Blue, (1998), "Toward the design of intelligent traveler information systems", Transportation Research Part C: Emerging Technologies 6(3), pp.157-172.
-Bantis, T. and J. Haworth, (2017), "Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics", Transportation Research Part C: Emerging Technologies 80, pp.286-309.
-Bao, J., Y. Zheng, D. Wilkie and M. Mokbel, (2015), "Recommendations in location-based social networks: a survey", GeoInformatica 19(3), pp.525-565.
-Bedogni, L., M. Di Felice and L. Bononi, (2016), "Context‐aware Android applications through transportation mode detection techniques", Wireless communications and mobile computing 16(16), pp.2523-2541.
-Bengio, Y., P. Frasconi and P. Simard, (1993), "The problem of learning long-term dependencies in recurrent networks", IEEE international conference on neural networks, IEEE.
-Biljecki, F., H. Ledoux and P. Van Oosterom, (2013), "Transportation mode-based segmentation and classification of movement trajectories", International Journal of Geographical Information Science 27(2), pp.385-407.
-Bolbol, A., T. Cheng, I. Tsapakis and J. Haworth, (2012), "Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification", Computers, Environment and Urban Systems 36(6), pp.526-537.
-Caruana, R., S. Lawrence and L. Giles, (2001), "Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping", Advances in neural information processing systems, pp.402-408.
-Chamoso, P., A. González-Briones, S. Rodríguez and J. M. Corchado, (2018), "Tendencies of technologies and platforms in smart cities: a state-of-the-art review", Wireless Communications and Mobile Computing.
-Chatzimilioudis, G., A. Konstantinidis, C. Laoudias and D. Zeinalipour-Yazti, (2012), "Crowdsourcing with smartphones", IEEE Internet Computing 16(5), pp.36-44.
-Chen, T. and C. Guestrin, (2016), "Xgboost: A scalable tree boosting system", Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining.
-Chon, J. and H. Cha, (2011), "Lifemap: A smartphone-based context provider for location-based services", IEEE Pervasive Computing 10(2), pp.58-67.
-Chung, E.-H. and A. Shalaby, (2005), "A trip reconstruction tool for GPS-based personal travel surveys", Transportation Planning and Technology 28(5), pp.381-401.
-Cui, G., J. Luo and X. Wang, (2018), "Personalized travel route recommendation using collaborative filtering based on GPS trajectories", International journal of digital earth 11(3), pp.284-307.
-Dabiri, S. and K. Heaslip, (2018), "Inferring transportation modes from GPS trajectories using a convolutional neural network." Transportation research part C: emerging technologies 86, pp.360-371.
-Das, R. D. and S. Winter, (2016), "Detecting urban transport modes using a hybrid knowledge driven framework from GPS trajectory", ISPRS International Journal of Geo-Information 5(11), pp.207.
-Dorogush, A. V., V. Ershov and A. Gulin, (2018), "CatBoost: gradient boosting with categorical features support", arXiv preprint arXiv:1810.11363.
-Endo, Y., H. Toda, K. Nishida and A. Kawanobe, (2016), "Deep feature extraction from trajectories for transportation mode estimation", Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer.
-Friedman, J. H., (2001), "Greedy function approximation: a gradient boosting machine", Annals of statistics, pp.1189-1232.
-Ge, R., S. M. Kakade, R. Kidambi and P. Netrapalli, (2019), "The step decay schedule: A near optimal, geometrically decaying learning rate procedure for least squares", arXiv preprint arXiv:1904.12838.
-Gers, F. A., J. Schmidhuber and F. Cummins (1999), "Learning to forget: Continual prediction with LSTM".
-Guo, M., S. Liang, L. Zhao and P. Wang (2020), "Transportation Mode Recognition With Deep Forest Based on GPS Data", IEEE Access 8, pp.150891-150901.
-Han, J., M. Kamber and J. Pei, (2011), "Data mining concepts and techniques third edition", The Morgan Kaufmann Series in Data Management Systems 5(4), pp.83-124.
-Hochreiter, S., (1998), "The vanishing gradient problem during learning recurrent neural nets and problem solutions", International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 6(02),
pp.107-116.
-Jahangiri, A. and H. Rakha, (2014), "Developing a support vector machine (SVM) classifier for transportation mode identification by using mobile phone sensor data", Transportation Research Board 93rd Annual Meeting.
-Jarašūniene, A., (2007), "Research into intelligent transport systems (ITS) technologies and efficiency", Transport 22(2), pp.61-67.
-Jović, A., K. Brkić and N. Bogunović, (2015), "A review of feature selection methods with applications", 2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO), Ieee.
-Ke, G., Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye and T.-Y. Liu, (2017), "Lightgbm: A highly efficient gradient boosting decision tree", Advances in neural information processing systems 30,
pp. 3146-3154.
-Kingma, D. P. and J. Ba, (2014), "Adam: A method for stochastic optimization", arXiv preprint arXiv:1412.6980.
-Kitchin, R., (2014), "The real-time city? Big data and smart urbanism." GeoJournal 79(1): pp.1-14.
-Krizhevsky, A., I. Sutskever and G. E. Hinton, (2017), "ImageNet classification with deep convolutional neural networks", Communications of the ACM 60(6), pp.84-90.
-Langley, R. B., (1997), "Innovation: the GPS error budget", GPS world 8(3), pp.51-56.
-Li, J., X. Pei, X. Wang, D. Yao, Y. Zhang and Y. Yue, (2021), "Transportation mode identification with GPS trajectory data and GIS information", Tsinghua Science and Technology 26(4), pp.403-416.
-Lipton, Z. C., C. Elkan and B. Naryanaswamy (2014). Optimal thresholding of classifiers to maximize F1 measure. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer.
-Nawaz, A., H. Zhiqiu, W. Senzhang, Y. Hussain, I. Khan and Z. Khan, (2020), "Convolutional LSTM based transportation mode learning from raw GPS trajectories." IET Intelligent Transport Systems 14(6), pp.570-577.
-Novaković, J. D., A. Veljović, S. S. Ilić, Ž. Papić and T. Milica, (2017), "Evaluation of classification models in machine learning." Theory and Applications of Mathematics & Computer Science 7(1), pp.39–46.
-Nwankpa, C., W. Ijomah, A. Gachagan and S. Marshall, (2018), "Activation functions: Comparison of trends in practice and research for deep learning." arXiv preprint arXiv:1811.03378.
-Pan, G., G. Qi, Z. Wu, D. Zhang and S. Li, (2012), "Land-use classification using taxi GPS traces", IEEE Transactions on Intelligent Transportation Systems 14(1), pp.113-123.
-Payne, S., (2015), "Study on key performance indicators for intelligent transport systems: final report in support of the implementation of the EU Legislative Framework on ITS (Directive 2010/40/EU)­".
-Phithakkitnukoon, S., T. Horanont, G. Di Lorenzo, R. Shibasaki and C. Ratti, (2010), "Activity-aware map: Identifying human daily activity pattern using mobile phone data, International workshop on human behavior understanding, Springer.
-Prokhorenkova, L., G. Gusev, A. Vorobev, A. V. Dorogush and A. Gulin, (2017), "CatBoost: unbiased boosting with categorical features", arXiv preprint arXiv:1706.09516.
-Quessada, M. S., R. S. Pereira, W. Revejes, B. Sartori, E. N. Gottsfritz, D. D. Lieira, M. A. da Silva, G. P. Rocha Filho and R. I. Meneguette (2020), "ITSMEI: An intelligent transport system for monitoring traffic and event information", International Journal of Distributed Sensor Networks 16(10): 1550147720963751.
-Scheiner, J. and C. Holz-Rau, (2007), "Travel mode choice: affected by objective or subjective determinants?", Transportation 34(4), pp. 487-511.
-Scherer, D., A. Müller and S. Behnke, (2010), "Evaluation of pooling operations in convolutional architectures for object recognition", International conference on artificial neural networks, Springer.
-Shi, X., Z. Chen, H. Wang, D.-Y. Yeung, W. K. Wong and W.-c. Woo, (2015), "Convolutional LSTM network: A machine learning approach for precipitation nowcasting", arXiv preprint arXiv:1506.04214.
-Simonyan, K. and A. Zisserman, (2014), "Very deep convolutional networks for large-scale image recognition", arXiv preprint arXiv:1409.1556.
-Song, X., H. Kanasugi and R. Shibasaki, (2016), "Deeptransport: Prediction and simulation of human mobility and transportation mode at a citywide level", Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.
-Stenneth, L., O. Wolfson, P. S. Yu and B. Xu (2011), "Transportation mode detection using mobile phones and GIS information", Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems.
-Stopher, P., C. FitzGerald and J. Zhang, (2008), "Search for a global positioning system device to measure person travel", Transportation Research Part C: Emerging Technologies 16(3),  pp.350-369.
-Su, X., H. Caceres, H. Tong and Q. He, (2016), "Online travel mode identification using smartphones with battery saving considerations", IEEE Transactions on Intelligent Transportation Systems 17(10): pp.2921-2934.
-Tamane, S. C., N. Dey and A. E. Hassanien (2020), "Security and Privacy Applications for Smart City Development, Springer".
-Tang, L., Z. Kan, X. Zhang, X. Yang, F. Huang and Q. Li, (2016), "Travel time estimation at intersections based on low-frequency spatial-temporal GPS trajectory big data", Cartography and Geographic Information Science 43(5), pp.417-426.
-Tang, L., X. Yang, Z. Dong and Q. Li, (2016), "CLRIC: Collecting lane-based road information via crowdsourcing", IEEE Transactions on Intelligent Transportation Systems 17(9), pp.2552-2562.
-Venkatesh, B. and J. Anuradha, (2019),
"A review of feature selection and its methods", Cybernetics and Information Technologies 19(1), pp.3-26.
-Vu, T. H., L. Dung and J.-C. Wang, (2016), "Transportation mode detection on mobile devices using recurrent nets", Proceedings of the 24th ACM international conference on Multimedia.
-Wang, B., Y. Wang, K. Qin and Q. Xia (2018), "Detecting transportation modes based on LightGBM classifier from GPS trajectory data. 2018 26th International Conference on Geoinformatics, IEEE".
-Xiao, Z., Y. Wang, K. Fu and F. Wu, (2017), "Identifying different transportation modes from trajectory data using tree-based ensemble classifiers", ISPRS International Journal of Geo-Information 6(2), pp.57.
-Yang, X., L. Tang, L. Niu, X. Zhang and Q. Li, (2018), "Generating lane-based intersection maps from crowdsourcing big trace data", Transportation Research Part C: Emerging Technologies 89, pp.168-187.
-Zheng, Y., H. Fu, X. Xie, W. Ma and Q. Li, (2011), "Geolife GPS Trajectory Dataset-User Guide", Microsoft Research.
-Zheng, Y., Q. Li, Y. Chen, X. Xie and W.-Y. Ma, (2008), "Understanding mobility based on GPS data", Proceedings of the 10th international conference on Ubiquitous computing.
-Zheng, Y., L. Liu, L. Wang and X. Xie (2008), "Learning transportation mode from raw gps data for geographic applications on the web", Proceedings of the 17th international conference on World Wide Web.