[
1. Andrew, Alex M. (2001) An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Kybernetes.
]Search in Google Scholar
[
2. Asci, Guven, and Guvensan, M. Amac. (2019) A Novel Input Set for LSTM-Based Transport Mode Detection. In: 2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019, 107–12. DOI: 10.1109/PERCOMW.2019.8730799.
]DOI öffnenSearch in Google Scholar
[
3. Axhausen, Kay W., Schönfelder, Stefan, Wolf, Jean, Oliveira, Marcelo and Samaga, Ute. (2003) 80 Weeks of GPS-Traces: Approaches to Enriching the Trip Information. Arbeitsberichte Verkehrs-Und Raumplanung, 178.
]Search in Google Scholar
[
4. Breiman, L., Friedman, J., Olshen, R. and C. Stone. (1984) Classification and Regression Trees–Crc Press. Boca Raton, Florida.
]Search in Google Scholar
[
5. Carpineti, C., Lomonaco, V., Bedogni, L., di Felice, M. and Bononi, L. (2018) Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity. In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018, 367–72. DOI: 10.1109/PERCOMW.2018.8480119.
]DOI öffnenSearch in Google Scholar
[
6. Chia, Jason, Lee, Jinwoo and Kamruzzaman, M. D. (2016) Walking to Public Transit: Exploring Variations by Socioeconomic Status, 10(9), 805–14. DOI: 10.1080/15568318.2016.1156792.
]DOI öffnenSearch in Google Scholar
[
7. Dabiri, Sina, and Heaslip, Kevin. (2018) Inferring Transportation Modes from GPS Trajectories Using a Convolutional Neural Network. Transportation Research Part C: Emerging Technologies, 86, 360–371. DOI: 10.1016/J.TRC.2017.11.021.
]DOI öffnenSearch in Google Scholar
[
8. Devaul, Richard W., and Dunn, Steve. (2001) Real-Time Motion Classification for Wearable Computing Applications.
]Search in Google Scholar
[
9. Efthymiou, Alexandros, Barmpounakis, Emmanouil N., Efthymiou, Dimitrios and Vlahogianni, Eleni I. (2019) Transportation Mode Detection from Low-Power Smartphone Sensors Using Tree-Based Ensembles. Journal of Big Data Analytics in Transportation 2019, 1(1), 57–69. DOI: 10.1007/S42421-019-00004-W.
]DOI öffnenSearch in Google Scholar
[
10. El-Geneidy, Ahmed, Grimsrud, Michael, Wasfi, Rania, Tétreault, Paul and Surprenant-Legault, Julien. (2014) New Evidence on Walking Distances to Transit Stops: Identifying Redundancies and Gaps Using Variable Service Areas. Transportation, 41(1), 193–210. DOI: 10.1007/S11116-013-9508-Z/FIGURES/4.
]DOI öffnenSearch in Google Scholar
[
11. Erkan, Uğur, Gökrem, Levent and Enginoğlu, Serdar. (2018) Different Applied Median Filter in Salt and Pepper Noise. Computers & Electrical Engineering, 70, 789–798. DOI: 10.1016/J.COMPELECENG.2018.01.019.
]DOI öffnenSearch in Google Scholar
[
12. Ferreira, Paulo, Zavgorodnii, Constantin and Veiga, Luís. (2020) EdgeTrans - Edge Transport Mode Detection. Pervasive and Mobile Computing, 69, 101268. DOI: 10.1016/J.PMCJ.2020.101268.
]DOI öffnenSearch in Google Scholar
[
13. He, Jinliao, Zhang, Ruozhu, Huang, Xianjin and Xi, Guangliang. (2018) Walking Access Distance of Metro Passengers and Relationship with Demographic Characteristics: A Case Study of Nanjing Metro. Chinese Geographical Science 2018, 28(4), 612–623. DOI: 10.1007/S11769-018-0970-6.
]DOI öffnenSearch in Google Scholar
[
14. Ho, Tin Kam. (1995) Random Decision Forests. In: Proceedings of 3rd international conference on document analysis and recognition, 1, 278–282.
]Search in Google Scholar
[
15. Ioffe, Sergey, and Szegedy, Christian. (2015) Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In: International conference on machine learning, 448–56. PMLR.
]Search in Google Scholar
[
16. Jiang, Xiang, de Souza, Erico N., Pesaranghader, Ahmad, Hu, Baifan, Silver, Daniel L. and Matwin, Stan. (2017) TrajectoryNet: An Embedded GPS Trajectory Representation for Point-Based Classification Using Recurrent Neural Networks’. Proceedings of the 27th Annual International Conference on Computer Science and Software Engineering, CASCON 2017, 9, 192–200. DOI: 10.48550/arxiv.1705.02636.
]DOI öffnenSearch in Google Scholar
[
17. Khan, Inayat, Khusro, Shah, Ali, Shaukat and Ahmad, Jamil. (2016) Sensors Are Power Hungry: An Investigation of Smartphone Sensors Impact on Battery Power from Lifelogging Perspective’. Bahria University Journal of Information & Communication Technologies (BUJICT), 9(2).
]Search in Google Scholar
[
18. Liang, Xiaoyuan, and Wang, Guiling. (2017) A Convolutional Neural Network for Transportation Mode Detection Based on Smartphone Platform. Proceedings – 14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017, 338–342. DOI: 10.1109/MASS.2017.81.
]DOI öffnenSearch in Google Scholar
[
19. Lin, Guifang, and Shen, Wei. (2018) Research on Convolutional Neural Network Based on Improved Relu Piecewise Activation Function. Procedia Computer Science, 131, 977–984. DOI: 10.1016/J.PROCS.2018.04.239.
]DOI öffnenSearch in Google Scholar
[
20. Lorintiu, Oana, and Vassilev, Andrea. (2016) Transportation Mode Recognition Based on Smartphone Embedded Sensors for Carbon Footprint Estimation. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 0, 1976–1981. DOI: 10.1109/ITSC.2016.7795875.
]DOI öffnenSearch in Google Scholar
[
21. Manzoni, Vincenzo, Maniloff, Diego, Kloeckl, Kristian and Ratti, Carlo. (2010) Transportation Mode Identification and Real-Time CO2 Emission Estimation Using Smartphones. SENSEable City Lab, Massachusetts Institute of Technology, Nd.
]Search in Google Scholar
[
22. Mucherino, Antonio, Papajorgji, Petraq J. and Pardalos, Panos M. (2009) K-Nearest Neighbor Classification. Data mining in agriculture, 83–106. Springer.10.1007/978-0-387-88615-2_4
]Search in Google Scholar
[
23. Muller, Ian Anderson Henk. (2006) Practical Activity Recognition Using GSM Data. In: Proceedings of the 5th International Semantic Web Conference (ISWC). Athens, 1. Citeseer.
]Search in Google Scholar
[
24. Nham, Ben, Siangliulue, Kanya and Yeung, Serena. (2008) Predicting Mode of Transport from Iphone Accelerometer Data. Machine Learning Final Projects, Stanford University.
]Search in Google Scholar
[
25. Nygaard, Magnus Frestad. (2016) Waiting Time Strategy for Public Transport Passengers. 61.
]Search in Google Scholar
[
26. Priscoli, Francesco Delli, Giuseppi, Alessandro and Lisi, Federico. (2020) Automatic Transportation Mode Recognition on Smartphone Data Based on Deep Neural Networks. Sensors 2020, 20(24), 7228. DOI: 10.3390/S20247228.
]DOI öffnenSearch in Google Scholar
[
27. Quintella, Carlos Alvaro De M. S., Andrade, Leila C. V. and Campos, Carlos Alberto v. (2016) Detecting the Transportation Mode for Context-Aware Systems Using Smartphones. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2261–2266. DOI: 10.1109/ITSC.2016.7795921.
]DOI öffnenSearch in Google Scholar
[
28. Reddy, Sasank, Mun, Min, Burke, Jeff, Estrin, Deborah, Hansen, Mark and Srivastava, Mani. (2010) Using Mobile Phones to Determine Transportation Modes. ACM Transactions on Sensor Networks (TOSN), 6(2). DOI: 10.1145/1689239.1689243.
]DOI öffnenSearch in Google Scholar
[
29. Scherer, Dominik, Müller, Andreas and Behnke, Sven. (2010) Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition. In: International conference on artificial neural networks, 92–101. Springer.10.1007/978-3-642-15825-4_10
]Search in Google Scholar
[
30. Sokolova, Marina, and Lapalme, Guy. (2009) A Systematic Analysis of Performance Measures for Classification Tasks. Information Processing & Management, 45(4), 427–37.10.1016/j.ipm.2009.03.002
]Search in Google Scholar
[
31. Srivastava, Nitish, Hinton, Geoffrey, Krizhevsky, Alex, Sutskever, Ilya and Salakhutdinov, Ruslan. (2014) Dropout: A Simple Way to Prevent Neural Networks from Overfitting. The Journal of Machine Learning Research, 15(1), 1929–1958.
]Search in Google Scholar
[
32. Tennøy, Aud, Knapskog, Marianne and Wolday, Fitwi. (2022) Walking Distances to Public Transport in Smaller and Larger Norwegian Cities. Transportation Research Part D: Transport and Environment, 103, 103169. DOI: 10.1016/J.TRD.2022.103169.
]DOI öffnenSearch in Google Scholar
[
33. Voss, Christine, Winters, Meghan, Frazer, Amanda and McKay, Heather. (2015) School-Travel by Public Transit: Rethinking Active Transportation. Preventive Medicine Reports, 2, 65–70. DOI: 10.1016/J.PMEDR.2015.01.004.471683526793430
]DOI öffnenSearch in Google Scholar
[
34. Wang, Shuangquan, Chen, Canfeng and Ma, Jian. (2010) Accelerometer Based Transportation Mode Recognition on Mobile Phones. APWCS 2010 - 2010 Asia-Pacific Conference on Wearable Computing Systems, 44–46. DOI: 10.1109/APWCS.2010.18.
]DOI öffnenSearch in Google Scholar
[
35. Zhao, Hong, Hou, Chunning, Alrobassy, Hala and Zeng, Xiangyan. (2019) Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network. Journal of Computer Networks and Communications. doi: 10.1155/2019/4967261.
]DOI öffnenSearch in Google Scholar
[
36. Zheng, Yu, Li, Quannan, Chen, Yukun, Xie, Xing, and Wei Ying Ma. (2008) Understanding Mobility Based on GPS Data. UbiComp 2008 - Proceedings of the 10th International Conference on Ubiquitous Computing, 312–321. DOI: 10.1145/1409635.1409677.
]DOI öffnenSearch in Google Scholar
[
37. Zuo, Ting, Wei, Heng and Rohne, Andrew. (2018) Determining Transit Service Coverage by Non-Motorized Accessibility to Transit: Case Study of Applying GPS Data in Cincinnati Metropolitan Area. Journal of Transport Geography, 67, 1–11. DOI: 10.1016/J.JTRANGEO.2018.01.002.
]DOI öffnenSearch in Google Scholar