Real-Time Travel Mode Detection with Smartphone Sensing and Machine Learning
Resumo
The detection of the travel modes used and, the prediction of trip purposes, through smartphone sensors data have emerged as two research challenges in recent years. Both of these problems have been deeply investigated in isolation, while the problem of inferring mode and purpose at the same time and, more specifically, using the same preprocessing algorithm has been less explored. Also, few studies presented solutions that can execute the detection of user travel modes in real-time, and even fewer have presented the evaluation of these solutions in a realistic manner. Meanwhile, some of the previous studies claim that off-the-shelf activity recognition solutions, do not perform well in the travel mode detection task, although many of them do not present a quantitative evidence of their bad performance. Thus, in this work, we propose three techniques for real-time travel mode detection, using different combinations of smartphone sensors, and one technique for join travel mode detection and trip purpose prediction using a single preprocessing algorithm, in real-time. We empirically evaluated the proposed techniques and an off-the-shelf activity recognition solution using field tests and cross-validation experiments with private and public mobility datasets.
Palavras-chave:
Travel Mode Detection, Mobile Sensing, Smart Mobility, Intelligent Trans- portation Systems, Machine Learning, Artificial Intelligence
Referências
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Bantis, T. and Haworth, J. (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, 80:286-309.
Batool, T., Vanrompay, Y., Neven, A., Janssens, D., and Wets, G. (2017). Ctass: a framework for contextualized travel behavior advice to cardiac patients. Procedia Computer Science, 113:303-309.
Bedogni, L., Di Felice, M., and Bononi, L. (2016). Context aware android applications through transportation mode detection techniques. Wireless Communications and Mobile Computing, 16(16).
Chen, K.-Y., Shah, R. C., Huang, J., and Nachman, L. (2017). Mago: Mode of transportinference using the hall-effect magnetic sensor and accelerometer. Proc. ACM Interact.Mob. Wearable Ubiquitous Technol., 1(2):8:1-8:23.
Dabiri, S. and Heaslip, K. (2018). Inferring transportation modes from gps trajectories using a convolutional neural network. Transportation Research Part C: Emerging Technologies, 86:360-371.
Ermagun, A., Fan, Y., Wolfson, J., Adomavicius, G., and Das, K. (2017). Real-time trippurpose prediction using online location-based search and discovery services. Transportation Research Part C: Emerging Technologies, 77:96-112.
Evenson, K. R. and Furberg, R. D. (2017). Moves app: a digital diary to track physical activity and location. Br J Sports Med, 51(15):1169-1170.
Fang, S.-H., Liao, H.-H., Fei, Y.-X., Chen, K.-H., Huang, J.-W., Lu, Y.-D., and Tsao, Y.(2016). Transportation modes classification using sensors on smartphones. Sensors,16(8):1324.
Gong, L., Kanamori, R., and Yamamoto, T. (2017). Data selection in machine learning for identifying trip purposes and travel modes from longitudinal gps data collectionlasting for seasons. Travel Behaviour and Society.
Mäenpää, H., Lobov, A., and Lastra, J. L. M. (2017). Travel mode estimation for multi-modal journey planner. Transportation Research Part C: Emerging Technologies,82:273-289.
Martin, B. D., Addona, V., Wolfson, J., Adomavicius, G., and Fan, Y. (2017). Methods for real-time prediction of the mode of travel using smartphone-based gps and accelerometer data. Sensors, 17(9):2058.
Meng, C., Cui, Y., He, Q., Su, L., and Gao, J. (2017). Travel purpose inference with gps trajectories, pois, and geo-tagged social media data. In Big Data (Big Data), 2017IEEE International Conference on, pages 1319-1324. IEEE.
Nitsche, P., Widhalm, P., Breuss, S., Brindle, N., and Maurer, P. (2014). Supporting large-scale travel surveys with smartphones-a practical approach. Transportation Research Part C: Emerging Technologies, 43:212-221.
Prelipcean, A. C., Gidófalvi, G., and Susilo, Y. O. (2017). Transportation mode detection— an in-depth review of applicability and reliability. Transport Reviews, 37(4):442—464.
Quintella, C. A. d. M. S., Andrade, L. C., and Campos, C. A. v. (2016). Detecting the transportation mode for context-aware systems using smartphones. In IEEE Intelligent Transportation Systems (ITSC).
Schobel, J., Pryss, R., Schlee, W., Probst, T., Gebhardt, D., Schickler, M., and Reichert,M. (2017). Development of mobile data collection applications by domain experts: Experimental results from a usability study. In International Conference on Advanced Information Systems Engineering, pages 60-75. Springer.
Seo, T., Kusakabe, T., Gotoh, H., and Asakura, Y. (2017). Interactive online machine learning approach for activity-travel survey. Transportation Research Part B: Methodological.
Shafique, M. A. and Hato, E. (2016). Travel mode detection with varying smartphone data collection frequencies. Sensors, 16(5).
Soares, E. F.d.S. (2019). Real-Time Travel Mode Detection with Smartphone Sensing and Machine Learning. Programa de Pósgraduação em Informática - PPGI, Universidade Federal do Estado do Rio de Janeiro - UNIRIO, 143 páginas.
Stopher, P. R., Jiang, Q., FitzGerald, C., et al. (2005). Processing gps data from travel surveys. 2nd international colloqium on the behavioural foundations of integrated land-use and transportation models: frameworks, models and applications, Toronto.
Su, X., Caceres, H., Tong, H., and He, Q. (2016). Online travel mode identification using smartphones with battery saving considerations. IEEE Transactions on Intelligent Transport. Systems, 17(10).
Wang, B., Gao, L., and Juan, Z. (2017). Travel mode detection using gps data and socioeconomic attributes based on a random forest classifier. IEEE Transactions on Intelligent Transportation Systems.
Xiao, G., Juan, Z., and Zhang, C. (2015). Travel mode detection based on gps track dataand bayesian networks. Computers, Environment and Urban Systems, 54:14-22.
Xiao, G., Juan, Z., and Zhang, C. (2016). Detecting trip purposes from smartphone-based travel surveys with artificial neural networks and particle swarm optimization. Transportation Research Part C: Emerging Technologies, 71:447-463.
Bantis, T. and Haworth, J. (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, 80:286-309.
Batool, T., Vanrompay, Y., Neven, A., Janssens, D., and Wets, G. (2017). Ctass: a framework for contextualized travel behavior advice to cardiac patients. Procedia Computer Science, 113:303-309.
Bedogni, L., Di Felice, M., and Bononi, L. (2016). Context aware android applications through transportation mode detection techniques. Wireless Communications and Mobile Computing, 16(16).
Chen, K.-Y., Shah, R. C., Huang, J., and Nachman, L. (2017). Mago: Mode of transportinference using the hall-effect magnetic sensor and accelerometer. Proc. ACM Interact.Mob. Wearable Ubiquitous Technol., 1(2):8:1-8:23.
Dabiri, S. and Heaslip, K. (2018). Inferring transportation modes from gps trajectories using a convolutional neural network. Transportation Research Part C: Emerging Technologies, 86:360-371.
Ermagun, A., Fan, Y., Wolfson, J., Adomavicius, G., and Das, K. (2017). Real-time trippurpose prediction using online location-based search and discovery services. Transportation Research Part C: Emerging Technologies, 77:96-112.
Evenson, K. R. and Furberg, R. D. (2017). Moves app: a digital diary to track physical activity and location. Br J Sports Med, 51(15):1169-1170.
Fang, S.-H., Liao, H.-H., Fei, Y.-X., Chen, K.-H., Huang, J.-W., Lu, Y.-D., and Tsao, Y.(2016). Transportation modes classification using sensors on smartphones. Sensors,16(8):1324.
Gong, L., Kanamori, R., and Yamamoto, T. (2017). Data selection in machine learning for identifying trip purposes and travel modes from longitudinal gps data collectionlasting for seasons. Travel Behaviour and Society.
Mäenpää, H., Lobov, A., and Lastra, J. L. M. (2017). Travel mode estimation for multi-modal journey planner. Transportation Research Part C: Emerging Technologies,82:273-289.
Martin, B. D., Addona, V., Wolfson, J., Adomavicius, G., and Fan, Y. (2017). Methods for real-time prediction of the mode of travel using smartphone-based gps and accelerometer data. Sensors, 17(9):2058.
Meng, C., Cui, Y., He, Q., Su, L., and Gao, J. (2017). Travel purpose inference with gps trajectories, pois, and geo-tagged social media data. In Big Data (Big Data), 2017IEEE International Conference on, pages 1319-1324. IEEE.
Nitsche, P., Widhalm, P., Breuss, S., Brindle, N., and Maurer, P. (2014). Supporting large-scale travel surveys with smartphones-a practical approach. Transportation Research Part C: Emerging Technologies, 43:212-221.
Prelipcean, A. C., Gidófalvi, G., and Susilo, Y. O. (2017). Transportation mode detection— an in-depth review of applicability and reliability. Transport Reviews, 37(4):442—464.
Quintella, C. A. d. M. S., Andrade, L. C., and Campos, C. A. v. (2016). Detecting the transportation mode for context-aware systems using smartphones. In IEEE Intelligent Transportation Systems (ITSC).
Schobel, J., Pryss, R., Schlee, W., Probst, T., Gebhardt, D., Schickler, M., and Reichert,M. (2017). Development of mobile data collection applications by domain experts: Experimental results from a usability study. In International Conference on Advanced Information Systems Engineering, pages 60-75. Springer.
Seo, T., Kusakabe, T., Gotoh, H., and Asakura, Y. (2017). Interactive online machine learning approach for activity-travel survey. Transportation Research Part B: Methodological.
Shafique, M. A. and Hato, E. (2016). Travel mode detection with varying smartphone data collection frequencies. Sensors, 16(5).
Soares, E. F.d.S. (2019). Real-Time Travel Mode Detection with Smartphone Sensing and Machine Learning. Programa de Pósgraduação em Informática - PPGI, Universidade Federal do Estado do Rio de Janeiro - UNIRIO, 143 páginas.
Stopher, P. R., Jiang, Q., FitzGerald, C., et al. (2005). Processing gps data from travel surveys. 2nd international colloqium on the behavioural foundations of integrated land-use and transportation models: frameworks, models and applications, Toronto.
Su, X., Caceres, H., Tong, H., and He, Q. (2016). Online travel mode identification using smartphones with battery saving considerations. IEEE Transactions on Intelligent Transport. Systems, 17(10).
Wang, B., Gao, L., and Juan, Z. (2017). Travel mode detection using gps data and socioeconomic attributes based on a random forest classifier. IEEE Transactions on Intelligent Transportation Systems.
Xiao, G., Juan, Z., and Zhang, C. (2015). Travel mode detection based on gps track dataand bayesian networks. Computers, Environment and Urban Systems, 54:14-22.
Xiao, G., Juan, Z., and Zhang, C. (2016). Detecting trip purposes from smartphone-based travel surveys with artificial neural networks and particle swarm optimization. Transportation Research Part C: Emerging Technologies, 71:447-463.
Publicado
07/12/2020
Como Citar
SOARES, Elton F. de S.; V. CAMPOS, Carlos Alberto; DE LUCENA, Sidney Cunha.
Real-Time Travel Mode Detection with Smartphone Sensing and Machine Learning. In: CONCURSO DE TESES E DISSERTAÇÕES - SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 38. , 2020, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 73-80.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc_estendido.2020.12404.
