Spatial Semantic Analysis and Origin-Destination Prediction Based on Extensive GPS Trajectory in Jakarta
https://doi.org/10.26594/register.v11i2.5388
Keywords:
Annotates GPS Trajectory, Spatial Semantic Analysis, Spatial Temporal Clustering, Origin Destination Prediction, Spatial Temporal RegressionAbstract
The rapid growth of mobility data from GPS trajectories offers unprecedented opportunities to gain deep insights into human mobility behavior, with significant implications for urban planning, traffic management, public transportation optimization, emergency response, and smart city development. However, a key challenge lies in transforming raw GPS trajectory data, consisting of sequences of coordinates and timestamps, into meaningful, context-rich information that can support analysis and decision making. This study proposes a semi-supervised framework to enhance the contextual and semantic understanding of journeys, using Grab Jakarta GPS trajectory data as a case study. The framework involves extracting origin-destination pairs, augmenting the data with temporal (day, time) and spatial (postal code, land use) contexts through public datasets, assigning cluster labels to characterize groups of journeys, analyzing mobility patterns, and ultimately predicting trip destinations. Origin-destination clustering, performed using the DBSCAN algorithm, identified five meaningful clusters, achieving the highest silhouette score of 0.56 with epsilon = 7.0 and min_samples = 5. Subsequently, a regression-based prediction model was developed, employing nine algorithms, including three deep learning approaches. The LSTM model demonstrated the best performance, yielding a mean squared error of 0.0053 and a coefficient of determination (R²) of 86.20% in predicting trip destinations. These findings highlight the potential of integrating spatial-temporal enrichment and machine learning to derive actionable insights from GPS trajectory data.
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[1] P. Jittrapirom, V. Caiati, A.-M. Feneri, S. Ebrahimigharehbaghi, M. J. A. González and J. Narayan, "Mobility as a Service: A Critical Review of Definitions, Assessments of Schemes, and Key Challenges," Urban Planning, vol. 2, no. 2, pp. 13-25, 29 June 2017.
[2] V. Bogorny, C. Renso, A. R. de Aquino, F. d. L. Siqueira and L. O. Alvares, "CONSTAnT – A Conceptual Data Model for Semantic Trajectories of Moving Objects," Transaction in GIS, vol. 18, no. 1, p. 66–88, February 2014.
[3] M. Bevis, J. Bedford and D. J. Caccamise II, "The Art and Science of Trajectory Modelling," Geodetic Time Series Analysis in Earth Sciences, pp. 1-27, August 2020.
[4] H. Noureddine, C. Ray and C. Claramunt, "Semantic Trajectory Modelling in Indoor and Outdoor Spaces," in 21st IEEE International Conference on Mobile Data Management (MDM), France, 2020.
[5] A. Guttman, "R-trees: A dynamic index structure for spatial searching," in 1984 ACM SIGMOD international conference on Management of data, 1984.
[6] S. Shang, R. Ding, B. Yuan, K. Xie, K. Zheng and P. Kalnis, "User oriented trajectory search for trip recommendation," in 15th international conference on extending database technology, 2012.
[7] N. Andrienko, G. Andrienko, N. Pelekis and S. Spaccapietra, "Basic Concepts of Movement Data," in Mobility, Data Mining, and Privacy Geographic Knowledge Discovery, Springer Berlin Heidelberg, 2008, p. 15–38.
[8] D. Kumar, H. Wu, Y. Lu, S. Krishnaswamy and M. Palaniswami, "Understanding Urban Mobility via Taxi Trip Clustering," in 17th IEEE International Conference on Mobile Data Management (MDM), 2016.
[9] D. Zhang, K. Lee and I. Lee, "Mining hierarchical semantic periodic patterns from GPS-collected spatio-temporal trajectories," Expert Systems with Applications, vol. 122, pp. 85-101, 15 May 2019.
[10] S. Wang, . G. Mei and S. Cuomo, "A generic paradigm for mining human mobility patterns based on the GPS trajectory data using complex network analysis," Concurrency and Computation: Practice and Experience, vol. 33, no. 4, 25 February 2021.
[11] S. Dutta, A. Das and B. K. Patra, "CLUSTMOSA: Clustering for GPS trajectory data based on multi-objective simulated annealing to develop mobility application," Applied Soft Computing, vol. 130, no. 109655, November 2022.
[12] S. Wang, X. Niu, P. Fournier-Viger, D. Zhou and F. Min, "A graph based approach for mining significant places in trajectory data," Information Sciences, vol. 609, pp. 172-194, September 2022.
[13] W. Li, H. Zhang, R. Shibasaki, J. Chen and H. H. Kobayashi, "Chapter two - Mining individual significant places from historical trajectory data," Handbook of Mobility Data Mining, vol. 2, pp. 15-26, 2023.
[14] J. Wang, W. Jiang and J. Jiang, "LibCity-Dataset: A Standardized and Comprehensive Dataset for Urban Spatial-temporal Data Mining," Intelligent Transportation Infrastructure, liad021, 7 November 2023.
[15] W. Tu, H. Ye, K. Mai, M. Zhou, J. Jiang, T. Zhao, S. Yi and Q. Li, "Deep online recommendations for connected E-taxis by coupling trajectory mining and reinforcement learning," International Journal of Geographical Information Science, vol. 38, no. 2, pp. 216-242, 2024.
[16] C. Chu, H. Zhang, P. Wang and F. Lu, "Simulating human mobility with a trajectory generation framework based on diffusion mode," International Journal of Geographical Information Science, vol. 38, no. 5, 06 February 2024.
[17] L. Gong, S. Guo, Y. Lin, Y. Liu, E. Zheng and Y. Shuang, "STCDM: Spatio-Temporal Contrastive Diffusion Model for Check-In Sequence Generation," IEEE Transactions on Knowledge and Data Engineering, pp. 1-14, 10 January 2025.
[18] A. Crivellari and Y. Shi, "Generative adversarial deep learning model for producing location-based synthetic trajectory data," Connection Science, vol. 37, no. 1, p. 2458502, 30 January 2025.
[19] Z. Xu, Y. Yin, C. Dai, X. Huang, R. Kudali, J. Foflia, G. Wang and R. Zimmermann, "Grab-Posisi-L: A Labelled GPS Trajectory Dataset for Map Matching in Southeast Asia," in Proceedings of the 28th International Conference on Advances in Geographic Information Systems, 2020.
[20] W. Bian, G. Cui and X. Wang, "A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories," Sensors, vol. 20, no. 7, 6 April 2020.
[21] M. Liu, L. Zhang, J. Ge, Y. Long and W. Che, "Map Matching for Urban High-Sampling-Frequency GPS Trajectories," ISPRS International Journal of Geo-Information, vol. 9, no. 1, p. 31, 5 January 2020.
[22] M. Dogramadzi and A. Khan, "Accelerated Map Matching for GPS Trajectories," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 5, pp. 4593-4602, May 2022.
[23] L. Ruback, M. A. Casanova, A. Raffaetà, C. Renso and V. Vidal, "Enriching Mobility Data with Linked Open Data," in Proceedings of the 20th International Database Engineering & Applications Symposium, 2016.
[24] Y. Gao, L. Huang, J. Feng and X. Wang, "Semantic trajectory segmentation based on change-point detection and ontology," International Journal of Geographical Information Science, vol. 34, no. 12, pp. 2361-2394, 2020.
[25] Z. Yan, D. Chakraborty, C. Parent, S. Spaccapietra and K. Aberer, "Semantic trajectories: Mobility data computation and annotation," ACM Transactions on Intelligent Systems and Technology, vol. 4, no. 3, p. 1–38, 01 July 2013.
[26] S. Hwang, C. Evans and T. Hanke, "Detecting Stop Episodes from GPS Trajectories with Gaps," in Seeing Cities Through Big Data: Research, Methods and Applications in Urban Informatics, Springer Nature, 2017, p. 427–439.
[27] H. Simanjuntak and F. Ciravegna, "Semantic Understanding of Human Mobility Lifestyle to Support Crisis Management," in Proceedings of the 16th International Association for Information Systems for Crisis Response and Management (ISCRAM) Conference, 2019.
[28] D. Guo, X. Zhu, H. Jin, P. Gao and C. Andris, "Discovering Spatial Patterns in Origin-Destination Mobility Data," Transaction in GIS, vol. 16, no. 3, pp. 411-429, June 2012.
[29] Y. Liang, Z. Zhao and X. Zhang , "Modeling taxi cruising time based on multi-source data: a case study in Shanghai," Transportation, vol. 51, p. 761–790, 2024.
[30] R. F. Jonaghani, M. Wachowicz and T. Hanson, "Matrix Factorization for Globally Consistent Periodic Flow Prediction in Taxi Systems," Transportation Research Record: Journal of the Transportation Research Board, vol. 2678, no. 5, pp. 1-12, May 2024.
[31] A. Monreale, F. Pinelli, R. Trasarti and F. Giannotti, "WhereNext: a location predictor on trajectory pattern mining," in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009.
[32] J. Zhang, Y. Zheng and D. Qi, "Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction," in Proceedings of the AAAI Conference on Artificial Intelligence, 2017.
[33] X. Zhang, Z. Zhao, Y. Zheng and J. Li, "Prediction of Taxi Destinations Using a Novel Data Embedding Method and Ensemble Learning," IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 1, pp. 68-78, 1 January 2020.
[34] J. Zhao, L. Zhang, J. Ye and C. Xu, "MDLF: A Multi-View-Based Deep Learning Framework for Individual Trip Destination Prediction in Public Transportation Systems," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 13316-13329, 2022.
[35] M.-F. Chiang, E.-P. Lim and J.-W. Low, "On Mining Lifestyles from User Trip Data," in Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2015.
[36] X. Guangnian, Z. Juan and C. Zhang, "Detecting trip purposes from smartphone-based travel surveys with artificial neural networks and particle swarm optimization," Transportation Research Part C: Emerging Technologies, vol. 71, pp. 447-463, October 2016.
[37] A. Ermagun, Y. Fan, J. Wolfson, G. Adomavicius and K. Das, "Real-time trip purpose prediction using online location-based search and discovery services," Transportation Research Part C: Emerging Technologies, vol. 77, pp. 96-112, April 2017.
[38] X. Huang, Y. Yin , S. Lim, G. Wang, B. Hu, J. Varadarajan, S. Zheng, A. Bulusu and R. Zimmermann, "Grab-Posisi: An Extensive Real-Life GPS Trajectory Dataset in Southeast Asia," in Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, 2019.
[39] F. Martínez-Plumed, L. Contreras-Ochando, C. Ferri, J. Hernández-Orallo, M. Kull, N. Lachiche, M. J. Ramírez-Quintana and P. Flach, "CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories," IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 8, pp. 3048 - 3061, 01 August 2021.
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