Smarttour: A Hybrid Rule-Machine Learning Based Framework With an Adaptive Refinement Loop For Tourist Behaviour Analysis and Classification
DOI:
https://doi.org/10.63503/acset.87Keywords:
urban mobility, tourist classification, behaviour analytics, smart cities, rule-based learning, machine learningAbstract
SmartTour is a modular and scalable framework aimed at analyzing and classifying travel behaviour patterns for tourists and population using data relating to mobility. This includes traveling by different modes such as pedestrians, vehicles, etc. The data collected by the SmartTour framework is used for feature extraction concerning travel behaviour, which is then classified using rules and other intelligent techniques such as machine learning. This also includes the extraction of meaningful features such as zone diversity, weekend travel behaviour, temporal entropy, etc. For improving robustness and generalization, SmartTour includes a feedback-based refinement technique that detects discrepancies between rule-based classifiers and machine learning approaches. These inconsistencies contribute towards enhanced classification rules. The system effectively facilitates analytical information such as peak travel time, most popular routes, and tourist-dense regions with the help of interactive visualizations. Therefore, SmartTour proves to be an interpretable, adaptive, and extensible system for urban mobility analysis with its potential applications for smart cities and tourism management.
References
[1] Y. Tang, J. He, and Z. Zhao, “Activity-Aware Human Mobility Prediction With Hierarchical Graph Attention Recurrent Network,” IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 2, Feb. 2025.
[2] P. Jahanmanesh, A. Farhadi, and A. Zamanifar, “Mobility Management With AI,” IEEE Access, vol. 13, pp. 1–25, 2025, doi: 10.1109/AC CESS.2025.3566337.
[3] J. Xu and Q. Ma, “Tourist Attractions Recommendation by Using Enhanced Location Knowledge Graph,” IEEE Access, vol. 13, 2025, doi: 10.1109/ACCESS.2025.3613788.
[4] H. Yang, C. Yan, Z. Chen, and P. Wang, “A K-Shape Clustering Based Transformer-Decoder Model for Predicting Multi-Step Potentials of Urban Mobility Field,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 8, pp. 10298–10312, Aug. 2024.
[5] M. Chen, Q. Yuan, C. Yang, and Y. Zhang, “Decoding Urban Mobility: Application of Natural Language Processing and Machine Learning to Activity Pattern Recognition, Prediction, and Temporal Transferability Examination,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 7, pp. 7151–7173, July 2024.
[6] Y. Chen, N. Xie, H. Xu, X. Chen, and D. Lee, “A Multi-Context Aware Human Mobility Prediction Model Based on Motif-Preserving Travel Preference Learning,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 2, pp. 2139–2152, Feb. 2024.
[7] R. Ke, K. Wang, Q. Liu, L. Liu, and P. Wang, “Uncovering Urban Tourist Mobility Patterns Based on Large-Scale Mobile Phone Data,” Journal of Transport Geography, vol. 130, Art. 104496, Jan. 2026.
[8] L. L´opez-Naranjo et al., “Artificial Intelligence in the Tourism Business: A Systematic Review,” Frontiers in Artificial Intelligence, vol. 8, Art. 1599391, 2025.
[9] “Machine Learning in Travel Mode Choice Studies: A Systematic Literature Review of Applications, Methods, and Challenges,” Results in Engineering, vol. 21, 2025.
[10] L. Zientara et al., “What Drives Tourists’ Sustainable Mobility at City Destinations? Insights from Ten European Capital Cities,” Journal of Destination Marketing and Management, vol. 33, 2024.
[11] Y.-Z. Liu, H. M. Nguyen, and M. T. Nguyen, “Electric Vehicles and Urban Tourism in Smart Cities: A Bibliometric Review of Sustainable Mobility Trends,” World Electric Vehicle Journal, vol. 16, no. 10, 2025.
[12] “Understanding Tourists’ Behaviour Toward Transport Mode Choice by Using Machine Learning Methods,” European Journal on Decision Processes, vol. 12, 2024.
[13] “A Systematic Review of Mode Choice Behaviour in Urban Transportation,” Discover Cities, 2025.
[14] “Promoting Sustainable Urban Tourism Through GIS Route Optimization and Modeling Exclusive Pedestrian Networks,” Scientific Reports, 2024.
[15] E. Denteh et al., “Integrating Travel Behaviour Forecasting and Generative Modeling for Predicting Future Urban Mobility,” arXiv preprint, 2025.
[16] A. Bahi and A. Ourici, “An Intelligent Agent-Based Simulation of Human Mobility in Extreme Urban Morphologies,” arXiv preprint, 2025.
[17] Q. Liu, C. Li, and W. Ma, “GATSim: Urban Mobility Simulation With Generative Agents,” arXiv preprint, 2025.
[18] L. Sun, N. Ashrafi, and M. Pishgar, “Optimizing Urban Mobility Through Complex Network Analysis and Big Data From Smart Cards,” arXiv preprint, 2025. [19] “Revolutionizing Urban Mobility: AI, IoT, and Predictive Analytics in Adaptive Traffic Control Systems,” Electronics, vol. 14, 2025.
[20] “Data-Driven Urban Mobility: Using Machine Learning to Optimize Traffic and Public Transport Operations,” Research Study, 2024
Downloads
Published
Conference Proceedings Volume
Section
License
Copyright (c) 2026 Adroid Conference Series: Engineering and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
