DHPA: Dynamic Human Preference Analytics Framework: A Case Study on Taxi Drivers’ Learning Curve Analysis
Open accessAbstract
Many real-world human behaviors can be modeled and characterized as sequential decision-making processes, such as a taxi driver’s choices of working regions and times. Each driver possesses unique preferences on the sequential choices over time and improves the driver’s working efficiency. Understanding the dynamics of such preferences helps accelerate the learning process of taxi drivers. Prior works on taxi operation management mostly focus on finding optimal driving strategies or routes, lacking in-depth analysis on what the drivers learned during the process and how they affect the performance of the driver. In this work, we make the first attempt to establish Dynamic Human Preference Analytics. We inversely learn the taxi drivers’ preferences from data and characterize the dynamics of such preferences over time. We extract two types of features (i.e., profile features and habit features) to model the decision space of drivers. Then through inverse reinforcement learning, we learn the preferences of drivers with respect to these features. The results illustrate that self-improving drivers tend to keep adjusting their preferences to habit features to increase their earning efficiency while keeping the preferences to profile features invariant. However, experienced drivers have stable preferences over time. The exploring drivers tend to randomly adjust the preferences over time.
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Cites
Proceedings of the 25th international conference on Machine learning - ICML '08
(2008) · doi:10.1145/1390156 · Cited by 6,865 · see citing works
Bellman, R. (1957) · Indiana University Mathematics Journal · doi:10.1512/iumj.1957.6.56038 · Cited by 2,408 · see citing works
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics 2016
Murray, I., & Graham, M. (2016) · Cited by 2,079 · see citing works
Proceedings of the 26th International Conference on Data Engineering, ICDE 2010
Li, F., Moro, M. M., Ghandeharizadeh, S., Haritsa, J. R., Weikum, G., Carey, M. J., Casati, F., Chang, E. Y., Manolescu, I., Mehrotra, S., et al. (2010) · Max Planck Institute for Plasma Physics · Cited by 452 · see citing works
Bayesian Inverse Reinforcement Learning
Krishnamurthy, V. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009449441.027 · Cited by 443 · see citing works
T-Finder: A Recommender System for Finding Passengers and Vacant Taxis
Yuan, N. J., Zheng, Y., Zhang, L., & Xie, X. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.153 · Cited by 432 · see citing works
Yuan, Z., Zhou, X. S., & Yang, T. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219922 · Cited by 376 · see citing works
Self-citationDeep Distributed Fusion Network for Air Quality Prediction
Yi, X., Zhang, J., Wang, Z., Li, T., & Zheng, Y. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219822 · Cited by 341 · see citing works
Tong, Y., Chen, Y., Zhou, Z., Chen, L., Wang, J., Yang, Q., Ye, J., & Lv, W. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098018 · Cited by 271 · see citing works
A cost-effective recommender system for taxi drivers
Qu, M., Zhu, H., Liu, J., Liu, G., & Xiong, H. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623668 · Cited by 187 · see citing works
Growing the charging station network for electric vehicles with trajectory data analytics
Li, Y., Luo, J., Chow, C.-Y., Chan, K.-L., Ding, Y., & Zhang, F. (2015) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2015.7113384 · Cited by 132 · see citing works
Self-citationRong, H., Zhou, X. S., Yang, C., Shafiq, Z., & Liu, A. X. (2016) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2983323.2983689 · Cited by 81 · see citing works
Self-citationWang, J., Huang, H., Xu, P., Xie, S., & Wong, S. C. (2019) · Journal of Transportation Safety & Security · doi:10.1080/19439962.2018.1551257 · Cited by 81 · see citing works
Detecting Vehicle Illegal Parking Events using Sharing Bikes' Trajectories
He, T., Bao, J., Li, R., Ruan, S., Li, Y., Tian, C., & Zheng, Y. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219887 · Cited by 60 · see citing works
Self-citationTaxi Driving Behavior Analysis in Latent Vehicle-to-Vehicle Networks
Xu, T., Zhu, H., Zhao, X., Liu, Q., Zhong, H., Chen, E., & Xiong, H. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939799 · Cited by 49 · see citing works
The Optimal Distribution of Electric-Vehicle Chargers across a City
Liu, C., Deng, K., Li, C., Li, J., Li, Y., & Luo, J. (2016) · Proceedings · doi:10.1109/icdm.2016.0037 · Cited by 45 · see citing works
Self-citationDetecting and Analyzing Urban Regions with High Impact of Weather Change on Transport
Ding, Y., Li, Y., Deng, K., Tan, H., Yuan, M., & Ni, L. M.-S. (2016) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2016.2623320 · Cited by 38 · see citing works
Self-citationA Traffic Flow Approach to Early Detection of Gathering Events
Vahedian, A., Zhou, X. S., Li, L., Shafiq, Z., Liu, A. X., & Zhang, F. (2017) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3078850 · Cited by 33 · see citing works
Self-citationRegion sampling and estimation of geosocial data with dynamic range calibration
Li, Y., Steiner, M., Bao, J., Wang, L., & Zhu, T. (2014) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2014.6816726 · Cited by 30 · see citing works
Self-citationDissecting the Learning Curve of Taxi Drivers: A Data-Driven Approach
Pan, M., Li, Y., Zhou, X. S., Liu, Z., Song, R., Lu, H. D., & Luo, J. (2019) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611975673.88 · Cited by 26 · see citing works
Self-citationScalable user assignment in power grids
Lyu, B., Li, S., Li, Y., Fu, J., Trapp, A. C., Xie, H., & Liao, Y. (2016) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2996913.2996970 · Cited by 8 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 8
- [3]3. Pieter Abbeel and Andrew Y. Ng. 2004. Apprenticeship learning via inverse reinforcement learning. In Proceedings of the 21st International Conference on Machine Learning. ACM, New York, NY, 1.
- [22]22. R. S. Witte and J. S. Witte. 2013. Statistics (10th ed.). John Wiley & Sons.
- [24]24. Y. Ge, H. Xiong, A. Tuzhilin, K. Xiao, and M. Gruteser. 2010. An energy-efficient mobile recommender system. In Proceedings of the 16th International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, 899- 908.
- [25]25. Y. Ge, C. Liu, H. Xiong, and J. Chen. 2011. A taxi business intelligence system. In Proceedings of the 17th International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, 735-738.
- [26]26. Zi Yang, Xinpeng Wang, Xin Pei, Shuo Feng, Dajun Wang, Jianqiang Wang, and S. C. Wong. 2018. Longitudinal safety analysis for heterogeneous platoon of automated and human vehicles. In Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC'18). IEEE, Los Alamitos, CA, 3300-3305.
- [28]28. J. Yuan, Y. Zheng, L. Zhang, X. Xie, and G. Sun. 2011. Where to find my next passenger. In Proceedings of the 13th International Conference on Ubiquitous Computing. ACM, New York, NY, 109-118.
- [30]30. Brian D. Ziebart. 2010. Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy. Master's Thesis. Carnegie Mellon University.
- [31]31. Brian D. Ziebart, Andrew L. Maas, J. Andrew Bagnell, and Anind K. Dey. 2008. Maximum entropy inverse reinforce- ment learning. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI'08), Vol. 8. 1433-1438.
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Spatio-temporal feature fusion for dynamic taxi route recommendation via deep reinforcement learning
Ji, S., Wang, Z., Li, T., & Zheng, Y. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106302 · Cited by 64 · see citing works
BRITD: behavior rhythm insider threat detection with time awareness and user adaptation
Song, S., Gao, N., Zhang, Y., & Ma, C. (2024) · Cybersecurity · doi:10.1186/s42400-023-00190-9 · Cited by 45 · see citing works
“User behaviors can be described as sequential decision-making processes (Pan et al. 2020).”
BackgroundWang, G., Fang, Z., Xie, X., Wang, S., Sun, H., Zhang, F., Liu, Y., & Zhang, D. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3428080 · Cited by 35 · see citing works
xGAIL: Explainable Generative Adversarial Imitation Learning for Explainable Human Decision Analysis
Pan, M., Huang, W., Li, Y., Zhou, X. S., & Luo, J. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403186 · Cited by 33 · see citing works
Self-citationPradeep, S., Sharma, Y. K., Lilhore, U. K., Simaiya, S., Kumar, A., Ahuja, S., Margala, M., Chakrabarti, P., & Chakrabarti, T. (2023) · Scientific Reports · doi:10.1038/s41598-023-44701-7 · Cited by 29 · see citing works
Neural network training method for materials science based on multi-source databases
Guo, J., Chen, Z., Liu, Z., Li, X., Xie, Z. Y., Wang, Z., & Wang, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-19426-8 · Cited by 17 · see citing works
Self-citationWu, Y., Xiang, Y., Tong, E., Ye, Y., Cui, Z., Tian, Y., Zhang, L., Liu, J., Han, Z., & Niu, W. (2024) · IEEE Network · doi:10.1109/mnet.2024.3366232 · Cited by 13 · see citing works
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Zhang, L. (2022) · Scientific Reports · doi:10.1038/s41598-022-23209-6 · Cited by 12 · see citing works
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Li, N., Zhang, M., Li, J., Kang, E., & Tei, K. (2023) · International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) · doi:10.1109/seams59076.2023.00027 · Cited by 12 · see citing works
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Xu, Y., Xiao, M., Wu, J., & He, S. (2024) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2024.3434978 · Cited by 11 · see citing works
Enhancing Container Security Through Phase-Based System Call Filtering
Chen, K., Lu, H. D., Yao, Y., Fang, B., Liu, Y., & Tian, Z. (2025) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2025.3583414 · Cited by 11 · see citing works
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Self-citation
- Authors
- Pan, M., Huang, W., Li, Y., Zhou, X., Liu, Z., Song, R., Lu, H., Tian, Z., & Luo, J.
- Affiliations
- Worcester Polytechnic Institute (US)
- University of Iowa (US)
- William & Mary (US)
- North Carolina State University (US)
- Guangzhou University (CN)
- Lenovo Group Limited
- Venue
- ACM Transactions on Intelligent Systems and Technology
- Published
- 2020-01-17
- Volume
- 11
- Issue
- 1
- Pages
- 1-19
- ISSN
- 2157-6904 (ISSN-L)
- 2157-6912
- OpenAlex
- W3013077068
- Cited by
- 47
- References
- 21
- Citations by year
- 2025: 10 · 2024: 16 · 2023: 5 · 2022: 9 · 2021: 2 · 2020: 4 · 2019: 1
- FWCI
- 1.96
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Transactions on Intelligent Systems and Technology (publishedVersion)
- Funders
- DiDi Chuxing Inc.
- NSF (CNS-1657350, CMMI-1831140, IIS-1566386, IIS-1755769 and DMS-1555244)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- inverse reinforcement learning, sequential decision making, profile features, driver learning
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