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Amin Vahedian Khezerlou |
Publications |
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PUBLICATIONS ·
Johnson, N., Prasad, S., Vahedian, A., Altay, N. and Jain, A.
(2022). Modelling ragpickers’ productivity at the bottom of the pyramid: the
use of artificial neural networks (ANNs). International Journal of Operations
& Production Management, Vol. 42 No. 4, pp. 552-576. ·
An, B., Vahedian, A., Zhou, X., Street, W. N., & Li, Y.
(2022). HintNet: Hierarchical Knowledge Transfer
Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data. In Proceedings of the 2022 SIAM
International Conference on Data Mining (SDM) (pp. 334-342). Society for
Industrial and Applied Mathematics. ·
Vahedian, A., & Zhou, X. (2021, December). Precise Bayes
Classifier: Summary of Results. In 2021 IEEE International Conference on Data
Mining (ICDM) (pp. 649-658). IEEE. Acceptance rate 9.9%. ·
Vahedian, A., Zhou, X., Li, X., Street, W. N., Li, Y. (2021).
DILSA+: Predicting Urban Dispersal Events Through Deep Survival Analysis with
Enhanced Urban Features. ACM Transactions on Intelligent Systems and
Technology (ACM TIST), 12(4), 1-25. ·
Vahedian, A., Zhou, X., Tong, L., Street, W.N. & Li, Y.
(2019). Predicting Urban Dispersal Events: A Two- Stage Framework through
Deep Survival Analysis on Mobility Data. In 2019 AAAI Conference on
Artificial Intelligence (AAAI'19), January 27, 2019, Honolulu, HI, USA. ·
Vahedian, A., Zhou, X., Tong, L., Li, Y., & Luo, J. (2019).
Forecasting Gathering Events through Trajectory Destination Prediction: A
Dynamic Hybrid Model. IEEE Transactions on Knowledge and Data Engineering
(IEEE TKDE). ·
Khezerlou, A. V., Zhou, X., Li, L., Shafiq, Z., Liu, A. X., &
Zhang, F. (2017). A traffic flow approach to early detection of gathering
events: Comprehensive results. ACM Transactions on Intelligent Systems and
Technology (ACM TIST), 8(6), 74. ·
Zhou, X., Rong, H., Yang, C., Zhang, Q., Khezerlou, A.V., Zheng,
H., Shafiq, Z., Liu, A.X. (2018). Optimizing Taxi Driver Profit Efficiency: A
Spatial Network-based Markov Decision Process Approach. IEEE Transactions on
Big Data (IEEE TBD). ·
Vahedian, A., Zhou, X., Tong, L., Li, Y., & Luo, J. (2017,
November). Forecasting gathering events through continuous destination
prediction on big trajectory data. In Proceedings of the 25th ACM SIGSPATIAL
International Conference on Advances in Geographic Information Systems (p.
34). ACM. ·
Zhou, X., Khezerlou, A. V., Liu, A., Shafiq, Z., & Zhang, F.
(2016, October). A traffic flow approach to early detection of gathering
events. In Proceedings of the 24th ACM SIGSPATIAL International Conference on
Advances in Geographic Information Systems (p. 4). ACM. ·
Khezerlou, A. V., & Alizadeh, S. (2014). A New Model for
Discovering Process Trees from Event Logs. Applied intelligence, 41(3),
725-735. PEER-REVIEWED WORKSHOP ARTICLES ·
Xiong, H.,
Vahedian, A., Zhou, X., Li, Y., & Luo, J. (2018). Predicting Traffic Congestion
Propagation Patterns: A Propagation Graph Approach. In 11th ACM SIGSPATIAL
International Workshop on Computational Transportation Science (IWCTS'18),
November 6, 2018, Seattle, WA, USA. ·
Chiu, J., Vahedian, A. & Zhou, X. (2018). Understanding
Business Location Choice Pattern: A Co-Location Analysis on Urban POI Data.
In 2nd INFORMS Workshop on Data Science, November 3, 2018, Phoenix, AZ, USA. ·
Vahedian, A., Li, X., Xiong, H., Zhou,
X., Colbert, A. (Oct. 2019). Motivated or Exhausted: A Data-Driven Study of
Taxi Driver Behavior Following Traffic Congestions. 3rd INFORMS Workshop on
Data Science. Seattle, Washington, USA. ARTICLES UNDER REVIEW AND WORKING PAPERS ·
Johnson, N., Prasad, S., Zakaria, R., Vahedian, A., Altay, N. Capturing
non-linear interactions: A synergistic approach in helping vulnerable
communities (Submitted December 2021). International Journal of Forecasting. ·
Vahedian, A., Zhou, X., Colbert, A. (Working paper). Effects of
Traffic Congestion on Taxi Driver Motivation: A Novel Analysis of Empirical
Distributions (Target Journal: ACM TMIS). ·
An, B., Vahedian, A., Zhou, X. LisaNet:
Learning-Integrated Space Partition Networks for Traffic Accident Forecasting
on Heterogeneous Data (Working paper. Target: IEEE TKDE). ·
Vahedian, A., Zhou, X. Non-parametric Bayesian Learning (Working
paper. Target: IEEE TKDE). |
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