Amin Vahedian Khezerlou

Orcid: 0000-0003-3413-3744

According to our database1, Amin Vahedian Khezerlou authored at least 11 papers between 2014 and 2022.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2022
HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data.
Proceedings of the 2022 SIAM International Conference on Data Mining, 2022

2021
Forecasting Gathering Events through Trajectory Destination Prediction: A Dynamic Hybrid Model.
IEEE Trans. Knowl. Data Eng., 2021

DILSA+: Predicting Urban Dispersal Events through Deep Survival Analysis with Enhanced Urban Features.
ACM Trans. Intell. Syst. Technol., 2021

Precise Bayes Classifier: Summary of Results.
Proceedings of the IEEE International Conference on Data Mining, 2021

2020
Optimizing Taxi Driver Profit Efficiency: A Spatial Network-Based Markov Decision Process Approach.
IEEE Trans. Big Data, 2020

2019
Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Predicting Traffic Congestion Propagation Patterns: A Propagation Graph Approach.
Proceedings of the 11th ACM SIGSPATIAL International Workshop on Computational Transportation Science, 2018

2017
A Traffic Flow Approach to Early Detection of Gathering Events: Comprehensive Results.
ACM Trans. Intell. Syst. Technol., 2017

Forecasting Gathering Events through Continuous Destination Prediction on Big Trajectory Data.
Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2017

2016
A traffic flow approach to early detection of gathering events.
Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2016, Burlingame, California, USA, October 31, 2016

2014
A new model for discovering process trees from event logs.
Appl. Intell., 2014


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