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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Bas, T. A., Keivanpour, S., & Yarahmadi, A. (2024, February). Freight Broker Business Models in the Digital Age: A Comparative Analysis and Recommendations for Start-ups [Paper]. 14th International Conference on Industrial Engineering and Operations Management, Dubai, UAE. External link
Bas, T. A., Keivanpour, S., & Yarahmadi, A. (2023, October). Comparative Analysis of Smart Freight Matching Business Models and Sustainability KPIs [Paper]. 1st International Conference on Smart Mobility and Vehicle Electrification, Southfield, Michigan, USA (1 page). External link
Haji Sami, E., Shahnejat Bushehri, A., Amirnia, A., Yarahmadi, A., & Keivanpour, S. (2025). Integrated sequential matching and routing approach for efficient and eco-friendly freight logistics. Transportation Research Part C Emerging Technologies, 179, 105290. External link
Miyangaskary, M. K., Keivanpour, S., Lamghari, A., & Yarahmadi, A. (2023, October). Advanced Optimization Model Under Uncertainty for Sustainable Closed-Loop Supply Chain of Electric Vehicle Battery [Paper]. 1st International Conference on Smart Mobility and Vehicle Electrification, Southfield, Michigan, USA. External link
Shiri, A., Yarahmadi, A., & Keivanpour, S. (2025). Real-Time Matching and Dispatching for Urban Freight Transportation: A Hierarchical Reinforcement Learning Through Actor-Critic and H3 Spatial Partitioning. IEEE Transactions on Intelligent Transportation Systems, 1-12. External link
Shiri, A., Yarahmadi, A., Keivanpour, S., & Lamghari, A. (2024, October). Real-time RL-based Matching with H3 Geohash Partitioning in Smart Freight Platform [Paper]. IEEE 100th Vehicular Technology Conference (VTC2024-Fall), Washington, DC, USA. External link
Shiri, A., Keivanpour, S., Yarahmadi, A., & Lamghari, A. (2023, October). Learning-Based Matching Algorithm for Smart Freight Platform and Sustainability Assessment in Montreal [Paper]. 1st International Conference on Smart Mobility and Vehicle Electrification, Southfield, Michigan, USA. External link
Yarahmadi, A., Morency, C., & Trépanier, M. (2025). A context-aware approach to construct driving cycles in a volatile urban environment. Transportation, 33 pages. External link
Yarahmadi, A., Morency, C., & Trépanier, M. (2024). Identifying optimal number of driving cycles to represent diverse driving conditions. International Journal of Sustainable Transportation, 704-726. External link
Yarahmadi, A. (2023). A Methodological Framework to Develop Optimal Sets of Driving Cycles for a Region [Ph.D. thesis, Polytechnique Montréal]. Available
Yahyataba, A., Haji Sami, E., Lamghari, A., Keivanpour, S., & Yarahmadi, A. (2023, October). Stochastic Optimization Model for Smart Freight Matching and perspective of the application in Montreal [Paper]. 1st International Conference on Smart Mobility and Vehicle Electrification, Southfield, Michigan, USA. External link
Yarahmadi, A., Morency, C., & Trépanier, M. (2023). New data-driven approach to generate typologies of road segments. Transportmetrica A: Transport Science, 24 pages. External link
