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2. An Inverse Optimal Value Approach for Synchronously Optimizing Activity Durations and Worker Assignments with a Project Ideal Cost. (2023). Chen, Zhengrui ; Zhao, Yanan ; Zhang, Lili ; Shi, Dan.
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3. Decomposition and Adaptive Sampling for Data-Driven Inverse Linear Optimization. (2022). Gupta, Rishabh ; Zhang, QI.
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4. Inverse Mixed Integer Optimization: Polyhedral Insights and Trust Region Methods. (2022). , Timothy ; Bodur, Merve ; Zhu, Ian Yihang.
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5. A multimodal multicommodity network equilibrium model with service capacity and bottleneck congestion for China-Europe containerized freight flows. (2022). Bao, Zhaoyao ; Li, Xinyan ; Xie, Chi.
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6. Stability of Solutions for Parametric Inverse Nonlinear Cost Transportation Problem. (2020). Abo-Elnaga, Yousria ; Allah, Abd.
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7. Weight-categorized truck flow estimation: A data-fusion approach and a Florida case study. (2020). Eluru, Naveen ; Li, Xiaopeng ; Balusu, Suryaprasanna Kumar ; Sheela, Parvathy Vinod ; Zhao, Dongfang ; Pinjari, Abdul Rawoof.
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8. Network Learning via Multiagent Inverse Transportation Problems. (2018). Lai, Xuebo ; Xu, Susan Jia ; Nourinejad, Mehdi.
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   RePEc:inm:orserv:v:52:y:2018:i:6:p:1347-1364.

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9. A method to directly derive taste heterogeneity of travellers’ route choice in public transport from observed routes. (2017). Min, Yun-Hong ; Hong, Sung-Pil ; Kim, Kyungmin ; Byeon, Geunyeong.
   In: Transportation Research Part B: Methodological.
   RePEc:eee:transb:v:95:y:2017:i:c:p:41-52.

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10. An inventory-based simulation model for annual-to-daily temporal freight assignment. (2015). Ritchie, Stephen G. ; Zhao, Miyuan ; Chow, Joseph Y. J., .
    In: Transportation Research Part E: Logistics and Transportation Review.
    RePEc:eee:transe:v:79:y:2015:i:c:p:83-101.

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1. Traffic Simulation-Based Approach for A Cradle-to-Grave Greenhouse Gases Emission Model. (2019). Carrese, Stefano ; Patella, Sergio Maria ; Asdrubali, Francesco ; Scrucca, Flavio.
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2. Efficient calibration of microscopic car-following models for large-scale stochastic network simulators. (2019). Punzo, Vincenzo ; Osorio, Carolina.
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3. Stochastic travel demand estimation: Improving network identifiability using multi-day observation sets. (2018). Fan, Yueyue ; Yang, Yudi.
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4. Path Flow and Trip Matrix Estimation Using Link Flow Density. (2017). de Grange, Louis ; Bekhor, Shlomo ; Gonzalez, Felipe.
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5. Efficient calibration techniques for large-scale traffic simulators. (2017). Flotterod, Gunnar ; Zhang, Chao ; Osorio, Carolina.
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6. Inferring origin-destination pairs and utility-based travel preferences of shared mobility system users in a multi-modal environment. (2016). Kwon, Changhyun ; Kang, Jee Eun ; Kumar, Anshuman Anjani ; Nikolaev, Alexander.
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7. The Elevator Trip Origin-Destination Matrix Estimation Problem. (2015). Siikonen, Marja-Liisa ; Kuusinen, Juha-Matti ; Sorsa, Janne.
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12. A Bayesian method for estimating traffic flows based on plate scanning. (2013). Jimenez, Pilar ; Nogal, Maria ; Menendez, Jose ; Castillo, Enrique.
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20. Optimal traffic plate scanning location for OD trip matrix and route estimation in road networks. (2010). Sanchez-Cambronero, S. ; Minguez, R. ; Jimenez, P. ; Castillo, E..
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22. Identification of vehicle sensor locations for link-based network traffic applications. (2009). Peeta, Srinivas ; Hu, Shou-Ren ; Chu, Chun-Hsiao.
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31. An approach to estimating and updating origin-destination matrices based upon traffic counts preserving the prior structure of a survey matrix. (2005). Doblas, Javier ; Benitez, Francisco G..
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34. Simultaneous Estimation of the Origin-Destination Matrices and Travel-Cost Coefficient for Congested Networks in a Stochastic User Equilibrium. (2001). Yang, Hai ; Meng, Qiang.
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