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Base de données et galerie internationale d'ouvrages d'art et du génie civil

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Alexander M. Hainen

La bibliographie suivante contient toutes les publications répertoriées dans la base de données qui sont reliées à ce nom en tant qu'auteur, éditeur ou collaborateur.

Talukder, Md Abu Sufian / Tedla, Elsa G. / Hainen, Alexander M. / Atkison, Travis (2022): Analytical and Empirical Evaluation of Freight Priority System in Connected Vehicle Environment. Dans: Journal of Transportation Engineering, Part A: Systems, v. 148, n. 6 (juin 2022).
https://doi.org/10.1061/jtepbs.0000673
Lidbe, Abhay D. / Tedla, Elsa G. / Hainen, Alexander M. / Jones, Steven L. (2017): Analytical Techniques for Evaluating the Implementation of Adaptive Traffic Signal Control Systems. Dans: Journal of Transportation Engineering, Part A: Systems, v. 143, n. 5 (mai 2017).
https://doi.org/10.1061/jtepbs.0000034
Lidbe, Abhay D. / Tedla, Elsa G. / Hainen, Alexander M. / Jones, Steven L. (2019): Feasibility Assessment for Implementing Adaptive Traffic Signal Control. Dans: Journal of Transportation Engineering, Part A: Systems, v. 145, n. 2 (février 2019).
https://doi.org/10.1061/jtepbs.0000208
Talukder, Md Abu Sufian / Lidbe, Abhay D. / Tedla, Elsa G. / Hainen, Alexander M. / Atkison, Travis (2021): Trajectory-Based Signal Control in Mixed Connected Vehicle Environments. Dans: Journal of Transportation Engineering, Part A: Systems, v. 147, n. 5 (mai 2021).
https://doi.org/10.1061/jtepbs.0000510
Hainen, Alexander M. / Harbin, Kristopher B. / Dye, Daniel / Lindly, Jay K. (2020): Duration Analysis of Emergency Shutdown Incidents Regarding Hazardous Liquid Pipelines. Dans: Journal of Performance of Constructed Facilities (ASCE), v. 34, n. 3 (juin 2020).
https://doi.org/10.1061/(asce)cf.1943-5509.0001364
Crawford, P. Shane / Al-Zarrad, Mohammad A. / Graettinger, Andrew J. / Hainen, Alexander M. / Back, Edward / Powell, Lawrence (2018): Rapid Disaster Data Dissemination and Vulnerability Assessment through Synthesis of a Web-Based Extreme Event Viewer and Deep Learning. Dans: Advances in Civil Engineering, v. 2018 ( 2018).
https://doi.org/10.1155/2018/7258156

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