no code implementations • 24 Dec 2023 • Kexin Chen, Jinping Guan, Ravi Seshadri, Varun Pattabhiraman, Youssef Medhat Aboutaleb, Ali Shamshiripour, Chen Liang, Xiaochun Zhang, Moshe Ben-Akiva
The utility includes both the benefit in the inventory gained and the cost in time, monetary expense as well as maintenance of safety stock.
no code implementations • 29 Dec 2021 • Giovanni Calabro', Andrea Araldo, Simon Oh, Ravi Seshadri, Giuseppe Inturri, Moshe Ben-Akiva
Our model allows deciding whether to deploy a FR or a DR feeder, in each sub-region of an urban conurbation and each time of day, and to redesign the line frequencies and the stop spacing of the main trunk service.
no code implementations • 31 May 2021 • Haizheng Zhang, Ravi Seshadri, A. Arun Prakash, Constantinos Antoniou, Francisco C. Pereira, Moshe Ben-Akiva
Simulation-based Dynamic Traffic Assignment models have important applications in real-time traffic management and control.
no code implementations • 21 Jan 2021 • Youssef M. Aboutaleb, Mazen Danaf, Yifei Xie, Moshe Ben-Akiva
This paper discusses capabilities that are essential to models applied in policy analysis settings and the limitations of direct applications of off-the-shelf machine learning methodologies to such settings.
1 code implementation • 18 Aug 2020 • Youssef M. Aboutaleb, Moshe Ben-Akiva, Patrick Jaillet
We formulate the problem of learning an optimal nesting structure from the data as a mixed integer nonlinear programming (MINLP) optimization problem and solve it using a variant of the linear outer approximation algorithm.
2 code implementations • 3 Feb 2020 • Yafei Han, Francisco Camara Pereira, Moshe Ben-Akiva, Christopher Zegras
Our formulation consists of two modules: a neural network (TasteNet) that learns taste parameters (e. g., time coefficient) as flexible functions of individual characteristics; and a multinomial logit (MNL) model with utility functions defined with expert knowledge.
no code implementations • 14 Jan 2020 • Youssef M. Aboutaleb, Mazen Danaf, Yifei Xie, Moshe Ben-Akiva
We propose a new estimator, called MISC, that uses a mixed-integer optimization (MIO) program to find an optimal block diagonal structure specification for the covariance matrix, corresponding to subsets of correlated coefficients, for any desired sparsity level using Markov Chain Monte Carlo (MCMC) posterior draws from the unrestricted full covariance matrix.