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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
New approach for a stable multi-criteria ridesharing system
Sawsen Ben Nasr · 2019-01-09 · via cs.DS updates on arXiv.org

The witnessed boom in mobility results in many problems such as urbanization, costly construction of many highways and air pollution. In an attempt to address these problems, in this master, we are interested in the implementation of a ridesharing system. Ridesharing is recognized as a highly effective means of transport to solve energy consumption, environmental pollution and traffic congestion issues. Indeed, ridesharing can reduce the number of vehicles on the roads to avoid traffic jams and thus it contributes to a reduction in greenhouse gas emissions. Its main thrust resides in sharing transport expenses, meeting different people and making traveling more enjoyable. In this respect, we introduce in this dissertation an effective ridesharing system, called the Stable Multi-Criteria Rideshare Matching (SMRM) system, that (i) considers users' personal preferences when sharing a private space with others and (ii) enables a stable matching between driver and passenger sets. The performed experiments show that the introduced system outperforms its competitors in terms of stability quality and cost. Keywords: Smart cities, Social sustainability, Ridesharing , Social preferences , TOPSIS , Stable marriage .