This paper proposes the use of a quasi-random sequence, specifically the Halton sequence, for estimating the mixed multinomial logit (MMNL) model. The MMNL model is a flexible discrete choice model that accommodates general patterns of competitiveness and heterogeneity across individuals. Traditional estimation methods, such as the pseudo-random maximum simulated likelihood method, use pseudo-random points to evaluate multi-dimensional integrals in the log-likelihood function. The paper suggests an alternative quasi-random maximum simulated likelihood method that uses non-random but uniformly distributed sequences to improve accuracy and reduce computational time. The quasi-random method is compared with the polynomial-based cubature method and the pseudo-random Monte Carlo (PMC) method through numerical experiments in the context of intercity travel mode choice. The results indicate that the quasi-random method provides significantly better accuracy with fewer draws and computational time compared to the pseudo-random method. This suggests that the quasi-random method could facilitate the practical application of the flexible MMNL model, which is useful for modeling behaviorally rich structures in discrete choice analysis. The paper also discusses the theoretical foundations of the quasi-random method, including the properties of quasi-random sequences and their superior uniform distribution compared to pseudo-random sequences. The experimental design involves generating data with varying levels of random coefficients to test the performance of the different estimation methods across different dimensions of integration. The results show that the quasi-random method outperforms both the polynomial-cubature and pseudo-Monte Carlo methods, especially in higher dimensions. Overall, the paper highlights the potential of the quasi-random method to enhance the efficiency and accuracy of MMNL model estimation, making it a valuable tool for researchers and practitioners in discrete choice analysis.This paper proposes the use of a quasi-random sequence, specifically the Halton sequence, for estimating the mixed multinomial logit (MMNL) model. The MMNL model is a flexible discrete choice model that accommodates general patterns of competitiveness and heterogeneity across individuals. Traditional estimation methods, such as the pseudo-random maximum simulated likelihood method, use pseudo-random points to evaluate multi-dimensional integrals in the log-likelihood function. The paper suggests an alternative quasi-random maximum simulated likelihood method that uses non-random but uniformly distributed sequences to improve accuracy and reduce computational time. The quasi-random method is compared with the polynomial-based cubature method and the pseudo-random Monte Carlo (PMC) method through numerical experiments in the context of intercity travel mode choice. The results indicate that the quasi-random method provides significantly better accuracy with fewer draws and computational time compared to the pseudo-random method. This suggests that the quasi-random method could facilitate the practical application of the flexible MMNL model, which is useful for modeling behaviorally rich structures in discrete choice analysis. The paper also discusses the theoretical foundations of the quasi-random method, including the properties of quasi-random sequences and their superior uniform distribution compared to pseudo-random sequences. The experimental design involves generating data with varying levels of random coefficients to test the performance of the different estimation methods across different dimensions of integration. The results show that the quasi-random method outperforms both the polynomial-cubature and pseudo-Monte Carlo methods, especially in higher dimensions. Overall, the paper highlights the potential of the quasi-random method to enhance the efficiency and accuracy of MMNL model estimation, making it a valuable tool for researchers and practitioners in discrete choice analysis.