The recent application of the rational inattention (RI) theory in transportation has shed light on a promising alternative way of understanding how information influences the travel choices of passengers. However, existing RI literature has not yet addressed the discrete choice problem with multiple variates. Thus, this study develops a multivariate rational inattention (MRI) discrete choice model. This assumes that acquiring information is costly and the unit information cost varies among variates, so decision-makers rationally choose the amount of information to acquire for each variate. We demonstrate that the MRI discrete choice model results in a probabilistic formulation similar to the logit model, but with the superiority of integrating unit information costs and the prior knowledge of decision-makers. Furthermore, we apply the MRI discrete choice model to the metro route choice problem and calibrate the model based on the revealed preference (RP) data collected from the Chengdu metro. It is found that the proposed model has satisfactory accuracy with better interpretability than the logit model and univariate rational inattention discrete choice model.
Export Date: 18 December 2024
Correspondence Address: Jiang, G.; School of Intelligent Systems Engineering, China; email: jianggg@mail.sysu.edu.cn
Funding details: Fundamental Research Funds for the Central Universities
Funding details: Civil Aviation University of China, CAUC, 3122024QD05
Funding details: Civil Aviation University of China, CAUC
Funding details: National Natural Science Foundation of China, NSFC, 71971038, 72201285, 52272308
Funding details: National Natural Science Foundation of China, NSFC
Funding details: Sun Yat-sen University, SYSU, 22qntd1701
Funding details: Sun Yat-sen University, SYSU
Funding details: Basic and Applied Basic Research Foundation of Guangdong Province, 2024A1515012422
Funding details: Basic and Applied Basic Research Foundation of Guangdong Province
Funding text 1: This research was supported by National Natural Science Foundation of China [72201285, 52272308, 71971038], Fundamental Research Funds for Central Universities, Civil Aviation University of China [3122024QD05] and the Guangdong Basic and Applied Basic Research Foundation [2024A1515012422].
Funding text 2: This research was supported by Fundamental Research Funds for the Central Universities , Civil Aviation University of China [ 3122024QD05 ], Grants from the National Natural Science Foundation of China [ 72201285 ], [ 52272308 ], and the Fundamental Research Funds for the Central Universities, Sun Yat-sen University [ 22qntd1701 ].
