[2H5-OS-8a] 移動系列のデータマイニングと機械学習

[2H5-OS-8a-03] Route Choice Model with Sparse Output Based on Generalized Entropy Regularization

〇Aoi Watanabe¹, Ken Hidaka¹ (1. Toyota Central R&D Labs., Inc.)

Keywords:route choice model, traffic assignment, generalized entropy regularization, Tsallis entropy, alpha-entmax

Modeling route choices of travelers to reach their destinations has various practical applications. However, conventional route choice models use a softmax function to calculate the probability of each route based on its cost, which results in positive flow even for extremely long routes. In this research, we developed a route choice model that can account for zero flow by utilizing the sparse output property of the activation function. The sparse activation function was derived through regularization with generalized entropy, a generalization of Shannon entropy. Our results show that our model can exhibit a range of models, from a softmax-type model to one that captures zero flow. Additionally, we show that parameter estimation can be performed through linear regression.
This reduces the computational cost on previous route choice models that could represent zero flow, which required a combination of maximum likelihood method and shortest path search during estimation.