We had invited talks by the guests from Diponegoro University, Iwan sensei, and Wido sensei. It was a nice opportunity to understand the state-of-the-art researches in Indonesia in the field of urban and regional planning.

We also have an informal “second” final master defense for the linkage students. Well done!

Place: IDEC large conference room

Date & time: 15:00-17:45 on September 21, 2022

Schedule:

(1) Invited talks

15:00-15:45  Prof. Wido Prananing Tyas

“Strategy and Innovation of Micro and Small Enterprises for Local Development toward 4.0 Era”

15:45-16:30  Prof. Iwan Rudiarto

“Building Scenario for Future Spatial Formations and Development”

16:30-16:45 Break

(2) Presentations of students under Linkage program

16:45-17:05 Dopit Saputra

17:05-17:25 Kiki Nidya Stephanie

17:25-17:45 Melanton Hendra Siregar

Four master students have just completed their study in our lab. Many congratulations on your graduation, Ei Ei, Alex, Hulio, and Hendra san!

PS. We are lucky enough to have two guests, Iwan sensei and Wido sensei, from Diponegoro University on such a memorial day.

Survey on expected return time in Kure, Hiroshima

The following paper is now available online:

Ishii, Y., Hayakawa, K., Koide, S., & Chikaraishi, M. (2022). Entropy Tucker model: Mining latent mobility patterns with simultaneous estimation of travel impedance parameters. Transportation Research Part C: Emerging Technologies, 137, 103559.

Abstract

With the rapid increase in the availability of passive data in the field of transportation, combining machine learning with transportation models has emerged as an important research topic in recent years. This study proposes an entropy Tucker model that integrates (1) a Tucker decomposition technique, i.e., an existing machine learning method, and (2) an entropy maximizing model, i.e., an existing model used for modeling trip distribution in the field of transportation. In addition, an optimization algorithm is presented to empirically identify the proposed model. The proposed model provides a solid theoretical foundation for the machine learning method, substantially improves prediction performance, and provides richer behavioral implications through empirical parameter estimation of travel impedance. We conducted a case study using public transit smart card data. The results showed that the proposed model improves the prediction performance and interpretability of the results compared to the conventional nonnegative Tucker decomposition technique. Further, we empirically confirmed that the travel impedance varies with the origin–destination pair, time of the day, and day of the week. Finally, we discussed how embedding the theoretical foundations of transport modeling into machine learning methods can facilitate the use of various passive data in wider practical contexts of transport policy decision making.

https://www.sciencedirect.com/science/article/pii/S0968090X22000080

The following paper is now available online:

Nakayama, S., Chikaraishi, M. (2021) Doubly generalized logit: a closed-form discrete choice model system with multivariate generalized extreme value distributed utilities, Transportation Research Part C, Vol. 132, 103315.

Abstract:
In this study, we generalize our previous q-generalized multinomial logit model (Nakayama & Chikaraishi, 2015), in which the heteroscedastic variance and flexible shape of the utility distribution are considered, by allowing for statistical dependency of alternatives. This is achieved by introducing the q-generalization of McFadden’s multivariate Gumbel distribution. Thus, the logit model is doubly generalized; 1) each utility follows the generalized extreme value distribution that includes the Gumbel, Weibull, and Fréchet distributions; and 2) the utility distribution is multivariate, and therefore, a nested or cross-nested structure and dependency of alternatives are allowed. The proposed doubly generalized logit model system allows for deriving new closed-form discrete choice models such as the q-generalized nested logit model and q-generalized cross-nested logit (CNL) model. Furthermore, the model system includes conventional logit models such as the multinomial logit, nested logit, and CNL models as special cases as well as the new generalized logit models, while retaining a closed-form expression. We empirically confirm that the goodness-of-fit of the proposed model could be substantially better compared to that of the conventional models in some cases, though the degree of improvements varies across cases.

https://www.sciencedirect.com/science/article/pii/S0968090X21003235?dgcid=author