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

Congratulations on your graduation, Maya Safira (Doctor course), Faisal Ali (Master course). Dr. Maya Safira is the very first PhD student from our lab. We really appreciate her hard work and efforts. Particularly, she made a great contribution to improving our ability as a team. We will try to keep your meme in our lab. Best wishes for both of your future!

The Excellent Young Researcher Award in the 2021 Summer School was given to Diana Nur Safitri (the 1st year PhD student). Congraturations!

Hirodai team won the 3rd place of excellence in the 20th summer course: behavioral modeling in transportation networks. Congratulations!

Diana won the Top Prize and Yoshioka won a Best Paper Award from the EASTS2021! Congratulations!

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Yasoshima Yoshinosuke Prize (The Top Prize from all EASTS2021 papers)

Authors: Nur Diana Safitri, Makoto Chikaraishi:
Title: Impact of Transport Network Disruption on Travel Demand: A Case Study of July 2018 Heavy Rain Disaster, Japan

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Best Paper Award for “Discovering interesting facts”

Authors: Taisei YOSHIOKA, Makoto CHIKARAISHI, Akimasa FUJIWARA:
Title: Empirical Models of Consumer and Merchant Behavior in the Two-Sided Market of Local Currency

The following paper is now available online!

Varghese, V., Chikaraishi, M., Jana, A. (2021) The architecture of complexity in the relationships between information and communication technologies and travel: A review of empirical studies, Transportation Research Interdisciplinary Perspectives, Vol. 11, 100432.

Abstract: The use of information and communication technologies (ICT) has become an integral part of people’s lives, having complex interactions with different facets of human activity participation and travel behaviour. Studies over decades have tried to understand the relationships between ICT and travel. However, the fast evolution of ICT systems and the complex nature of these relationships have resulted in a limited understanding of the overall architecture, the sub-components, and their interactions. This study aims to achieve two objectives. First, we update the current state of knowledge on ICT-travel relationships by conducting an extensive literature review. Second, based on the findings of the review, we propose a conceptual model that identifies sub-components and their interrelationships that need to be better understood to accurately grasp the impacts of ICT on transportation. We summarized 186 empirical studies from the perspectives of survey methods, ICT devices, services, applications, direct relationships with travel behaviour related variables and higher-order or indirect impacts on other parameters such as emissions and congestion. It was observed that the nature of empirical results would depend on assumptions, objectives and type of data used. The findings of the review were then used to classify architecture in the relationship between ICT and travel into four distinct types. The merits, demerits, and challenges associated with the analysis across types were then illustrated. We then develop a conceptual model that highlighted and discussed the possible interactions between 1) ICT systems, 2) travel-related factors and 3) higher-order variables.

https://www.sciencedirect.com/science/article/pii/S259019822100138X

The following paper is now available online!

Santos, J.R., Varghese, V., Chikaraishi, M., Uchida, T. (2021) An Integrated Framework for Risk and Impact Assessment of Sediment Hazard on a Road Network, Transportation Research Record.

Abstract: Road networks are highly vulnerable to risks stemming from both internal factors, such as the topological structure of the network, and external factors, such as natural disasters. The disruptions caused by these potential risk factors could result in severe physical and socio-economic losses. Therefore, understanding the impact and risk associated with road networks will be beneficial in reducing losses and helping to prepare better risk mitigation and management strategies. This study proposes an integrated approach to assess risk of sediment hazard on the road network by borrowing concepts from (a) transport vulnerability studies, (b) disaster risk assessment, and (c) spatial risk analysis and applying it to an identified vulnerable road network in Kure, Japan. The proposed risk framework holistically incorporates the processes of topological network vulnerability analysis, exposure spatial analysis, hazard occurrence probability estimation through binary logit regression, impact calculation using Monte Carlo simulation, and risk estimation. Using the recorded information on the rainfall event and sediment disaster that occurred in Hiroshima prefecture in July 2018, 12,000 possible multi-link disruption scenarios were simulated. Spatial distribution of the risk calculations helped to identify links with high probability of disruption and high impact, that is, high-risk links. The findings of this study may support policy decisions on road risk mitigation and recovery prioritization during disaster and road infrastructure investment through risk-benefit analysis.

https://journals.sagepub.com/doi/abs/10.1177/03611981211016462

The following paper is now available online!

Santos, J.R., Safitri, N.D., Safira, M., Varghese, V., Chikaraishi, M. (2021) Road Network Vulnerability and City-level Characteristics: A Nationwide Comparative Analysis of Japanese Cities, Environment and Planning B:Urban Analytics and City Science (Accepted).

Abstract: Climate change is making our cities more vulnerable, increasing the needs for further policy actions to make them more resilient. In particular, the transport network is critical in the first phase of disaster response. This study presents the epirical findings of a large scale, nationwide analysis of the road network vulnerability in 69 Japanese cities. We (1) identify the level of network efficiency using topological elements in its undisturbed normal state; (2) evaluate the level of network robustness under different random and targeted attack scenarios; and (3) analyze the relationship of the identified network efficiency and robustness indicators with city-level characteristics. The main findings include: (1) cities with a higher population and a higher infrastructure investment tend to be more robust under random attacks; (2) larger cities tend to be less robust to targeted attacks, presumably due to their high agglomeration of urban functions; (3) car dependency tends to make cities more vulnerable toward random attacks and less vulnerable toward targeted attacks as it indicates a weaker concentration in urban functions; and (4) a high modal share for trains tends to make cities less vulnerable toward random events as it indicates a high agglomeration of urban functions. These findings will help policymakers to prioritize their budget allocations to improve nationwide disaster resilience.

https://journals.sagepub.com/doi/abs/10.1177/2399808321999318

The following paper is now available online!

Wu, L., Chikaraishi, M., Nguyen, T.A.H., Fujiwara, A. (2021) Analysis of post-disaster population movement by using mobile spatial statistics, International Journal of Disaster Risk Reduction, 54, 102047.

Abstract: Understanding and predicting post-disaster human movements is critical for evaluating a population’s vulnerability and resilience and developing plans for disaster evacuation, response and recovery. In this study, we attempt to analyze population movement by using mobile spatial statistics. In order to extract behavior patterns from the aggregated data, we use four different Latent Variable Analysis (LVA) methods – Independent Component Analysis (including FastICA and Spatial colored ICA), Non-negative Matrix Factorization (NMF), and Sparse Principal Component Analysis (SPCA) to analyze mobile statistics data of the disaster-affected area. The results indicate that each LVA methods has its pros and cons in extracting behavior patterns from the aggregated population. We conclude that, using multiple LVA methods and finding out the common patterns would be a robust way to understand and explain population dynamics. Finally, we argue that using mobile spatial statistics would be a feasible and practical option to estimate the dynamic change of human population after the occurrence of disasters.

https://www.sciencedirect.com/science/article/pii/S2212420921000133

The following paper is now available online!

Varghese, V. Chikaraishi, M., Urata, J. (2020) Deep Learning in Transport Studies: A Meta-Analysis on the Prediction Accuracy, Journal of Big Data Analytics in Transportation, Vol. 2, 199–220.

Abstract:
Deep learning methods are being increasingly applied in transport studies, while the methods require modellers to go through a try-and-error model tuning process particularly on choosing neural network structure. Moreover, the accuracy level also depends on other factors such as the type of data, sample size, region of data collection, and time of prediction. To efficiently facilitate such a model tuning process, this study attempts to summarize the relationship between the prediction accuracy of deep learning models and the factors which influence it. We conducted a comprehensive review of the literature by adopting a detailed search strategy, followed by a meta-analysis on prediction accuracy. Four separate linear mixed effects models, taking into account unobserved heterogeneities in prediction accuracy across studies, were developed to statistically test the impacts of influential factors on prediction accuracy for (a) all observations (136 studies; 2314 cases), (b) studies with MAPE, MRE, and average accuracy indicators (86 studies; 1,878 cases), (c) classification-based studies with accuracy indicator (29 studies; 220 cases), and (d) traffic forecasting studies with MAPE, MRE, and average accuracy indicators (36 studies, 991 cases). The final model includes additional factors to test the influence of sample size and time horizon of prediction variables. The findings showed that, as expected, deep learning models, particularly ones that consider spatiotemporal dependencies of transport phenomena, show better prediction accuracies compared to conventional machine learning models. We also found that, on average, the prediction accuracy is improved by 5.90% with 100 million additional data, while the accuracy is reduced by 5.28% with 100 min increase in time horizon of prediction in traffic forecasting studies. We concluded this paper with a comprehensive summary of the existing findings on the applications of deep learning to transport studies.

https://link.springer.com/article/10.1007/s42421-020-00030-z