This study develops an urban economic analysis method that can assess the long-term effects of social infrastructure developments within a city. To this end, we construct a model that considers agglomeration economies and that can explain the endogenous formation of polycentric urban configurations. We then present an analytical method for efficiently obtaining stable equilibria in the model. Furthermore, to demonstrate the applicability of this analysis method, we conduct a counterfactual analysis on the improvement of commuting costs for the Kanazawa urban employment area. The results show that the population distribution changes reflect the model characteristics.

 This study focuses on buses which comprise one of the most important forms of public transportation in local cities. Targeting 58 local cities, we statistically analysed the relationship between the existence of bus stops and bus frequency as of 2010, population composition ratio in 2010 and 2015, and the social change rate for 5 years by age groups in units of 500m mesh.

 Results were clarified using multiple comparison tests. They show that in relation to the population composition ratio at the two time points, the ratio of elderly people in the mesh with low bus convenience is larger than that with high bus convenience. Furthermore, in relation to the social change rate for 5 years, the mesh with high bus convenience, which had a frequency of 4 buses or more per hour, can suppress the number of teens leaving compared to other meshes. However, it was revealed that the number of those in their 20s moving in could not be promoted.

 Crowdsourced delivery (CSD) is a system wherein ordinary drivers make deliveries during their own trips. While the CSD system can reduce delivery costs, it needs to match drivers and shippers accurately, which is very difficult because of two reasons: (i) drivers’ willingness to accept and shippers’ willingness to pay for the delivery are private information, and (ii) matching drivers with shippers is a large-scale integer programming problem. To overcome these issues, we propose an efficient driver-and-shipper matching mechanism. In the proposed mechanism, we first decompose the large-scale matching problem into two-stage hierarchical problems. This decomposition creates sub-problems which are small-scale, easy-to-solve integer programming problems. This sub-problems are solved with Vickrey-Clarke-Groves (VCG) auction, in which the drivers’ willingness to accept and shippers’ willingness to pay for the delivery are revealed. Next, by continuous approximation of the master-problem, we formulate the master-problem in closed form. Moreover, we solve the master-problem by using dynamic programming, so that we can resolve the second issue of the CSD system. Numerical experiments conducted using our mechanism show that the proposed algorithm drastically improves computational efficiency.

 This study proposes a data collection, modeling and simulation method to describe the endogenous generation mechanism of social networks considering activity participation. The key characteristics of the proposed methods are (1) the joint use of a name generator (to elicit social networks) and an activity diary as a feasible survey method, (2) the overcoming of the limitations of the partial observation of the social networks generation process through modeling and simulation, and (3) the description of population-level network endogenous generation mechanism based on empirical data. In addition, an empirical analysis is conducted using data from newly arrived international students in Japan, where factors affecting network formation of international students were identified, and the effects of a set of policies on network formation evaluated quantitatively through simulation.