In this research, we considered the issues, limits and the improvement policies of the port plan systems for waterfront redevelopment. We analyzed the past redevelopment cases from various viewpoints based on documentary reviews and the results of questionnaire survey to port managers, and checked the consistency of the plans before and after redevelopment, and consequently identified the issues in the institutional frameworks about the adopted planning methods. In conclusions, it became clear that the redevelopment scale of less than 80-100ha and the project cost of less than 100 billion yen are indications for port plan to be completed within the target year of redevelopment project. In addition, we pointed out that as improvement policies on the port plan systems it is important to make the rigid procedure to include the project start time, the completion time, the responsible organization, and the expected achievement, and so on, in the planning matters when the redevelopment project is referred in a port plan.
This study provides an efficient method for solving two-dimensional FO model (Fujita and Ogawa, 1982). We first formulate the FO model as a quadratic programming problem (QP). Applying Benders’ decomposition, we then decompose the QP into a “master problem” and a “sub-problem.” The former obtains the firm’s location, while the latter obtains the household’s commute pattern (i.e. O-D demand for all possible pair of locations). Since the number of the unknown variables of the sub-problem thus should be the square order of the number of locations, it is extremely difficult to solve the sub-problem in a straightforward manner for the case with 10,000 (= 100 × 100) locations. In this article, we thus clarify that the sub-problem is equivalent to the Hitchcock’s transportation problem. This enables us to develop an efficient solution method, which reduces the number of the basic variables to the order of the number of the locations. Finally, some numerical examples exhibit that the proposed method can solve a large-scale FO model with more than 20,000 locations within a practical time.
This study aims at establishing the analytical and empirical framework for evaluating user beneﬁt for improving train delay and punctuality based on scheduling-based approaches mainly developed in the literature of travel time reliability. We extend the approach for train commuters’ boarding time choice by incorporating the disparity between actual and scheduled travel time as well as journey travel time variability and estimated scheduling parameters and value of travel time variability by jointly using Tokyo’s urban rail commuter data and train operation data. Then we conduct the computation of user economic beneﬁt for the scenario study of dispersing the work start time distribution based on the train operation simulation model and the calibrated scheduling model of commuters. We ﬁnd that the computed user beneﬁt is about 23 JPY/passenger/day when we set a speciﬁc value of travel time saving obtained from a literature.
It is a well-known fact that the real estate market is segmented geographically. This phenomenon has attracted much research interest, and attempts have been made to extract regions where valuation standards are the same. Most studies presume a division structure according to specific geographical units. However, they might have failed to extract the actual condition of geographic segmentation because the real estate market has a hierarchical division structure, ranging from the municipality level to the neighborhood level.
This study proposes a new approach to identify geographical segmentation of the real estate market. We construct a real estate rent model with several regional explanatory variables that depend on different spatial resolutions and then implement the generalized fused lasso—a regression method for promoting sparsity—to extract regions where the valuation standard is the same. The proposed method is applied to the rent data of apartments in the Tokyo metropolitan area, and it confirmed the applicability of the proposed approach. The result indicates the characteristics of the real estate market in Tokyo: multi-scale segmentation occurs in the whole area, and neighborhood-level segmentation is particularly notable at the city center.
In this article, we develop a novel numerical method for solving dynamic user equilibrium trafﬁc assignment model with simultaneous departure time and route choice (DUE-SDR) model. We ﬁrst formulate the DUE-SDR model on either a one-to-many or a many-to-one network with a point-queue model as a mixed linear complementarity problem (MLCP). Our analyses reveals that the MLCP reduced to a quadratic programming (QP), which enables us to obtain the DUE-SDR assignment by solving the equivalent QP by using the conventional Frank-Wolfe algorithm. Several numerical examples exhibit that the proposed method solves the DUE-SDR model with about 20,000 unknown variables in a practical time.