The popularization of electric vehicles (EVs) is limited by their driving range and long charging times. To address this, in-motion wireless power transfer systems (WPTSs) are currently attracting attention as a new power supply system. In-motion WPTSs have coils embedded under the road to transfer power from the WPTSs to EVs while driving. However, the main drawback of WPTSs is their large investment, especially in supporting the long-distance trips of EVs on expressways. Therefore, this study proposes a new mixed-integer programming model (MIP) to determine the optimal location of WPTSs for maximized total feasible flow demand. By focusing on long-distance trips on expressways, we propose the first flow-capturing model for WPTS locations that can (i) solve for the distance of WPTS installed as continuous variables, and (ii) solve problems based on real-scale data using a general MIP solver. Our method is extended to a discussion of WPTS installations on expressways in Japan. We observe that WPTS has strong potential as an EV power supply system in terms of coverage and economic rationality. In particular, WPTS has economic rationality not only in busy networks but also in sparsely populated networks that connect urban and rural areas. Thus, this study clarifies the important insights of WPTSs in improving their effectivity to narrow down the demand and ensure the flexibility in the locations of WPTS.

As environmental issues become more prominent, electric vehicles (EV) have attracted an increasing amount of attention. However, the continuous cruising distance of an EV is limited to approximately 160 km, which is insufficient for everyday use. Battery capacity is the limiting factor in long-distance EV travel, because the vehicles need to stop at EV recharging stations multiple times. In Japan, there are more than 2,000 EV charge stations, but there are, at most, two rapid chargers. When multiple users converge at the station, a queuing (or waiting) condition is created, which may lead to a call-loss condition. In other words, an appropriate number of chargers must be installed at each station when planning the EV infrastructure. Therefore, the number of vehicles entering the station must be estimated. In this study, a mathematical model based on the supporting infrastructure for widespread EV use is proposed to estimate the number of vehicles arriving at each charge station.

Transactions in financial markets are not evenly spaced but can be concentrated within a short period of time. In this study, we investigated the factors that determine the transaction frequency in financial markets. Specifically, we employed the Hawkes process model to identify exogenous and endogenous forces governing transactions of individual stocks in the Tokyo Stock Exchange during the COVID-19 pandemic. To enhance the accuracy of our analysis, we introduced a novel EM algorithm for the estimation of exogenous and endogenous factors that specifically addresses the interdependence of the values of these factors over time. We detected a substantial change in the transaction frequency in response to policy change announcements. Moreover, there is significant heterogeneity in the transaction frequency among individual stocks. We also found a tendency where stocks with high market capitalization tend to significantly respond to external news, while their excitation relationship between transactions is weak. This suggests the capability of quantifying the market state from the viewpoint of the exogenous and endogenous factors generating transactions for various stocks.