Makoto Chikaraishi

Exploring Variation Properties of Time Use Behavior on the Basis of a Multilevel Multiple Discrete-Continuous Extreme Value Model

This study attempts to examine the variation properties of time use behavior on the basis of a multilevel multiple discrete–continuous extreme value model, which describes both activity participation and time allocation behavior by incorporating various variance components. Five major variation components are dealt with, including interindividual, interhousehold, temporal, spatial, and intraindividual variations. The MobiDrive data, continuous 6-week daily travel data, allow identification of these variations simultaneously. Two types of models are empirically examined: the model without consideration of the influence of explanatory variables (null model) and the model that introduces explanatory variables (full model). Estimation results of the null model confirm that intraindividual variation still accounts for more than 50% of the total variation (except for mandatory activities) even after incorporation of the aforementioned four other types of variations jointly. In contrast, the results from the full model reveal that most types of unobserved variations (especially the intraindividual variation) still dominate in the total variation even after the relevant observed information is introduced. These findings provide useful insights into both model development and data collection methods as well as to the understanding of the mechanisms of time use decisions.

Changes in Variations of Travel Time Expenditure

To improve methods of evaluating the benefits from investments in the transport infrastructure, this paper attempts to develop a method for capturing changes in variations in travel time expenditure and shows empirical results from data from the German Mobility Panel, a multiday and multiperiod panel survey. The proposed methodology can simultaneously and explicitly deal with changes and variations. The empirical analysis indicates that over time, travel time expenditures become more dependent on situational attributes than on individual or household attributes; such dependence implies that travel time expenditures change toward diversification and that longer periods of behavioral observation to describe peoples travel time expenditures become more important. The results also imply that understanding of the dynamic aspects of behavior is still important and remains a challenging issue to be revisited, not only for improving forecasting models but also for reconsidering the benefits from investment in transportation infrastructure.

Effects of nursing care on time use behaviour: a comparative analysis in Japan

This study attempts to clarify differences in Japanese time use behaviour for people with/without responsibility for nursing care of family members by comparing the behaviour of three population groups: in-home caregivers, out-of-home caregivers and non-caregivers. The analysis is also conducted with respect to the behaviour on weekdays and weekends. After examining the differences of time use behaviour based on simple statistical tests, we develop a multilevel multiple discrete-continuous extreme value model, which simultaneously represents activity participation and time allocation. In the model, observed variations, unobserved inter-individual variations and unobserved spatial variations are distinguished. Using large-scale national time use data collected in Japan, the analysis confirms various differences of time use behaviour across the three groups, especially between in-home caregivers and non-caregivers.

Mobility of the Elderly

This chapter briefly overviews studies on mobility of the elderly with a particular focus on its conceptualization, measurement, and evaluation. The role of mobility in the everyday life of the elderly is first explored through putting mobility discourses into a broader context. After clarifying hierarchy of travel needs, interdependencies between mobility and other life domains, and the linkage between mobility and well-being are discussed. Then, policy aspects related to mobility of the elderly are examined, aiming to draw on the potential conflicts that exist among different perspectives including social welfare, economy, and urban planning. Finally, this chapter points out the needs for conducting further cross-cutting empirical studies, establishing a clearer linkage between conceptual framework and empirical framework, developing a simple and standardized method to collectively show the importance of social aspects of transport, and exploring the potential changes in the role or position of the elderly in future.

Optimal Sampling Designs for Multi-Day and Multi-Period Panel Surveys

Abstract Purpose — This study proposes an optimal survey design method for multi-day and multi-period panels that maximizes the statistical power of the parameter of interest under the conditions that non-linear changes in response to a policy intervention over time can be expected. Design/methodology/approach — The proposed method addresses balances among sample size, survey duration for each wave and frequency of observation. Higher-order polynomial changes in the parameter are also addressed, allowing us to calculate optimal sampling designs for non-linear changes in response to a given policy intervention. Findings — One of the most important findings is that variation structure in the behaviour of interest strongly influences how surveys are designed to maximize statistical power, while the type of policy to be evaluated does not influence it so much. Empirical results done by using German Mobility Panel data indicate that not only are more data collection waves needed, but longer multi-day periods of behavioural observations per wave are needed as well, with the increase in the non-linearity of the changes in response to a policy intervention. Originality/value — This study extends previous studies on sampling designs for travel diary survey by dealing with statistical relations between sample size, survey duration for each wave, and frequency of observation, and provides the numerical and empirical results to show how the proposed method works.

Spatiotemporal Changes of Traffic Demand Price Elasticities in Japan Throughout 2008 Bayesian Approach

This study attempts to examine spatiotemporal changes of traffic demand elasticities regarding gasoline prices and economic conditions and focusing on the substantial fluctuations that occurred throughout 2008 in Japan. This research is aimed at a better understanding of the spatial and temporal instability of the elasticities and possible reasons for changes in the elasticities. First, a random coefficient model is built to represent spatial heterogeneity in the elasticities and is estimated by using a hierarchical Bayesian method. Next, a sequential Bayesian updating method is applied to examine monthly changes in the elasticities. The data used in the empirical analysis were monthly traffic volume data collected on 53 expressways. The results showed that although the monthly changes in the average elasticities over all routes were mainly observed before August 2008, different directions of change across routes were observed after September 2008, when gasoline prices began to fall. The results also indicated that responses to gasoline price changes might be dependent on the causes of price changes. Furthermore, on urban expressways, it was found that once a reduction in traffic demand was attained because of rising gasoline prices, the demand did not fully recover even after the actual prices fell again to the original level.

The Car-Dependent Life

This chapter focuses on car dependence in people’s life. The authors first describe a new phenomenon about the decline in young people’s car ownership and usage by providing additional facts and insights based on literature review and a case study in Japan. Especially, the case study in Japan uses data from a longitudinal national household expenditure survey and confirmed that car ownership and usage decisions in Japan are more or less associated with decisions about other household expenditures. Second, recent research on shared mobility is reviewed from the perspective of smart use of cars. Third, existing studies on cars from the life-oriented consideration are described by looking at shopping behavior (both store-shopping and online shopping) and general purchasing behavior as well as electric vehicle ownership and usage. As for electric vehicles, the influence of lifestyle is explored. Fourth, car ownership and usage for an inclusive society are discussed, where low-income persons, children, and the elderly are focused on. Fifth, behavioral changes toward less dependence on car from a long-term perspective are illustrated. Finally, discussions on car dependence from the life-oriented perspective are given.

Vibration distribution measurement using a high-speed multithread active vision

This study proposes a concept for multithread active vision sensing that can measure dynamically changing displacement and vibration at multiple points on civil engineering structures. In multithread active vision sensing, a high-speed camera can function virtually as multiple tracking cameras by accelerating its measurement, computation, and actuation with ultrafast viewpoint switching at millisecond level. We developed a galvano-mirror-based high-speed multithread active vision system that can switch 500 different views in a second; it functioned as 15 virtual cameras each operating at 33.3 fps to observe multiple scenes in completely different views. The experimental results for a 4-m-long truss-structure bridge model to which 15 markers were attached show that a single active vision system can observe the deformation of the bridge structure and estimate modal parameters, such as resonant frequencies and mode shapes, at a frequency on the order of dozens of Hertz.

Mediation Effects of Income on Travel Mode Choice

Effects of income on travel behavior have been widely examined in the literature; however, a majority of existing studies focused mainly on the direct effects of income and neglected the indirect effects. This focus is especially true in the context of developing countries in which income per capita is increasing rapidly. Effects of income on travel behavior may be observed through its impacts on other life choices. Such indirect effects are called “mediation effects” in this study. To fill the foregoing research gap, this study focused on travel mode choice in the Hanoi metropolitan area of Vietnam and developed a path model with multiple discrete choices. In the path model, mediation effects of income on travel mode choice were captured by modeling residential location choice as a discrete mediator that generated nonlinear indirect effects of income on mode choice (the proposed model is called the “mediation model”). As a comparison, a joint model of residential location and travel mode choices was built with only direct effects of income on both types of choice. Based on data collected at three new urban areas in Hanoi in 2015, model estimation results confirm the existence of both direct effects and mediation effects of income on mode choice. Simulation analyses further show that as income increases, the share of motorcycles sharply decreases up to a certain level of income; however, it rises after that level.

Parking: Learning from Japan

The discussion of reforming parking pricing in Europe and the US suffers from a lack of imagination, as it is hard to envision a city with spatially and temporally strong variance in parking prices within short ranges of distance given the Western historical precedent. This paper is intended to help spark the imagination by presenting the case of downtown Hiroshima, where such a regulatory framework has already been in place for decades. It will map and describe the outcomes for the core of this metropolitan area of just over one million inhabitants. This description is supported by an explanation of the regulatory framework and a chronicle of the development of the number of parking spaces across the country. Parking prices vary by a factor of 25 during daytime hours on weekdays within this 8-km area. The highest rates shift location by 1.25 km from daytime to nighttime. Rates fall to zero where there is no demand.