During the COVID-19 pandemic, decreasing human mobility remains an essential measure despite the start of vaccinations due to the disease’s long incubation period and high infectivity. This study aims to investigate the effects of the 1st state of emergency, prefectural state of emergency, and 2nd state of emergency on human mobility during the COVID-19 pandemic based on the mobile spatial statistics and the pedestrian count data by applying the Bayesian structural time series model. First, we quantified the effects of different states of emergency regarding both under and after the declaration. Second, comparing the two data sets shows that the staying time reduced after the 1st state of emergency even though the number of pedestrians recovered the previous level. Third, the changes in human mobility consistent with the existing surveys on remote work and behaviors during the COVID-19 pandemic.

In this study, we conducted an interview survey and a message effect verification experiment using a cognitive function test in order to verify the effects and side effects of the psychological effects of the cognitive test on examinees.
As a result of the interview survey, most people said, “It was difficult.” after doing cognitive test regardless of the results. And someone compared with the past results. As a result of the message effect verification experiment by the cognitive test, those classified as (iii) had significantly lower values in 4 scales behavioral intention of car use reduction / license return after the test. These results indicate that the expected side effect was indeed in play. Those classified as (ii) had a significantly higher intention to return their driver's license after the test. From the above, the cognitive test had both a main effect and a side effect.

The effects of events that affect road traffic, such as new opening of expressways and toll changes, are generally grasped by comparing before and after indicators such as traffic volume and loss of time. However, there is a possibility that the change in the effect cannot be clearly grasped due to the influence of different day arrangements or the influence of incidents. On the other hand, past studies apply a state space model to traffic volume observed in time series, and classify them into long-term fluctuations, periodic fluctuations, and short-term fluctuations, and analyze traffic characteristics. Therefore, in this paper, we apply the method shown in previous studies, such as classifying the traffic volume observed in time series into each fluctuation component for the traffic situation observed on the Hanshin Expressway. In addition, we will try to clarify the effects of new expressways opening and toll change by grasping long-term fluctuations. We also consider the applicability of the method in practice.

This study focused on the effects of transportation environment of community on the exercise frequency, subjective health condition and subjective well-being. We adopted accessibility as the transportation environment. The accessibility is evaluated by public transport and automobile to three types of purpose. The variable of the exercise frequency, subjective health condition and subjective well-being were collected by the questionnaire survey. We used logistic regression for the analysis of relationship between the variables, which consider the difference of the community by the multilevel analysis. The result shows the accessibility of medical facilities by public transport and by car is significant on the health condition and frequency of going out, respectively. This result implies that the improvement of the accessibility by public transport and by car may affect the health condition of the community.

The SA/PAs on expressway are required to offer not only traditional rest stop services but also attractiveness of tourism destination as well as additional services, such as refreshments, dining options, and souvenir shopping. It is clear that these marketing strategies furthermore should take diversification of tourist's decision structure patterns underlying stopover behaviors at SA/PAs into consideration. The latent class analysis is applied to driver's behavior pattern data on Chugoku Expressway. The LCA models aims to represent how the drivers conditioned by those latent classes choose the location of SA/PA using the observed variables. The developed LCA models with covariate variables indicate an effective tool of market segmentation of these SA/PA stopover behavior patterns.

Managing the cost of the long term care service is critically important to provide the better service to the aged people in the hyper-aged society. In such management, we need to be sensitive in keeping the necessary services in cutting the cost. Therefore, identifying what determines the aged people's service user is necessary. Mobility would be one of the keys to the decision making in choosing the service, because most of the nursing care service is related to the transportation: Nursing service offers not only facility care services but home nursing care including shopping assistance. In this study, we investigated the relationship between the transportation environment and the use of the service. We focused on those who do not use nursing service, despite the certification of the long-term care, in order to find why they do not use. We analyzed what led one to use or not use the nursing care service by using multilevel logistic regression analysis, because it allowed us to consider the difference of the error structure. We applied the model to the data of a local city in Japan. The result shows that some variables indicating accessibility significantly affected one's decision whether one uses the service or not. The accessibilities of car to clinic, hospital and neighborhood shops significantly affected the decision, while only the accessibility to the hospital was significant when one uses public transport. More than 50 percent of the aged people use motorcars to travel in this city, and this might be why more number of the accessibility by using the car significantly affected the nursing service use. We might be able to manage the cost of service use more effectively and efficiently through the accessibility improvement. use. We might be able to manage the cost of service use more effectively and efficiently through the accessibility improvement. (C) 2017 The Authors. Published by Elsevier B.V.

The time series of travel time variation of highway can be observed easily by ICT devices such as ETC transaction record. This study focused on the travel time variation of a highway where the traffic congestion frequently occurs. This study assumes the cyclic fluctuation is stationary, then we analyzed the residuals of the travel time model. The travel time variation is modeled by ARMA process and the residuals are analyzed by the GARCH model. We proposed to use volatility as one index of travel time reliability and showed the variation of volatility during four years graphically. From that result, we show the characteristics of transition of the index.

In this study, we focused on a short-trip rideshare system including nonmonetary rewards supported by other-regarding preferences of altruistic drivers in a village community. A methodology for simulating various mechanism designs of the short-trip rideshare system was developed. Using numerical simulation, we studied ways to improve the sustainability of the system by providing informational guidance. As a result, we showed that a higher number of rideshare pairs were sustained by messages that shaped the expectations of driver candidates about the success of ridesharing. From our numerical example, we concluded that the conveyance of informational guidance by a system operator, which induced each driver candidate to take action towards their delayed matching in place of failure, was reasonable roughly three times out of ten to promote system sustainability from the perspective of both the number of rideshare pairs and the welfare level of all participants.

This study focused on modeling the indexes of individual built environment and their effects on the subjective evaluation of the life. The life satisfaction is regarded as the stable index for measuring happiness in the past researches in this decade. Thus we adopted it as the main subjective index in this study. The indexes of built environment of individual were measured as the number of available alternatives and the experience of activity abandoning, based on the idea of capability theory. The empirical study used the data collected in the mountain area. We found that the life satisfaction is not directly related with the number of alternatives but the experience of activity abandoning trip which means the margin of the capability is related.

About 1.3 million lose their life worldwide, and this is set to double by 2030 if status quo continues. It is the developing countries which account for overwhelming part of the current fatalities, and forecasted accident fatalities. There are local as well global initiatives to address the safety issues, in developing countries in particular. However, there are emerging issues in developing countries that may result in safety trend different from that of industrialized countries. This has huge policy implication as it may make innovative measures inevitable. Therefore, to address the safety issues effectively it is necessary to grasp its nature and trend in developing countries. This paper analyses the dynamics in traffic accident trend in Asian countries, re-evaluates the current traffic safety approaches for strategic policy recommendations for emerging trends in the region.

Abstract: In regions of Japan, in small, low-density communities that have inadequate public transport services, the lack of support systems for persons of limited mobility is emerging as a social issue. To complement this, this study focuses on volunteer-based dynamic ridesharing. In this paper, the feasibility of ridesharing is analyzed by pre-survey data and the availability is examined through a field experiment. The pre-survey showed that the field suited the ridesharing experiment, due to some results of a proper proportion between demanders and suppliers for rideshare, and a synchronicity of trip destinations and travel times, etc.. The experiment, however, revealed issues such as worry and hesitation of volunteer-based suppliers who have no opportunity to match other demanders except stable-matching pairs, incentives for their participation and their reservation, and sudden cancellation of the suppliers at the matching locations after their reservations. This paper describes improvements of the proposed ridesharing system.

The data on how travelers evaluate a specific sightseeing destination are useful for formulating tourism strategies. Making a survey is the most common way to get such data now, but the method has become difficult because travelers have become so diverse that the data are not as meaningful and substantial as they had been. To overcome this problem, Internet blogs are used: comments on sightseeing are so popular that it is easy to collect the needed data. Blog data on tourism were collected in Yatsugatake, Japan, and compared with the data obtained from ordinary questionnaire surveys to examine the features of blog data. Information from 192 blogs was collected. Analyzing the terminology used in the blogs allowed the determination of some attributes of their authors. These attributes were compared with those of questionnaire survey respondents. Although the desired information can be obtained from survey respondents, information from blogs depends on what the authors write about any destination. Nonetheless, because a blogger's destination is clear in his or her blog, the information about destinations can be used by co-occurrence analysis. With categorization of the entry tags of each blog, information about each blogger's interests can be obtained, and their preferences clearly indicate that their destination choices are closely related to their interests. In sum, although the information in blogs is subjective, if used properly the information can be effective in developing strategies for tourism by complementing data from ordinary surveys.

The shift of travel demand policies from the conventional supply oriented policies to management oriented policies has made the effectiveness of conventional trip-based modeling approaches, which still dominates the practice, questionable. In addition to this, the increasing finical burden of the conventional PT-surveying is making the sustainability of the model doubtful. The attempts so far to reduce the cost mainly focused on finding alternative surveying methods and techniques. In this paper, however, we investigate how shifting to alternative models, tour-based model in this case, may contribute in reducing the cost burden of the conventional PT-surveying approach. We will also investigate future prospect of this approach.

The shift of travel demand policies from the conventional supply oriented policies to management oriented policies has made the effectiveness of conventional models questionable. However, although the new generation travel demand models have theoretically appealing backgrounds, they have so far failed to fully attract the practitioners to apply them for practical purposes. Lack at proper understanding and wrong perceptions of the new generation models have contributed to the gap between research and practice. This paper suggests a way to incorporate the features of new generation models using ordinary PT (person trip) data, to get an optimum model. Based on this, we propose a model that captures the link among travel decisions of a day as a travel pattern. We also illustrate the practical advantages of the new generation models, comparing with conventional models, in evaluating policies by an empirical study.

Mobility, one of the key concepts to evaluate the effect of a transportation policy such as TDM and mobility management as well as to analyze the problems such as social exclusion, must be measured by how much of an intention to make a trip can be realized, not merely by how many trips are available. The advent of new communication tools such as the Internet and mobile phones has allowed one to accomplish certain tasks that previously required a trip. This new situation has brought up a discussion over the necessity to incorporate telecommunications as an aspect of mobility. Aware of such discussions, we analyzed the relationship between the number of trips and telecommunications based on the data we collected on trips, telecommunications, and activities, and found some significant correlations. Our study which used an ordered regression model found several significant relationships between the individual attributes and the number of trips/telecommunications. We formulated a model which assumes the latent factors among the trips and telecommunications. In addition, we found that the latent factors construable as intentions to trips and telecommunications could be measured better by e-mails than by trips. These results indicate that measuring mobility requires the inclusion of information about telecommunications. (C) 2009 Elsevier Ltd. All rights reserved.

While tourist has a variety of needs to get information and make a decision about their excursions in sightseeing areas, it becomes one of important subjects to identify the causal relationship between their excursion patterns and the obtained information. The relationship of information usage and excursion behavior was confirmed by using the structural equation model: information usage at the scheduling stage has an impact on tourist individual's Cognition for the sightseeing area, tourist individual's Cognition affects information usage at excursion stage, information usage at the excursion stage affect behavior of excursion. Therefore, modeling of the excursion behavior should consider the relationships mentioned above.

The purpose of this paper is to analyze the effect of the information uses on excursion activity patterns. The survey are conducted at the different types of sightseeing area: Fuji-Five Lakes area and Arashiyama area. First, two data sets from these surveys are empirically used for a comparative analysis to identify the basic characteristics of excursion patterns. Second, focusing on the Fuji-Five Lakes area data set the information use patterns by excursion pattern are analyzed by using the concept of timespace prism. Finally, the duration model for Kyoto-Arashiyama excursion behaviors is developed to evaluate the effect f the information use on the length of time-duration at a stop in a chain. This model indicates that the information uses significantly determine the parameters concerning the duration-time distribution.

In this study, our purpose is to identify a causal relationship in receptivity relating to the public involvement for current road improvement projects in Korea. Some of hypotheses about causal relationships among the psychological factors are assumed to develop a structural equation model. This model includes latent variables determining the receptivity relating to public involvement procedure, such as distrust feeling for administration (public sector), the knowledge and satisfaction about the road project, and the comprehensive faculty on the public involvement. The estimated model indicates that these assumed hypotheses are proved and that the administrative distrust strongly determines the receptivity as well as understanding of the road project and the knowledge of PI procedure.

The focus of this study is in two points. One is constructing a simulation model of tourists' excursion behavior considering scheduling. The other is proposal of an evaluation method for the activity simulation model. To explain the characteristics of the touring behavior, a model of activity sequence was developed. In this model, it is assumed that tourists choose a combination of visit areas and then select the first and the last spot independently. We developed a simulation model for whole tourist behavior using other several models and showed its result. We also develop an evaluation indicator for the model system. This indicator is based on the reenactment of activity patterns. We concluded the model system we proposed could simulate the behavior well under a specific situation.

The problem-solution-oriented method, capable of evaluating various measures reflecting future social needs and diversified values, will be the mainstream of the next generation transportation demand forecasting method. The activity-based researches have been explored from the viewpoint of. such problem solution. Of particular concerns in this paper are time use patterns involving non-workdays activity and travel behaviors. Using the activity diary survey conducted as a supplementary survey of the fourth person trip survey in Kei-Han-Shin metropolitan areas, a model of time allocation in a non-workday. which the time duration for each of eight types of activity is estimated subject to a time budget constraint, was developed. The proposed model was also examined concerning its validity as a transport-policy evaluation tool.

This paper aims to develop a model of location preference of manufacturing industry companies in response to the change in level of service of travel time caused by highway construction. Two types of location choice model are proposed: One is a Logit type model directly incorporating a function decreased by distance. The other is a Dogit type model that represents the area-specific component determined by this distance function. The results show that the estimated parameters for both of two models are statistically significant, while hit ratios are not enough. They also suggest that, as the correlation coefficients between land price and the estimated area attractiveness are not insignificant, the area attractiveness have relations with the potential of the areas.

This study proposes a new urban-travel demand forecasting system based on a micro-simulation approach as an alternative method to four-step estimation methods. This system comprises, 1) PCATS which simulates an individual's daily activity-travel pattern, 2) DEBNetS which simulates vehicle trips on urban road network, 3) a household simulator which generates future household attributes, and 4) a system to evaluate inter-zonal travel times and costs by public transit based on public transport networks and time tables. The system was applied to replicate the travel demand in a 1990 travel survey data set. A comparison between the observed and the forecast indicated that the system is capable of producing reasonable forecasts.

This Study is focused on the structural heterogeneity of perception. We adopt LISREL type model to calibrate perception structure and use the actual choice as the indicator of the heterogeneity. First, we segmented the population by actual choice. Next we modeled cognitive dissonance effect introducing the choice indicator into the measurement equation. The inertia effect is also tested by introducing the choice indicator into structural equation. In the case study of choice modeling with latent variables, the inertia effect model improve the fit measure more than the other model.

The objective of this research is to find factors that affect our recreational behavior by using disaggregate models for such trips. More specifically, a discrete-continuous model is proposed to explain the choice of the number of days spent for recreational trips in a year and the type of the trips. The model is applied to the national survey of recreational behavior to find that regional economic and recreational resources substantially affect the behavior.

This research proposes a methodology to quantitatively evaluate the impact of traveler's pre-trip information considering transaction cost for travel behavior changes. The transaction cost is defined as the difference of expected utilities which are observed before and after travel behavior changes, and is estimated using a discrete choice model which represents mode choice behavior with and without the information on travel time respectively. The model is estimated with mode choice RP data and SP data for hypothetical information provision. Furthermore, we empirically analyze the impact of pre-trip information provision to enhance new transportation services such as Park and Ride.

The purpose of this study is investigating efficient methods incorprating the heterogeneity of individual into the travel mode choice models. In this study, we propose two different market-segmentation approaches. One approach is a market segmentation by individual specific estimates obtained from a number of SP data of an individual. Another is a market segmentation by latent attitudinal variables. Then we applied these proposed methods to the mode choice SP data setting up virtual high-speed ferry and mode choice RP data. Finally, we got efficiently and interesting results, however some investigations are acquired.

This research proposes a method for analyzing demand of sightseeing trips to evaluate road network improvement projects. A model system of describing behavior of single-day sightseeing travel by auto is developed. The system consists of the scheduling model and the destinationroute choice model using the method of disaggregate discrete choice analysis. The model are estimated from questionnaire survey data on holiday trips. Using the proposed model system, we conducted demand forecasting for alternative improvement project on the road network.

This study is aimed to develop a dynamic discrete choice model with latent market segments using panel data. The model system consists of two models: a latent segmentation model and a choice model with serial correlation. This model system is applied to panel data of shopping place choice. The estimation results shows that the latent segmentation model significantly improved goodness of fit and the choice model yielded more efficient parameter estimates by taking into account of serial correlation.

Traditional discrete choice models for travel behavior use only manifest explanatory variables such as travel time and cost. Choice decisions, however, are strongly affected by latent factors such as comfort, safety, and reliability. Some authors have reported that using subjective ratings against those latent factors as explanatory variables gives better fit to the calibration data, but such an approach is rarely useful in forecasting. This paper proposes a methodology for incorporating choice data and subjective rating data to develop integrated models of choice behavior and psychological perception structure. The proposed models are based on the multiple-indicator-multiple-cause type model and the LISREL type model.