京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第17960号 / 工博第3808号 / 新制||工||1583(附属図書館) / 30790 / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 藤井 聡, 准教授 SCHMOECKERJan-Dirk, 准教授 神田 佑亮 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

The need for a more sustainable approach to water consumption has increasingly gained attention in the last decade. The domestic sector accounts for over half of abstracted water in the UK and, as such, has become a major target for water efficiency interventions. Current research and water efficiency interventions are dominated by a positivist approach, focusing on a limited range of factors that can be quantitatively measured. This thesis questions the dominant approach and argues that a more holistic overview of water efficiency can be achieved through the consideration of socio-technical and behavioural theories. Taking a more constructivist approach, this research draws on four theories from socio-technical and behavioural fields and combines them to create a framework for the analysis of water efficiency interventions. The framework is applied to two case studies, exploring water users¿ perceptions of water, water supply, personal water use, and their responses to the water efficiency interventions. The case studies were selected to provide examples of current mainstream approaches to water demand management. Research methods used included semi-structured interviews and observation. The research findings support the argument that the current dominant approach to domestic water efficiency interventions is limited and, in some cases, ineffectual. Issues of trust, knowledge, motivation and the relationships between water users and water companies were raised. The thesis concludes that the use of a constructivist perspective could help to provide a more effective approach to understanding and improving water demand management.

The number of trips on highways and urban networks has significantly increased in the recent decades in many cities across the world. At the same time, the road network capacities have not kept up with this increase in travel demand. Urban road networks in many countries are severely congested, resulting in increased travel times, increased number of stops, unexpected delays, greater travel costs, inconvenience to drivers and passengers, increased air pollution and noise level, and increased number of traffic accidents. Expanding traffic network capacities by building more roads is extremely costly as well as environmentally damaging. More efficient usage of the existing supply is vital in order to sustain the growing travel demand. Travel Demand Management (TDM) techniques involving various strategies that increase the travel choices to the consumers have been proposed by the researchers, planners, and transportation professionals. TDM helps create a well balanced, less automobile dependent transportation system. In the past, several TDM strategies have been proposed and implemented in several cities around the world. All these TDM strategies, with very few exceptions, are static in nature. For example, in the case of congestion pricing, the toll schedules are previously set and are implemented on a daily basis. The amount of toll does not vary dynamically, with time of day and level of traffic on the highway (though the peak period tolls are different from the off-peak tolls, they are still static in the sense that the tolls don't vary continuously with time and level of traffic). The advent of Electronic Payment Systems (EPS), a branch of the Intelligent Transportation Systems (ITS), has made it possible for the planners and researchers to conceive of dynamic TDM strategies. Recently, few congestion pricing projects are beginning to adopt dynamic tolls that vary continuously with the time of day based on the level of traffic (e.g. I-15 value pricing in California). Dynamic TDM is a relatively new and unexplored topic and the future research attempts to provide answers to the following questions: 1) How to propose and model a Dynamic TDM strategy, 2) What are the advantages of Dynamic TDM strategies as compared to their Static counterparts, 3) What are the benefits and costs of implementing such strategies, 4) What are the travel impacts of implementing Dynamic TDM strategies, and 5) How equitable are the Dynamic TDM strategies as compared to their Static counterparts. This dissertation attempts to address question 1 in detail and deal with the remaining questions to the extent possible, as questions 2, 3, 4, and 5, can be best answered only after some real life implementation of the proposed Dynamic TDM strategies. Two novel Dynamic TDM strategies are proposed and modeled in this dissertation -- a) Dynamic Congestion Pricing and b) Dynamic Highway Space Inventory Control System. In the first part, dynamic congestion pricing, a real-time road pricing system in the case of a two-link parallel network is proposed and modeled. The system that is based on a combination of Dynamic Programming and Neural Networks makes "on-line" decisions about road toll values. In the first phase of the proposed model, the best road toll sequences during certain time period are calculated off-line for many different patterns of vehicle arrivals. These toll sequences are computed using Dynamic Programming approach. In the second phase, learning from vehicle arrival patterns and the corresponding optimal toll sequences, neural network is trained. The results obtained during on-line tests are close to the best solution obtained off-line assuming that the arrival pattern is known. Highway Space Inventory Control System (HSICS), a relatively new demand management concept, is proposed and modeled in the second half of this dissertation. The basic idea of HSICS is that all road users have to make reservations in advance to enter the highway. The system allows highway operators to make real-time decisions whether to accept or reject travellers' requests to use the highway system in order to achieve certain system-wide objectives. The proposed HSICS model consists of two modules -- Highway Allocation System (HAS) and the Highway Reservation System (HRS). The HAS is an off-line module and determines the maximum number of trips from each user class (categorized based on time of departure, vehicle type, vehicle occupancy, and trip distance) to be accepted by the system given a pre-defined demand. It develops the optimal highway allocations for different traffic scenarios. The "traffic scenarios-optimal allocations" data obtained in this way enables the development of HRS. The HRS module operates in the on-line mode to determine whether a request to make a trip between certain origin-destination pair in certain time interval is accepted or rejected. / Ph. D.

This research explores the feasibility of providing innovative and effective solutions for traffic congestion. The design of reservation systems is being considered as an alternative and/or complementary travel demand management (TDM) strategy. A reservation indicates that a user will follow a booking procedure defined by the reservation system before traveling so as to obtain the right to access a facility or resource. In this research, the reservation system is introduced for a cordon-based downtown road network, hereafter called the Downtown Space Reservation System (DSRS). The research is executed in three steps. In the first step, the DSRS is developed using classic optimization techniques in conjunction with an artificial intelligence technology. The development of this system is the foundation of the entire research, and the second and third steps build upon it. In the second step, traffic simulation models are executed so as to assess the impact of the DSRS on a hypothetical transportation road network. A simulation model provides various transportation measures and helps the decision maker analyze the system from a transportation perspective. In this step, multiple simulation runs (demand scenarios) are conducted and performance insights are generated. However, additional performance measurement and system design issues need to be addressed beyond the simulation paradigm. First, it is not the absolute representation of performance that matters, but the concept of relative performance that is important. Moreover, a simulation does not directly demonstrate how key performance measures interact with each other, which is critical when trying to understand a system structure. To address these issues, in the third step, a comprehensive performance measurement framework has been applied. An analytical technique for measuring the relative efficiency of organizational units, or in this case, demand scenarios called network Data Envelopment Analysis (DEA), is used. The network model combines the perspectives of the transportation service provider, the user and the community, who are the major stakeholders in the transportation system. This framework enables the decision maker to gain an in-depth appreciation of the system design and performance measurement issues. / Ph. D.

This dissertation seeks to understand travel satisfaction, travel affinity, and other psychological factors in relation to travel demand, such as the desire for trip making, willingness to spend time traveling, and choice of travel mode. The research was based on the Mood State in Transport Environments survey of 247 Android users (about 6,000 completed trip surveys) in the Blacksburg-Roanoke, VA, Washington, DC, and Minneapolis, MN metropolitan areas from fall 2016 to spring 2018. Respondents answered an entry survey, tracked their travel for 7 days, and answered a trip survey associated with each trip. The dataset provides opportunities to examine travel and activities during travel at the within- and between-person levels. Three studies in this dissertation examined three measures of the positive utility of travel and their relationship with travel behavior. I quantified (1) the desirability of trip making, (2) the ideal travel time related to different travel characteristics, and (3) the effect of satisfaction on commute mode choice. The first study examines the patterns of travel affinity with various travel modes, trip purposes, and activities during the trip. Travel affinity was measured by asking the willingness to forgo a trip when there is an opportunity to do so. I found that this is a valid and strong measure of the positive utility of travel. Travelers were more willing to make trips when they traveled on foot or bicycle, talked with someone during the trip, and took shorter trips. Additionally, commute trips were less likely to be enjoyed as compared to other, non-commute trips. The second study focused on (1) testing the validity of the "ideal travel time" measurement and (2) measuring factors associated with the willingness to spend time traveling. I found that although ideal travel time was a strong measure of the positive utility of travel, it was very weakly associated with the desirability of trip making and satisfaction with trips. Although few people wanted zero commute time (3%), the number of trips that had zero ideal travel time was much higher (16%), indicating that the desired travel amount may vary across different trip and environmental characteristics and purpose. Ideal travel time was longer for active travel trips, leisure trips, when conducting activities during trips (e.g., talking, using the phone, looking at the landscape), when traveling with companions and during the weekend. The third study investigated the role of travel satisfaction and attitude in mode choice behavior. This is one of the very few studies that have considered the role of these psychological factors in multimodal mode choice based on revealed preference data. I found that satisfaction and attitude toward modes and travel played a significant role in the choice model; it also modified the role of travel time in the models. However, the perception of travel time usefulness was insignificant in the model. Scenario analyses based on the model results showed that it is optimal to invest in active transportation and public transit at the same time in order to shift car drivers to these sustainable modes. These studies contribute to the small but growing body of literature on the positive utility of travel and transrational decision making in transportation. It is the only study that employed a smartphone survey with a repeated measure of trips over the course of 1-2 weeks. The third study is among the earliest attempts to include satisfaction and attitude together into mode choice models. This dissertation has several implications for research and practice. First, it calls for better measurements of well-being and satisfaction. Second, models with appropriate psychological factors would more realistically resemble actual travel behavior. Including satisfaction in the choice model changes the coefficient of travel time (and potentially cost), which modifies the value of travel time savings, a basis of most benefit-cost analyses in transportation planning and engineering. Better mode choice and trip generation models will generate more reliable predictions of future infrastructure use and investment. Third, studies of travel affinity (positive utility of travel) have implications for demand modeling and management practice. Practitioners should reevaluate the effectiveness of travel demand management strategies aimed at reducing travel time and trips, such as congestion pricing (e.g., tolls), online shopping, and telecommuting. / Doctor of Philosophy / People have various motivations to travel every day. For some, traveling is a means to an end to get from one place to another. Their main travel purpose is to perform some activities at destinations, such as grocery shopping, working, or visiting a friend. For others, traveling is a joy to get some fresh air, to be on one’s own company, to enjoy driving or exercising (while walking or bicycling), in addition to conducting activities at destinations. This idea of traveling for fun is still unpopular in transportation research. This dissertation seeks to understand the patterns of travel and motivations: who are traveling for fun, and when? Whether this affinity and satisfaction for travel drive people’s decision to choose a travel mode? To answer these questions, I measured the affinity for travel in two ways: willingness to make trips (i.e., travel from one place to another) and desired amount of time spent on travel. I found that people were willing to travel more when they conducted certain activities during trips, such as talking to others, talking on the phone, or other activities. Commuting was less fun as compared to other travel purposes, such as socializing or leisure. Bicyclists and pedestrians liked their trips and wanted to travel more than car drivers and bus users. People who were satisfied with their commute trips made by one mode would be more likely to use that mode for commuting. The affinity for travel is relevant to urban residents’ mental well-being and demand for travel, which translate into health and congestion relief benefits. The results from my studies suggest that more attentions on traveling for fun and multitasking should be paid to account for future mobility options, such as ride hailing (e.g., Uber, Lyft) and autonomous vehicles. These modes have promised fun from activities during travel, the autonomy, and convenience, and thus would generate more traffic on the road while providing less social and environmental benefits. The results from this dissertation would inform city planners, engineers, and health practitioners on planning for sustainable cities by improving well-being for transportation users and accommodate sustainable modes of transport, such as bicycling, walking, and transit by providing users with safe and satisfactory travel environments. The results also imply potential pitfalls of the current planning practice such as overestimating the value of travel time savings, benefit-cost analyses, and the effectiveness of travel demand management strategies, such as telecommuting and using information and communications, in reducing travel.

Departments of Transportation (DOTs) are responsible for keeping their road network in a state of good repair while also aiming to reduce congestion through the implementation of different traffic control and demand management strategies. These strategies can result in changes in traffic volume distributions, which in turn affect the level of pavement deterioration due to traffic loading. To address this issue, this dissertation introduces an integrated simulation-optimization framework that accounts for the combined effects of pavement conditions and traffic management decision-making strategies. The research focuses on exploring the range of possible performance outcomes resulting from this integrated modeling approach. The research also applied the developed framework to a particular traffic demand management strategy and assessed the impact of dynamic tolls around the specific site of I-66 inside the beltway. The integrated traffic-management/pavement-treatment framework was applied to address both the operational and pavement performance of the network. Aimsun hybrid macro/meso dynamic user equilibrium experiments were used to simulate the network with a modified cost function taking care of the dynamic pricing along the I-66 tolled facility. Furthermore, the framework was expanded to include the development of a systematic and comprehensive methodology to optimize the allocation of networkwide pavement treatment work zones over space and time. The proposed methodology also contributed to the development of a surrogate function that reduces the optimization computation burden so that researchers would be able to conduct work zone allocation optimization without having to run expensive simulation work. Finally, in this dissertation, a user-friendly decision-support tool was developed to assist in the pavement treatment and project selection planning process. We use machine learning models to encapsulate the simulation optimization process. / Doctor of Philosophy / Departments of Transportation (DOTs) are responsible for keeping their road network in a state of good repair. Improvement in pavement rehabilitation plans can lead to savings in the order of tens of millions of dollars. Pavement rehabilitation plans result in work zone schedules on the transportation network. Limited roadway capacities due to work zones affect traffic assignments and routing on the roads, which impacts the selection of optimal operation strategies to manage the resulting traffic. On the other hand, the choice of any particular operation and routing strategy will result in different distributions of traffic volumes on the roads and affect the pavement deterioration levels due to traffic loading, leading to other optimal rehabilitation plans and corresponding work zones. While there have been several research efforts on infrastructure condition assessment and other research efforts on traffic control and demand management strategies, there is a wide gap in the nexus of the two fields. To address this issue, this dissertation introduces an integrated simulation-optimization framework that accounts for the combined effects of pavement conditions and traffic management decision-making strategies. The research focuses on exploring the range of possible performance outcomes resulting from this integrated modeling approach. The research also applied the developed framework to a particular traffic demand management strategy and assessed the impact of dynamic tolls around the specific site of I-66 inside the beltway. The integrated traffic-management/pavement-treatment framework was applied to address both the operational and pavement performance of the network. Furthermore, the framework was expanded to include the development of a systematic and comprehensive methodology to optimize the allocation of networkwide pavement treatment work zones over space and time. The proposed methodology also contributed to the development of a surrogate function that reduces the optimization computation burden so that researchers would be able to conduct work zone allocation optimization without having to run expensive simulation work. Finally, in this dissertation, a user-friendly decision-support tool was developed to assist in the pavement treatment and project selection planning process. We use machine learning models to encapsulate the simulation optimization process.

System dynamics can be a very useful tool to expand the boundaries of one's mental models to better understand the underlying behavior of systems. But despite its utility, there remains challenges associated with system dynamics modeling that the current research addresses by expanding the system dynamics modeling toolbox. The first challenge relates to imprecision or vagueness, for example, with respect to human perception and linguistic variables. The most common approach is to use table or graph functions to capture the inherent vagueness in these linguistic (qualitative) variables. Yet, combining two or more table functions may lead to further complexity and, moreover, increased difficulty when analyzing the resulting behavior. As part of this research, we extend the system dynamics toolbox by applying fuzzy logic. Then, we select a problem of congestion pricing in mitigating traffic congestion to verify the effectiveness of our integration of fuzzy logic into system dynamics modeling. Another challenge, in system dynamics modeling, is defining proper equations to predict variables based on numerous studies. In particular, we focus on published equations in models for energy balance and weight change of individuals. For these models there is a need to define a single robust prediction equation for Basal Metabolic Rate (BMR), which is an element of the energy expenditure of the body. In our approach, we perform an extensive literature review to explore the relationship between BMR and different factors including age, body composition, gender, and ethnicity. We find that there are many equations used to estimate BMR, especially for different demographic groups. Further, we find that these equations use different independent variables and, in a few cases, generate inconsistent conclusions. It follows then that selecting a single equation for BMI can be quite difficult for purposes of modeling in a systems dynamics context. Our approach involves conducting a meta-regression to summarize the available prediction equations and identifying the most appropriate model for predicting BMR for different sub-populations. The results of this research potentially could lead to more precise predictions of body weight and enhanced policy interventions to help mitigate serious health issues such as obesity. / Ph. D.

No / There is widespread agreement among agencies governing UK water management that more extensive domestic water metering combined with additional measures will deliver a more efficient domestic water usage. This paper argues that qualitative research is needed to select and hone additional measures. According to theory, cooperation to reduce water use is more likely if people: a) believe in the necessity to reduce use; b) feel costs are fairly shared; and c) believe their actions can affect the situation. The case study of Lydd, Kent, is presented. Lydd is the first location in which compulsory water metering has been imposed in the UK. Qualitative information was collected to inform the communication strategies being implemented by the water supply company. The investigation found that none of the three factors predicted by theory were completely present. The paper concludes by providing some recommendations for improving the water company's communications strategy for encouraging a reduction in domestic water use. The key role of qualitative information in assisting in the targeting and design of water demand management programmes is highlighted.

The study investigates the contribution of WDM towards mitigating scarcity of potable water in cities with particular reference to Bulawayo.WDM origins and its successes are traced. The study classifies scarcity representations into four categories and reveals that the scarcity in Bulawayo satisfies all the four representations hence calls it total scarcity. The research employed document study, questionnaires, interviews and a focus group to collect data. Document study revealed that water restrictions successfully mitigate the scarcity in Bulawayo. Field work partially confirmed the usefulness of WDM in the life of the city and revealed the need to synchronize the conceptualizations of WDM among different stakeholders in order for the paradigm to do even more in terms of mitigating scarcity. The results were interpreted in terms of TPB. The key recommendation of the study is that the city invests in water use behaviour change in order to realise huge water savings. / Development Studies / M.A. (Development Studies)

The study investigates the contribution of WDM towards mitigating scarcity of potable water in cities with particular reference to Bulawayo.WDM origins and its successes are traced. The study classifies scarcity representations into four categories and reveals that the scarcity in Bulawayo satisfies all the four representations hence calls it total scarcity. The research employed document study, questionnaires, interviews and a focus group to collect data. Document study revealed that water restrictions successfully mitigate the scarcity in Bulawayo. Field work partially confirmed the usefulness of WDM in the life of the city and revealed the need to synchronize the conceptualizations of WDM among different stakeholders in order for the paradigm to do even more in terms of mitigating scarcity. The results were interpreted in terms of TPB. The key recommendation of the study is that the city invests in water use behaviour change in order to realise huge water savings. / Development Studies / M.A. (Development Studies)