Bicycle planning is a current topic for planning for sustainable travel, both in Sweden and internationally. The purpose of this study is to investigate how bicycle planning can be implemented to make cities more sustainable. The study will examine Cykellänken in Linköping municipality to answer the questions: How has Linköping municipality planned for Cykellänken? How does the bicycle planning of Cykellänken meet the safety and accessibility aspects? The theoretical framework consists of the concept Travel demand management, security and accessibility aspects. The methods are interviews and observations. The conclusions are that there are some general improvement measures, which are safer crossings, reduced power play between vehicles, reduced speed, improved marketing and information and more evaluations. / Cykelplanering är ett aktuellt ämne för att planera för hållbart resande, både i Sverige och internationellt. Syftet med studien är att undersöka hur cykelplanering kan genomföras för att städer ska bli mer hållbara. Studien kommer undersöka Cykellänken i Linköpings kommun för att svara på frågeställningarna: Hur har Linköpings kommun planerat för Cykellänken? Hur uppfyller cykelplaneringen av Cykellänken aspekterna säkerhet, tillgänglighet och framkomlighet? Det teoretiska ramverket består av konceptet Travel demand management, samt aspekterna säkerhet, tillgänglighet och framkomlighet. Metoderna för studien är intervjuer och observationer. Slutsatsernaär att det finns några generella förbättringsåtgärder, vilket är säkrare korsningar, reducera maktspelet mellan bil och cykel, sänkt hastighet, förbättrad marknadsföring och information samt fler utvärderingar.

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

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19979号 / 工博第4223号 / 新制||工||1653(附属図書館) / 33075 / 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 SCHMOECKER Jan-Dirk, 准教授 宇野 伸宏 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Increasing vehicular travel and environmental issues are trends increasing the pressure on urban transport systems. The new concept Mobility as a Service (MaaS) is one approach to tackle these challenges. The aim of MaaS is to reduce the need of the private car and increase the use of shared resources by providing one single application that integrates all transport modes, payment, and services. Several ongoing societal trends such as urbanization, technologic development, and sharing economy are examples of reasons for the emergence of MaaS. Young people are often leading the development and are early adopters of new technologies. The mobility demands of young people today and in the future will therefore play an important role in the implementations of MaaS. This qualitative study uses focus groups to explore mobility demands today and in the envisioned future of young people (aged 15-23) living in Stockholm. Our understanding of travel behaviour and mobility demands among young people is limited, and this study also aims to get deeper understanding of the underlying values and attitudes towards mobility that influence those demands and behaviours. This study aims to answer how young people’s mobility demands look like and if they correspond to the visions of MaaS as stated by developers and experts of the concept. Findings indicate that mobility demands and behaviour are strongly influenced by parents, and underlying values such as ‘Freedom’ and ‘Comfortable life’ affect mobility more than factors such as availability and travel time. There is little need for increased accessibility to transport today and in the envisioned future. Findings also indicate a relatively widespread environmental awareness, but an unwillingness to convert awareness to change of behaviour. The demands of young people correspond to some extent with the visions of MaaS, and the probability of young people to adopt MaaS and to choose environmentally friendly journey is high if demands for convenience and comfort are fulfilled.

The aim of this study was to develop a framework to model travel choice behavior in order to estimate intercity travel demand at nation-level in the United States. Nested and mixed logit models were developed to study national-level intercity transportation in the United States. A separate General Aviation airport choice model to estimates General Aviation person-trips and number of aircraft operations though more than 3000 airports was also developed. The combination of the General Aviation model and the logit models gives the capability to estimate a full spectrum of intercity travel demand in the United States. The logit models were calibrated using a nationwide revealed preference survey (1995 American Travel Survey). Separate models were developed for business and non-business trip purposes. An airport choice model is integrated into the mode choice model to estimate both the market share between any origin-destination pair and other modes of transportation, and the market share split between airports associated with the origin-destination pairs. The explanatory variables used in the utility functions of the models are travel time, travel cost, and traveler's household income. The logit models are used to estimate the market share of automobile and commercial air transportation between 3091 counties and 443 commercial service airports in the United States. The model was also used to estimate market share for on-demand air taxi services. Given an input county-to-county trip demand table, the models were used to estimate county-to-county travel demand by automobile and commercial airline between all counties and commercial service airports in the United States. The model has been integrated into a computer software framework called the Transportation Systems Analysis Model (TSAM) that estimates nationwide intercity travel demand in the United States. / Ph. D.

This dissertation is a collection of several models to understand airline and passenger dynamics in the National Airspace System (NAS). Agent-based modeling is one of the most widely used modeling simulation-analysis approaches to understanding the dynamic behavior of complex systems. The usefulness of agent-based modeling has been demonstrated by simulating the complex interactions between airlines, travelers, and airports of a small-scale transportation system. Three airlines, one low cost and two network airlines are simulated to examine how each airline behaves over time to maximize their profit margins for a given passenger demand and operation cost structure. Passenger mode choice and itinerary choice sub modules are embedded in the framework to characterize traveler agent's response to the evolved airline schedule. An airport delay model was implemented to estimate the average delay at each airport. The estimated delay fed into the mode choice and itinerary choice models to update the travel time related variables. International passenger demand is a very important component of the air transportation system in the United States. The proportion of international enplanements relative to total enplanements increased from 8% in 1990 to 11% in 2008. Nine linear regression models are developed to forecast the enplanements from the United States to each of nine international regions. The international enplanements from the CONUS to each world region are modeled as a function of GDP and GDP per capita of both the United States and the specific region. A dummy variable is also used to account for the effects of September 11, 2001. The total number of international enplanements is forecast to increase from 74.7 million in 2008 to 184.4 million in 2028. The average annual growth rate is expected to be 4.7%. The European Union – United States Open Skies Agreement, which became effective March 30, 2008. Mathematical models are developed to forecast the effect of EU-US Open Skies Agreement on commercial airline passenger traffic over the North Atlantic Ocean. Nine econometric models were developed to forecast passenger traffic between the United States and nine selected European countries between 2008 through 2020. 68 new nonstop flights between the United States airports and the European airports are predicted by the model in 2020 using the airport pair passenger demand forecast. London, Heathrow is demonstrated as an example for rerouting the excess air travel passengers from one airport to other airports when the airport operational capacity is exceeded. The proportion of international enplanements relative to total enplanements within CONUS increased from 8% in 1990 to 11% in 2008. 51% of the sampled international and U.S. territories passengers served by U.S. carriers had at least one domestic coupon in 2007. The number of DOI passengers through airport-pairs in each of the historical years (1990-2007) is estimated based on the adjusted 100% international itineraries including pure international itineraries plus the non-CONUS itineraries. The total number of DOI enplanements is estimated to grow from 37.3 million in 1990 to 79.4 million in 2007. 193 CONUS airports are estimated to have at least 10,000 DOI enplanements in 2007. The number of DOI enplanements is forecast to grow from 79.4 million in 2007 to 206.2 million in 2030 with average growth rate of 4.2% per year. In recent years, there has been an increasing use of secondary airports both in Europe and the U.S. Regional airports have long been considered as a possible source of relief to reduce airport congestion at the hub airport and to efficiently accommodate future air travel demand. The conditions under which the secondary airports develop in a metropolitan area are examined. Fifteen multi-airport systems including 19 Operational Evolution Plan airports and 25 active secondary airports are identified in the National Airspace System. Diverse trends of traffic distribution among airports in the same metropolitan area are observed. We observed that the number of markets served at the secondary airports is less than that at the primary airport in the same metropolitan area. Most of the secondary airports are currently dominated by the low-cost carriers. The share of seats supplied by the low-cost carriers at the secondary airports has increased during the period 1990-2008. Full service carriers concentrate their service mainly on the primary airport in all the multi-airport systems analyzed. The average seat capacity per aircraft at the secondary airports is higher than that of primary airports in most of the multi-airport systems. The secondary airports mainly serve the domestic O&D passengers. / Ph. D.

A tool to model both current rail and future high-speed rail (HSR) corridors has been presented in this work. The model is designed as an addition to the existing TSAM (Transportation System Analysis Model) capabilities of modeling commercial airline and automobile demand. TSAM is a nationwide county to county multimodal demand forecasting tool based on the classical four step process. A variation of the Box-Cox logit model is proposed to best capture the characteristic behavior of rail demand in US. The utility equation uses travel time and travel cost as the decision variables for each model. Additionally, a mode specific geographic constant is applied to the rail mode to model the North-East Corridor (NEC). NEC is of peculiar interest in modeling, as it accounts for most of the rail ridership. The coefficients are computed using Genetic Algorithms. A one county to one station assignment is employed for the station choice model. Modifications are made to the station choice model to replicate choices affected by the ease of access via driving and mass transit. The functions for time and cost inputs for the rail system were developed from the AMTRAK website. These changes and calibration coefficients are incorporated in TSAM. The TSAM model is executed for the present and future years and the predictions are discussed. Sensitivity analysis for cost and speed of the predicted HSR is shown. The model shows the market shift for different modes with the introduction of HSR. Limited data presents the most critical hindrance in improving the model further. The current validation process incorporates essential assumptions and approximations for transfer rates, short trip percentages, and access and egress distances. The challenges for the model posed by limited data are discussed in the model. / Master of Science

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.

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.

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.