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Author: Lei Zhang (Ph.D.)
Publisher:
ISBN:
Category : Traffic surveys
Languages : en
Pages : 43
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Book Description
The interaction between the built environment, travel behavior and public health is now a major concern for both researchers and urban planners. Currently, there is little empirical research that explores and examines the relationship between each of them. This study explores the linkages between an individual’s health, the urban form and his/her everyday travel behavior. The objective of this study is two-fold: (1) to develop a Smartphone Application that would serve as a platform to automatically collect person-level travel behavior data, and (2) to analyze the influence of daily activity patterns of an individual, his/her healthy-living and the urban form of their neighborhood of residence, on each other. In the past, scholars have explored different variables and linked them with the individual’s travel behavior. This study explores the linkages between an individual’s health, urban form and his/her everyday travel behavior. In order to capture accurate and comprehensive travel behavior information, a smartphone application is developed that can track user location for long periods without the need of user intervention. Focus is placed on designing the application to have minimum respondent burden and long-standing battery life of the smart device. Subjects are recruited through a web survey designed to collect information about the individual’s healthy living habits. Data from the application is regressed against the health measure data acquired from the survey. Results show that active modes of travel are positively associated with the person’s general health measures. The feasibility of this platform as a data collection method is highlighted while explaining the limitations due to the sample distribution and size.
Author: Lei Zhang (Ph.D.)
Publisher:
ISBN:
Category : Traffic surveys
Languages : en
Pages : 43
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Book Description
The interaction between the built environment, travel behavior and public health is now a major concern for both researchers and urban planners. Currently, there is little empirical research that explores and examines the relationship between each of them. This study explores the linkages between an individual’s health, the urban form and his/her everyday travel behavior. The objective of this study is two-fold: (1) to develop a Smartphone Application that would serve as a platform to automatically collect person-level travel behavior data, and (2) to analyze the influence of daily activity patterns of an individual, his/her healthy-living and the urban form of their neighborhood of residence, on each other. In the past, scholars have explored different variables and linked them with the individual’s travel behavior. This study explores the linkages between an individual’s health, urban form and his/her everyday travel behavior. In order to capture accurate and comprehensive travel behavior information, a smartphone application is developed that can track user location for long periods without the need of user intervention. Focus is placed on designing the application to have minimum respondent burden and long-standing battery life of the smart device. Subjects are recruited through a web survey designed to collect information about the individual’s healthy living habits. Data from the application is regressed against the health measure data acquired from the survey. Results show that active modes of travel are positively associated with the person’s general health measures. The feasibility of this platform as a data collection method is highlighted while explaining the limitations due to the sample distribution and size.
Author: Anne Aguilera
Publisher: Elsevier
ISBN: 0128126485
Category : Transportation
Languages : en
Pages : 224
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Book Description
Urban Mobility and the Smartphone: Transportation, Travel Behavior and Public Policy provides a global synthesis of the transformation of urban mobility by the smartphone, clarifying the definitions of new concepts and objects in mobility studies, accounting for the changes in transportation and travel behavior triggered by the spread of the smartphone, and discussing the implications of these changes for policy-making and research. Urban mobility is approached here as a system of actors: the perspectives of individual behavior (including lifestyles), the supply of mobility services (including actors, business models), and public policy-making are considered. The book is based on an extensive review of the academic literature as well as systematic observation of the development of smartphone-based mobility services around the world. In addition, case studies provide practical illustrations of the ongoing transformation of mobility services influenced by the dissemination of smartphones. The book not only consolidates existing research, but also picks up on weak signals that help researchers and practitioners anticipate future changes in urban mobility systems. Key Features • Synthesizes existing research into one reference, providing researchers and policy-makers with a clear and complete understanding of the changes triggered by the spread of the smartphone. • Analyzes numerous case studies throughout developed and developing countries providing practical illustrations of the influence of the smartphone on travel behavior, transportation systems, and policy-making. • Provides insights for researchers and practitioners looking to engage with the "smart cities" and "smart mobility" discourse. - Synthesizes existing research into one reference, providing researchers and policy-makers with a clear and complete understanding of the changes triggered by the spread of the smartphone - Analyzes numerous case studies throughout developed and developing countries providing practical illustrations of the influence of the smartphone on travel behavior, transportation systems, and policy-making - Provides insights for researchers and practitioners looking to engage with the "smart cities" and "smart mobility" discourse
Author: Yu-Jen Chen
Publisher:
ISBN:
Category : Structural equation modeling
Languages : en
Pages : 152
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Book Description
Research on travel behavior continues to be one of the most prominent areas in the transportation area. Planners and policymakers try to understand and manage travel behavior. Making and implementation of travel demand management (TDM) policies greatly rely on the understanding of the determinants of activity-travel patterns and travel demand. Among the activity-travel patterns, trip chaining and joint travel have received much research interest. Trip chaining is typically viewed as a home-based tour that connects multiple out-of-home activities. Joint travel is commonly defined as traveling with others. Travel demand is generally measured by trip generation and travel distances. Investigating different aspects of travel behavior helps us better understand the links between activity participation and mobility, and improves the evaluation of the transportation infrastructure investments and policies such as high occupancy vehicle (HOV) lanes and vehicle miles traveled (VMT) reduction programs. Several studies have regarded trip chaining, joint travel, trip generation, and travel distances as different dimensions of travel behavior to be examined in terms of various socio-demographics and urban form factors. However, limited work has been done to use activity-travel patterns as mediating variables and analyze how trip chaining and joint travel shape the resulting travel demand. Furthermore, relationships between travel behavior and urban form factors at out-of-home activity locations remain unclear. Based on the 2012 travel survey data from the Cleveland Metropolitan Area, this study first investigates the relationships among trip chaining, joint travel, home-based tour generation, and travel distances at three different levels: tour, individual, and household levels. Second, the influences of socio-demographics and urban form factors at tour origins and destinations on travel behavior are examined simultaneously. Lastly, while using trip chaining and joint travel as mediating variables, this study estimates the mediating effects of socio-demographics and urban form via activity-travel patterns on travel demand. The Structural Equation Modelling (SEM) approach is applied. The study reveals the existence of significant relationships between activity-travel patterns and travel demand. Trip chaining is negatively associated with joint travel. While it increases travel distances, this effects gets weakened through its indirect effect via decreased tour generation. Joint travel appears to increase tour generation but decrease the travel distances. Most socio-demographics have significant effects with expected signs on travel behavior. The analysis suggests that urban form factors at tour origins and destinations play important roles on the resulting travel demand. Some urban form factors may not have direct effects on travel demand but have significant indirect effects on tour generation or travel distances through activity-travel patterns. This research presents how activity-travel patterns shape travel demand and concludes that trip chaining and joint travel should be taken into consideration while analyzing travel demand. The findings on socio-demographics and urban form factors can be used as inputs to improve the future evaluation of transportation projects and help planners integrate land-use strategies as tools to change people’s travel behavior. This will further mitigate the negative externalities associated with our travel patterns.
Author: Rajanesh Kakumani
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
The validity of the influence of urban form on travel behavior has been a topic of interest in travel behavior research. Empirical research shows that urban form influences travel behavior causing less travel impacts. However, according to the conventional travel impact assessment following the ITE's (Institute of Transportation Engineers) Trip Generation Handbook, developments with higher levels of urban form measures will generate a greater travel impacts because they generate higher number of trips. The ITE Trip Generation Handbook is typically used as a guideline to estimate the number of trips generated by a development. The hypothesis made in the present research is that a development defined with higher levels of land use mix, street connectivity and residential density will generate a higher number of trips because of the greater accessibility but they will be shorter in length. Therefore, the effective distance travelled will be less even though higher numbers of trips are generated. Considering the distance travelled on a roadway will be an appropriate unit for measuring the travel impacts, the research argues that VMT (Vehicle Miles Travelled) can be a better measurement unit than the number of trips to validate the influence of urban form on travel behavior.
Author: Fei Yang
Publisher: Springer Nature
ISBN: 9811680086
Category : Computers
Languages : en
Pages : 235
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Book Description
This book is devoted to the technology and methodology of individual travel behavior analysis and refined travel information extraction. Traditional resident trip surveys are characterized by many shortcomings, such as subjective memory errors, difficulty in organization and high cost. Therefore, in this book, a set of refined extraction and analysis techniques for individual travel activities is proposed. It provides a solid foundation for the optimization and reconstruction of traffic theoretical models, urban traffic planning, management and decision-making. This book helps traffic engineering researchers, traffic engineering technicians and traffic industry managers understand the difficulties and challenges faced by transportation big data. Additionally, it helps them adapt to changes in traffic demand and the technological environment to achieve theoretical innovation and technological reform.
Author: André Laurent Carrel
Publisher:
ISBN:
Category :
Languages : en
Pages : 127
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Book Description
Smartphones are becoming an increasingly interesting survey medium for behavioral research due to their value for collecting long-term panel observations and supplementary data on the choice environment. Thanks to the sensor data, it becomes possible to survey participants based on whether or not a certain activity has been carried out. By fusing the phone-generated sensor data and survey responses with data from outside sources, substantial data sets can be generated which can be used to investigate choices in complex environments. Computational systems for behavior research take advantage of automation and scalability opportunities, thereby building also on pertinent bodies of literature regarding machine learning on large data sets and crowdsourcing. The importance of comprehensive, long-term data sets in understanding behavior has been highlighted in the choice theory literature, specifically with respect to capturing an individual decision-maker’s history of choices and personal experiences with those choices. To date, however, relatively few studies have capitalized on emerging technologies to create or analyze such data sets. Rich data sets which combine panel information on the decision-maker with information on the choice environment can support the study of dynamic phenomena, which is especially important in a rapidly changing world where behavioral adaptation can take place on a relatively small time scale and, once habits are formed, have long-lasting effects. Some examples of pressing questions in the field of transportation involve understanding how travelers are responding to the emerging sharing economy, to new ride sharing services and new information systems, how time use and travel patterns will change due to automated vehicles, and how more sustainable travel behavior can be promoted through incentive or pricing strategies. This dissertation aims to support the adoption of smartphone-based survey technology in travel behavior research in order to lay the groundwork for research aimed at answering the above questions. It describes the design and implementation of a smartphone-based study, presents a system for fusing smartphone data with externally acquired data, and demonstrates how these ample data sets can be leveraged to generate new behavioral insights. The problem chosen for study is the link between transit service quality, rider satisfaction and ridership retention on public transit. This is motivated by the fact that many transit agencies in the United States continue to see large rates of ridership turnover, and that to date, very little is known about what drives transit use cessation. The six-week San Francisco Travel Quality Study (SFTQS) was conducted in autumn 2013. It collected a data set that included high-resolution phone locations, a number of daily mobile surveys on specific trip experiences, responses to online entry and exit surveys, and transit vehicle locations. By fusing the phone location data with transit vehicle locations, individual-level automatic transit travel diaries could be created without the need to ask participants. The reduced respondent burden, in turn, facilitated a longer term data collection. Initial recruitment proved to be challenging, with response rates to some of the email and direct mailing lists around 1%, and response rates to in-person recruiting between 8 and 15%. On the other hand, attrition was lower than expected, considering the length of the study: The initial enrollment was 856 participants, of which 555 (65%) participants completed all required surveys and 637 (74%) completed the entry and exit survey as well as at least one daily mobile survey. Interestingly, 36% of participants later stated they would have preferred to fill out mobile surveys more frequently (e.g., one per trip rather than one per day) than what was required in the study. A central part of the computational infrastructure used to collect the data was the system of integrated methods to reconstruct and track travelers’ usage of transit at a detailed level by matching location data from smartphones to automatic transit vehicle location (AVL) data and by identifying all out-of-vehicle and in-vehicle portions of the passengers’ trips. This system is presented in detail in this dissertation, where it is shown how high-resolution travel times and their relationships with the timetable are derived. Approaches are presented for processing relatively sparse smartphone location data in dense transit networks with many overlapping bus routes, distinguishing waits and transfers from non-travel related activities, and tracking underground travel in a metro network. While transit agencies have increasingly adopted systems for collecting data on passengers and vehicles, the ability to derive high-resolution passenger trajectories and directly associate them with vehicles has remained a challenge. The system presented in this dissertation is intended to remedy this situation, and it enables a range of different analyses and applications. Results are presented from an implementation and deployment of the system during the SFTQS. An analysis of out-of-vehicle travel times shows that (a) longer overall travel times in trips involving a transfer are strongly driven by transfer times, and (b) median wait times at the origin stops are consistently low regardless of the headway. The latter can be seen as an effect of real-time information, as it appears that wait times are increasingly spent at locations other than the stop and that passengers time their arrivals at the stop. Given these shifts, the traditional assumption that the average wait time at a transit stop of a high-frequency route is half the headway due to random arrivals may need to be revisited. This dissertation presents two applications to derive new behavioral insights from the SFTQS data set and to demonstrate the power and value of these new types of data. The analyses were based on participants’ individual history of transit usage and experiences with service quality. The first analysis used the data from the daily mobile surveys to model the link between participants' reported satisfaction with travel times on specific trips (i.e., their subjective assessment) and objective measures of those travel times. Thanks to the tracking data, it was possible to decompose observed travel times into their in-vehicle and out-of-vehicle components, and to compare the observed in-vehicle travel times to scheduled in-vehicle travel times to identify delays suffered while the participant was on board. The estimation results show that on average, a minute of delay on board a vehicle contributed more to passenger dissatisfaction than a minute of waiting time either at the origin stop or at a transfer stop, and that delays on board metro trains are perceived as more onerous than delays on board buses. Furthermore, the models included participants' baseline satisfaction levels as reported in the entry survey and a daily measure of their subjective well-being. Both variables are relatively new elements in travel surveys, and both are seen to be significant in the estimation results. These results indicate that satisfaction with travel times may be composed of a baseline satisfaction level and a variable component that depends on daily experiences, and that there may be non-negligible interactions between subjective well-being and travel satisfaction. Therefore, it is recommended that future survey designs should include measures for both these variables. The second application builds on the results of the first to empirically investigate the causes for cessation of transit use, with a specific focus on the influence of personal experiences that users have had in the past, on resulting levels of satisfaction, and subsequent behavioral intentions. A latent variable choice model is developed to explain the influence of satisfaction with travel times, including wait times at the origin stop, in-vehicle travel times, transfer times and overall reliability, and satisfaction with the travel environment on behavioral intentions. The group of variables summarized as ``travel environment'' includes crowding, cleanliness, the pleasantness of other passengers, and safety. Satisfaction is modeled as a latent variable, and the choice consists of participants’ stated desire and intention to continue using public transportation in the future. In addition to the delay types captured in the first analysis, a set of negative critical incidents is included, namely being left behind at stops and arriving late to work, school or a leisure activity. The results of the model and descriptive analysis show that operational problems resulting in delays and crowding are much stronger drivers of overall dissatisfaction and cessation than variables related to the travel environment. The importance of baseline satisfaction, mood and the relatively larger impact of in-vehicle delays are confirmed by this model. Thanks to the framework, the critical incidents can be expressed in terms of equivalent delay minutes. For instance, being left behind at a bus stop is found to cause the same amount of dissatisfaction as approximately 18 minutes of wait time. Furthermore, the effect of delays or incidents on ridership can be quantified, as is demonstrated in a set of simulations using the San Francisco transit network (Muni) as a basis. It is shown that if all passengers were subjected to one hypothetical on-board delay of 10 minutes per person, the resulting loss of riders would account for approximately 9.5% of Muni's yearly ridership turnover. In summary, the contributions and impact of this dissertation are as follows: It presents a framework and system that allows the.
Author: Markus Otto Botte
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
[Truncated] A renewed interest in public transport, and particularly rail, has sparked a renaissance of Transit Oriented Development (TOD), with the aim of creating more liveable urban environments and fostering more sustainable travel behaviour. But without in-depth knowledge of the complex interactions between urban form, human activities and travel behaviour, the risk of mismatched urban planning outcomes, failing to adequately address human needs and desires, appears obvious. Hence, a debate on the relative roles of built environment and personal preferences and attitudes in shaping travel behaviour has been ongoing; however, the related empirical and theoretical research has often remained inconclusive in its findings. This research further adds to the discourse on the associations between different TOD opportunities and households' travel behaviour. There are a number of contributions that this research is making: 1) it compares various geometries and highlights the benefits of kernel density as the most appropriate spatial tool for Activity Spaces for a one-day travel diary; 2) through a data enrichment methodology, it reveals the potential for GPS methods to enhance the Activity Space measures; 3) it analyses changes in Activity Spaces as a result of changes in urban form and development of TODs; 4) it evaluates in a structural equation model the connections between TODs and travel behaviour after accounting for household preferences and self-selection; and 5) it validates findings of the multivariate model with an Artificial Neural Network for enhanced credibility and confidence in the findings. These contributions are explained briefly in the following paragraphs. After investigating the potential of Activity Space Analysis and adopting the concept as a central research element for the behavioural analysis of activity-travel patterns, Activity Space analysis was systematically examined in terms of methodology, visualisation, and practical application and subsequently deployed to evaluate urban form implications on household travel behaviour, with the aim to measure TOD success. The examination of a new public transport railway line, crossing the southern suburbs of Perth, Western Australia (WA), along a 72km long network spine, provided a real world scenario for measuring realised Activity Spaces and validation of the holistic modelling approach developed for this research.
Author: Konstadinos G. Goulias
Publisher:
ISBN: 0128173408
Category :
Languages : en
Pages : 734
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Book Description
Mapping the Travel Behavior Genome covers the latest research on the biological, motivational, cognitive, situational, and dispositional factors that drive activity-travel behavior. Organized into three sections, Retrospective and Prospective Survey of Travel Behavior Research, New Research Methods and Findings, and Future Research, the chapters of this book provide evidence of progress made in the most recent years in four dimensions of the travel behavior genome. These dimensions are Substantive Problems, Theoretical and Conceptual Frameworks, Behavioral Measurement, and Behavioral Analysis. Including the movement of goods as well as the movement of people, the book shows how traveler values, norms, attitudes, perceptions, emotions, feelings, and constraints lead to observed behavior; how to design efficient infrastructure and services to meet tomorrow's needs for accessibility and mobility; how to assess equity and distributional justice; and how to assess and implement policies for improving sustainability and quality of life. Mapping the Travel Behavior Genome examines the paradigm shift toward more dynamic, user-centric, demand-responsive transport services, including the "sharing economy," mobility as a service, automation, and robotics. This volume provides research directions to answer behavioral questions emerging from these upheavals. Offers a wide variety of approaches from leading travel behavior researchers from around the world Provides a complete map of the methods, skills, and knowledge needed to work in travel behavior Describes the state of the art in travel behavior research, providing key directions for future research
Author: Stefan Schönfelder
Publisher: Routledge
ISBN: 1317003454
Category : Political Science
Languages : en
Pages : 263
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Book Description
The recent availability of longitudinal data on individual trip making and activity behaviour has provided analysts with new insights into the structures and motives of daily life travel. Multi-week travel diary data-sets and GPS observations are exciting sources of information for the description and modelling of the variability of individual travel patterns. Through an analysis of these strong new data sets, this book questions what are the most suitable methodological tools to represent the structures of long-term travel behaviour. It also examines what the data tells us about the travellers' motives and looks at how planning should translate the findings into forecasting tools and transport strategies. In doing so, the multifaceted and ambiguous character of daily life travel is revealed, illustrating how, while sound routines in time and space seem to dominate daily life, individuals show a considerable amount of variability and flexibility in travel and activity behaviour.
Author: Yuan Yao
Publisher: Springer
ISBN: 3030021017
Category : Computers
Languages : en
Pages : 64
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Book Description
This SpringerBrief presents a typical life-cycle of mobile data mining applications, including: data capturing and processing which determines what data to collect, how to collect these data, and how to reduce the noise in the data based on smartphone sensors feature engineering which extracts and selects features to serve as the input of algorithms based on the collected and processed data model and algorithm design In particular, this brief concentrates on the model and algorithm design aspect, and explains three challenging requirements of mobile data mining applications: energy-saving, personalization, and real-time Energy saving is a fundamental requirement of mobile applications, due to the limited battery capacity of smartphones. The authors explore the existing practices in the methodology level (e.g. by designing hierarchical models) for saving energy. Another fundamental requirement of mobile applications is personalization. Most of the existing methods tend to train generic models for all users, but the authors provide existing personalized treatments for mobile applications, as the behaviors may differ greatly from one user to another in many mobile applications. The third requirement is real-time. That is, the mobile application should return responses in a real-time manner, meanwhile balancing effectiveness and efficiency. This SpringerBrief targets data mining and machine learning researchers and practitioners working in these related fields. Advanced level students studying computer science and electrical engineering will also find this brief useful as a study guide.
