Download or read book Modeling Users Behavior Toward Automated Vehicles and Mobility Services Using Revealed and Stated Preference Data written by Parasto Jabbari and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Emerging technologies in transportation, such as automated vehicles (AVs) and mobility services, are expected to impact travelers’ behavior and choices. However, due to many uncertainties surrounding these new technologies, the magnitude and direction of this impact remain a mystery. Literature on AVs identifies several crucial questions and issues surrounding automated vehicles and new mobility services including: (1) potential induced demand, (2) trust in technology and its effect on adoption, (3) AVs as a mobility-as-a-service enabler. In this dissertation, I aimed to tackle these issues by quantifying value of travel time as a determinant of induced demand, study trust in AV technology as a key determinant of adoption, and modeling within tour inter-dependencies as determinant of multimodal travel and MaaS adoption. First, I use the data of actual mode choices between ridehailing and free-float carsharing to build models of mode choices to inform analyses of the prospective change in time valuation and travel behavior when riding in future highly AVs. Then, I discuss the design and implementation of a choice survey based on users’ revealed trip diary that overcomes shortcomings of revealed preference data. Next, I use the data from the choice survey to build an integrated choice and latent variable (ICLV) model that quantifies the impact of psychological constructs such as AVs safety perception on trip-based mode choices, specifically choices involving privately-owned AVs and driverless ridehailing services. Finally, I build tour-based mode choice models that allow capturing interdependencies among trips within a tour and explore potential for multimodal trip. My results from analyzing revealed preference data shows that riding in a car versus driving one reduces the value of travel time (VoTT) by $23/hour which confirms a significant time savings benefit in eliminating the burden of driving for travelers. While AVs potentially provide time saving benefits, based on current public’s assessment of the technology’s safety, market share of AVs remain small. However, improvements in users’ perception of AVs’ safety can considerably grow the market share for privately-owned AVs to the point that it hinders market share of driverless ridehailing. Another avenue for AVs to affect transportation system is enabling multimodal travel. Using tour-based mode choice modeling, I found that people preferences to use unimodal tours when using AVs are about the same as any other modes and I identified strong inclination among our sample to use unimodal tours despite the mode of travel. The findings of this dissertation highlight the potential for increases in VMT and as a result increases in induced demand and GHG emissions, as it is expected that people’s value of travel time considerably drops in AVs and market share of AVs grow substantially when users perceive them safe. Also, as highlighted in this dissertation, even with AVs and driverless ridehailing mode inertia is high among users, and solely introducing these new modes would not contribute to multimodal travels. This dissertation illustrates that the adoption of AVs cannot solve many of the pressing transportation issues if they are introduced to the current system without any changes to the system. There is a need for policies and plans in place to make sure the new technologies potential is directed toward a more sustainable future.

Download or read book A Study of New Yorkers Preference for Autonomous Vehicles in NYC Based on Security Data Applying Discrete Choice Model Methods written by Dewei Xiao and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With continuous growth of urban populations, transportation system faces numerouschallenges such as surging demand of real time services, insufficient government investment and sustainability of environment. Shared autonomous vehicles might be a good way to tackle these problems. SAVs could provide relatively cheap mobility on-demand services and new technology like electrical autonomous vehicles have gained wide popularity across the world as it's a more environmental-friendly and energy efficient way of travel. As prosperous as this market seems, it becomes critical for us to study passengers' attitudes and preference towards SAVs, since it not only enriches behavioral study for suppliers in this market but also helps suppliers to design more reasonable operation strategies based on the study. This article intends to advance future research about the travel behavior impacts of SAVs, by identifying the characteristics of users who are likely to adopt SAV services and establishing willingness to pay measures for service attributes. This research uses the stated preference survey data conducted by Professor Ricardo using Qualtrics, applying a conditional logit model to study factors influencing the preferences and then a mixed logit model to study the unobserved heterogeneity in the distributions of travelers' preference. The results show that service attributes including travel cost, travel time and waiting time are critical determinants of the use of SAVs and the acceptance of DRS. Differences in willingness to pay for service attributes indicate that SAVs with DRS and SAVs without DRS are perceived as two distinct mobility options. The results imply that the adoption of SAVs may differ across subgroups, whereby young individuals and individuals with multimodal travel patterns are more likely to adopt SAVs. The methodological limitations of the study are also acknowledged. Despite a potential hypothetical bias, the results capture the directionality and relative importance of the attributes of interest.

Download or read book Attitudes and Attitude Change written by Gerd Bohner and published by Psychology Press. This book was released on 2014-03-18 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Attitudes - cognitive representations of our evaluation of ourselves, other people, things, actions, events, ideas - and attitude change have been a central concern in social psychology since the discipline began. People can - and do - have attitudes on an infinite range of things but what are attitudes, how do we form them and how can they be modified? This book provides the student with a comprehensive and accessible introduction to the basic issues in the psychological study of attitudes. Drawing on research from Europe and the USA it presents up-to-date coverage of the key issues that will be encountered in this area, including attitude formation and change, functions of attitudes, attitude measurement, attitudes as temporary constructs, persuasion processes and prediction of behaviour from attitudes.

Download or read book Modeling and Forecasting the Impact of Major Technological and Infrastructural Changes on Travel Demand written by Feras El Zarwi and published by . This book was released on 2017 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: The transportation system is undergoing major technological and infrastructural changes, such as the introduction of autonomous vehicles, high speed rail, carsharing, ridesharing, flying cars, drones, and other app-driven on-demand services. While the changes are imminent, the impact on travel behavior is uncertain, as is the role of policy in shaping the future. Literature shows that even under the most optimistic scenarios, society's environmental goals cannot be met by technology, operations, and energy system improvements only - behavior change is needed. Behavior change does not occur instantaneously, but is rather a gradual process that requires years and even generations to yield the desired outcomes. That is why we need to nudge and guide trends of travel behavior over time in this era of transformative mobility. We should focus on influencing long-range trends of travel behavior to be more sustainable and multimodal via effective policies and investment strategies. Hence, there is a need for developing policy analysis tools that focus on modeling the evolution of trends of travel behavior in response to upcoming transportation services and technologies. Over time, travel choices, attitudes, and social norms will result in changes in lifestyles and travel behavior. That is why understanding dynamic changes of lifestyles and behavior in this era of transformative mobility is central to modeling and influencing trends of travel behavior. Modeling behavioral dynamics and trends is key to assessing how policies and investment strategies can transform cities to provide a higher level of connectivity, attain significant reductions in congestion levels, encourage multimodality, improve economic and environmental health, and ensure equity. This dissertation focuses on addressing limitations of activity-based travel demand models in capturing and predicting trends of travel behavior. Activity-based travel demand models are the commonly-used approach by metropolitan planning agencies to predict 20-30 year forecasts. These include traffic volumes, transit ridership, biking and walking market shares that are the result of large scale transportation investments and policy decisions. Currently, travel demand models are not equipped with a framework that predicts long-range trends in travel behavior for two main reasons. First, they do not entail a mechanism that projects membership and market share of new modes of transport into the future (Uber, autonomous vehicles, carsharing services, etc). Second, they lack a dynamic framework that could enable them to model and forecast changes in lifestyles and transport modality styles. Modeling the evolution and dynamic changes of behavior, modality styles and lifestyles in response to infrastructural and technological investments is key to understanding and predicting trends of travel behavior, car ownership levels, vehicle miles traveled (VMT), and travel mode choice. Hence, we need to integrate a methodological framework into current travel demand models to better understand and predict the impact of upcoming transportation services and technologies, which will be prevalent in 20-30 years. The objectives of this dissertation are to model the dynamics of lifestyles and travel behavior through: " Developing a disaggregate, dynamic discrete choice framework that models and predicts long-range trends of travel behavior, and accounts for upcoming technological and infrastructural changes." Testing the proposed framework to assess its methodological flexibility and robustness." Empirically highlighting the value of the framework to transportation policy and practice. The proposed disaggregate, dynamic discrete choice framework in this dissertation addresses two key limitations of existing travel demand models, and in particular: (1) dynamic, disaggregate models of technology and service adoption, and (2) models that capture how lifestyles, preferences and transport modality styles evolve dynamically over time. This dissertation brings together theories and techniques from econometrics (discrete choice analysis), machine learning (hidden Markov models), statistical learning (Expectation Maximization algorithm), and the technology diffusion literature (adoption styles). Throughout this dissertation we develop, estimate, apply and test the building blocks of the proposed disaggregate, dynamic discrete choice framework. The two key developed components of the framework are defined below. First, a discrete choice framework for modeling and forecasting the adoption and diffusion of new transportation services. A disaggregate technology adoption model was developed since models of this type can: (1) be integrated with current activity-based travel demand models; and (2) account for the spatial/network effect of the new technology to understand and quantify how the size of the network, governed by the new technology, influences the adoption behavior. We build on the formulation of discrete mixture models and specifically dynamic latent class choice models, which were integrated with a network effect model. We employed a confirmatory approach to estimate our latent class choice model based on findings from the technology diffusion literature that focus on defining distinct types of adopters such as innovator/early adopters and imitators. Latent class choice models allow for heterogeneity in the utility of adoption for the various market segments i.e. innovators/early adopters, imitators and non-adopters. We make use of revealed preference (RP) time series data from a one-way carsharing system in a major city in the United States to estimate model parameters. The data entails a complete set of member enrollment for the carsharing service for a time period of 2.5 years after being launched. Consistent with the technology diffusion literature, our model identifies three latent classes whose utility of adoption have a well-defined set of preferences that are statistically significant and behaviorally consistent. The technology adoption model predicts the probability that a certain individual will adopt the service at a certain time period, and is explained by social influences, network effect, socio-demographics and level-of-service attributes. Finally, the model was calibrated and then used to forecast adoption of the carsharing system for potential investment strategy scenarios. A couple of takeaways from the adoption forecasts were: (1) highest expected increase in the monthly number of adopters arises by establishing a relationship with a major technology firm and placing a new station/pod for the carsharing system outside that technology firm; and (2) no significant difference in the expected number of monthly adopters for the downtown region will exist between having a station or on-street parking. The second component in the proposed framework entails modeling and forecasting the evolution of preferences, lifestyles and transport modality styles over time. Literature suggests that preferences, as denoted by taste parameters and consideration sets in the context of utility-maximizing behavior, may evolve over time in response to changes in demographic and situational variables, psychological, sociological and biological constructs, and available alternatives and their attributes. However, existing representations typically overlook the influence of past experiences on present preferences. This study develops, applies and tests a hidden Markov model with a discrete choice kernel to model and forecast the evolution of individual preferences and behaviors over long-range forecasting horizons. The hidden states denote different preferences, i.e. modes considered in the choice set and sensitivity to level-of-service attributes. The evolutionary path of those hidden states (preference states) is hypothesized to be a first-order Markov process such that an individual's preferences during a particular time period are dependent on their preferences during the previous time period. The framework is applied to study the evolution of travel mode preferences, or modality styles, over time, in response to a major change in the public transportation system. We use longitudinal travel diary from Santiago, Chile. The dataset consists of four one-week pseudo travel diaries collected before and after the introduction of Transantiago, which was a complete redesign of the public transportation system in the city. Our model identifies four modality styles in the population, labeled as follows: drivers, bus users, bus-metro users, and auto-metro users. The modality styles differ in terms of the travel modes that they consider and their sensitivity to level-of-service attributes (travel time, travel cost, etc.). At the population level, there are significant shifts in the distribution of individuals across modality styles before and after the change in the system, but the distribution is relatively stable in the periods after the change. In general, the proportion of drivers, auto-metro users, and bus-metro users has increased, and the proportion of bus users has decreased. At the individual level, habit formation is found to impact transition probabilities across all modality styles; individuals are more likely to stay in the same modality style over successive time periods than transition to a different modality style. Finally, a comparison between the proposed dynamic framework and comparable static frameworks reveals differences in aggregate forecasts for different policy scenarios, demonstrating the value of the proposed framework for both individual and population-level policy analysis. The aforementioned methodological frameworks comprise complex model formulation. This however comes at a cost in terms.

Download or read book On Preference Modeling Techniques to Analyze Autonomous Vehicle Adoption and Use written by Gopindra Sivakumar Nair and published by . This book was released on 2020 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past decade, there have been rapid advancements in autonomous driving technologies that enable vehicles to drive by themselves. Vehicles equipped with such technologies are commonly referred to as autonomous vehicles (AVs). These technologies have already reached a stage of maturation, and are being pilot-tested as well as tested on public roads. Despite these advancements, much is still unclear regarding preferences for this technology and potential AV use implications. In this dissertation, advanced econometric models are used for gaining insights regarding individual propensities to accept and use AVs, as well as the implications of such use on the transportation infrastructure. Before AVs can gain widespread acceptance, it is critical to demonstrate (to regulatory authorities and the general public) the safe and reliable operation of AVs. Such a demonstration would involve extensive testing of AVs on public roads. For the success of such tests, it is important to ensure that people do not perceive the operation of these vehicles on public spaces as unsafe. This dissertation will make use of a nationwide survey conducted by the Pew Research Center to understand the socio-demographic characteristics and other attributes that affect an individual’s perception of the safety of sharing the road with AVs. A simultaneous equation model is estimated that takes into account the endogeneity between factors affecting individuals’ acceptance of AVs and their perceived safety of sharing the road with AVs. Once AVs have been deemed safe and acceptable for public use, services based on AVs may be made available to consumers in a variety of formats. Some households may choose not to own a vehicle and instead access AV-based services through a mobility-as-a-service (MaaS) framework. To understand ownership preferences regarding AVs, a recent survey conducted in the Puget Sound Region that elicited preferences for different paradigms of AV use and ownership is used. The stated preferences are translated to a ranking format, and the underlying assumptions and robustness of different ranking models based on the utility maximization principle are investigated. Model results on the preferred method of AV adoption suggest that a considerable proportion of individuals would opt to use AVs in the context of ride-hailing and by extension would be willing to accept a MaaS framework that utilizes AVs. Ride-hailing is a service that has been rapidly gaining popularity over the past decade. The preference for AV-based ride-hailing is expected to accelerate this trend. Unfortunately, most of the current travel demand modeling frameworks (TDMs) are not adequately structured to handle ride-hailing trips. A specific aspect of ride-hailing trips that the TDMs currently fail to capture is deadheading trips. In this dissertation, a framework and an approach to model deadheading trips are proposed. Data obtained from a ride-hailing service provider is employed for model estimation. Overall, the research undertaken as part of this dissertation will provide transportation planners and safety professionals with insights and guidance on how best to navigate the transition into the era of autonomous vehicle adoption and use

Download or read book Policy Implications of Autonomous Vehicles written by and published by Academic Press. This book was released on 2020-08-15 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Policy Implications of Autonomous Vehicles, Volume Five in the Advances in Transport Policy and Planning series systematically reviews policy relevant implications of AVs and the associated possible policy responses, and discusses future avenues for policy making and research. It comprises 13 chapters discussing: (a) short-term implications of AVs for traffic flow, human-automated bus systems interaction, cyber-security and safety, cybersecurity certification and auditing, non-commuting journeys; (b) long-term implications of AVs for carbon dioxide (CO2) emissions and energy, health and well-being, data protection, ethics, governance; (c) implications of AVs for the maritime industry and urban deliveries; and (d) overall synthesis and conclusions. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Advances in Transport Policy and Planning series Updated release includes the latest information on the policy implications of autonomous vehicles

Download or read book Three Revolutions written by Daniel Sperling and published by Island Press. This book was released on 2018-03 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: Front Cover -- About Island Press -- Subscribe -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- 1. Will the Transportation Revolutions Improve Our Lives-- or Make Them Worse? -- 2. Electric Vehicles: Approaching the Tipping Point -- 3. Shared Mobility: The Potential of Ridehailing and Pooling -- 4. Vehicle Automation: Our Best Shot at a Transportation Do-Over? -- 5. Upgrading Transit for the Twenty-First Century -- 6. Bridging the Gap between Mobility Haves and Have-Nots -- 7. Remaking the Auto Industry -- 8. The Dark Horse: Will China Win the Electric, Automated, Shared Mobility Race? -- Epilogue -- Notes -- About the Contributors -- Index -- IP Board of Directors

Download or read book Big Data and Mobility as a Service written by Haoran Zhang and published by Elsevier. This book was released on 2021-10-01 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data and Mobility as a Service explores MaaS platforms that can be adaptable to the ever-evolving mobility environment. It looks at multi-mode urban crowd data to assess urban mobility characteristics, their shared transportation potential, and their performance conditions and constraints. The book analyzes the roles of multimodality, travel behavior, urban mobility dynamics and participation. Combined with insights on using big data to analyze market and policy decisions, this book is an essential tool for urban transportation management researchers and practitioners. - Summarizes current fundamental MaaS technologies - Shows how to utilize anonymous big data for transportation analysis and problem-solving - Illustrates, with data-enabled shared transportation service examples from different countries, the similarities and differences within a global urban mobility framework

Download or read book Mobility and Safety Implications of Automated Vehicles in Mixed Traffic by Recognizing Behavioral Variations of Drivers written by Mudasser Seraj and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Introduction of Connected-Automated Vehicle (CAV) technology provided a new opportunity to fix the traditional transportation system. Automated vehicles (AuV) would take the driving responsibility and drive the vehicles by analyzing their' surrounding through a range of sensors. The connectivity feature of these vehicles would facilitate to sense of the roadway and traffic conditions beyond the range of sensors and make informed decisions. While the vehicles equipped with these technologies becoming more common, large-scale market penetration will take a long time. Hence, our transportation infrastructure will pass through a transitional phase where both Human-driven vehicles (HuV) and AuVs share the roadway. Additionally, the prosperity and acceptance of these technologies depend on a clear understanding of the implications of overcoming the limitations of the traditional transportation system. My research focused on developing a comprehensive modeling framework to establish numerical simulation of both types of vehicles (i.e., HuVs, AuVs ) while recognizing the variations of driving behaviors of human drivers. Modeling both vehicle types provided the opportunity to explore diverse mixed traffic scenarios to attain extensive insights into such traffic conditions. Prior to developing the modeling framework, the variations of the human driving patterns were identified through extensive analysis of real-world human driving data. Bi-directional (i.e., longitudinal, lateral) control features were analyzed to comprehend human instincts during driving which can be integrated with the human driver modeling. Further analysis was performed to classify driving behaviors based on these features for the short and long term. The upsides of studying human driving behavior rest not only on better understanding for modeling human drivers but also on designing automated vehicles capable of addressing the variations of human driver behavior. The behavioral classification approach in this part of the research used three vehicular features known as jerk, leading headway, and yaw rate to classify human drivers into two groups (Safe and Hostile Driving) on short-term classification, and drivers' habits are categorized into three classes (Calm Driver, Rational Driver, and Aggressive Driver). Through the proposed method, behavior classification has been successfully identified in 86.31 ± 9.84% of speeding and 87.92 ± 10.04% of acute acceleration instances. Afterward, the foundation of mixed traffic modeling was developed through car-following strategy formulation. This part of the research proposes a naïve microscopic car-following strategy for a mixed traffic stream in CAV settings and measured shifts in traffic mobility and safety as a result. Additionally, this part of the research explores the influences of platoon properties (i.e. Intra-platoon Headway, Inter-platoon Headway, Maximum Platoon Length) on traffic stream characteristics. Different combinations of HuVs and AuVs are simulated in order to understand the variations of improvements induced by AuVs in a traffic stream. Simulation results reveal that grouping AuVs at the front of the traffic stream to apply CACC-based car-following model will generate maximum mobility benefits for the traffic. Higher mobility improvements can be attained by forming long, closely spaced AuVs at the cost of reduced safety. To achieve balanced mobility and safety advantages from mixed traffic movements, dynamically optimized platoon configurations should be determined at varying traffic conditions and AuVs market penetrations. Finally, grounded on prior research on human driving behavior and modeling framework of mixed traffic, this research objectively experimented with bi-directional motion dynamics in a microscopic modeling framework to measure the mobility and safety implications for mixed traffic movement in a freeway weaving section. This part of research begins by establishing a multilane microscopic model for studied vehicle types from model predictive control with the provision to form a CACC platoon of AuV vehicles. The proposed modeling framework was tested first with HuV only on a two-lane weaving section and validated using standardized macroscopic parameters from the HCM. This model was then applied to incrementally expand the AuV share for varying inflow rates of traffic. Simulation results showed that the maximum flow rate through the weaving section was attained at a 65% AuV share while steadiness in the average speed of traffic was experienced with increasing AuV share. Finally, the results of simulated scenarios were consolidated and scaled to report expected mobility and safety outcomes from the prevailing traffic state as well as the optimal AuV share for the current inflow rate in weaving sections.

Download or read book Autonomous Vehicles and Future Mobility written by Pierluigi Coppola and published by Elsevier. This book was released on 2019-06-11 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous Vehicles and Future Mobility presents novel methods for examining the long-term effects on individuals, society, and on the environment for a wide range of forthcoming transport scenarios, such as self-driving vehicles, workplace mobility plans, demand responsive transport analysis, mobility as a service, multi-source transport data provision, and door-to-door mobility. With the development and realization of new mobility options comes change in long-term travel behavior and transport policy. This book addresses these impacts, considering such key areas as the attitude of users towards new services, the consequences of introducing new mobility forms, the impacts of changing work related trips, and more. By examining and contextualizing innovative transport solutions in this rapidly evolving field, the book provides insights into the current implementation of these potentially sustainable solutions. It will serve as a resource of general guidelines and best practices for researchers, professionals and policymakers.

Download or read book Is the Future of Urban Mobility Shared written by Patricia Sauri Lavieri and published by . This book was released on 2018 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Society is experiencing the initial stages of a technological revolution that promises to disrupt urban transportation as known today and induce behavioral and social changes. The main factors guiding the transformation of urban mobility are the growth of Information and Communication Technology (ICT)-enabled transportation services and the development of autonomous vehicle (AV) technologies. While the use of ICTs and vehicular automation are expected to provide direct road capacity improvements due to the real-time provision of traffic information, crash reductions, and platooning capabilities, these gains may be offset by latent demand effects. That is, the increase in level of service may actually result in the generation of more trips and escalation of vehicle miles traveled. In this sense, proactive planning and policy guided towards promoting the use of shared vehicles and pooled rides are important to minimize possible negative externalities of automation. The current dissertation provides initial guidance to such planning by examining individuals’ preferences toward the adoption of current and future mobility services and technologies. A research framework containing four independent but related analysis components is developed to allow a comprehensive investigation of travelers’ characteristics and behaviors associated with ride-hailing use and preferences regarding AVs. Empirical analyses are conducted using advanced econometric techniques applied to different types of data from three different cities. The results of the empirical analyses are compared and implications to transportation planning and policy are discussed. The results from the analyses undertaken in the dissertation show that, from a behavioral perspective, a service-based transportation future where people predominantly travel using shared vehicles and pooled rides instead of their own vehicles is on its way but still distant. A complex combination of actions is required to promote the use of shared services both today and in an AV future. Among these actions, we identify the need for campaigns to (a) increase technology awareness among older individuals and individuals from lower income households, and (b) reduce privacy-sensitivity among non-Hispanic Whites and millennials. Such efforts should also be complemented by a decrease in service fares and urban densification. The results also suggest the need to promote policies and integrated multi-modal systems that discourage individuals from substituting the use of active and public transit modes by ride-hailing and AV-based services. Finally, we observe that individuals seem to be less sensitive to the presence of strangers in a commute trip than in a leisure trip, but the sensitivity to time is the opposite. The implications of these results are that pooled services may have a large market penetration potential for commute trips as long as operated efficiently with minimal detour and pick-up/drop-off delays.

Download or read book Autonomous Driving written by Markus Maurer and published by Springer. This book was released on 2016-05-21 with total page 698 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies? Experts from Germany and the United States define key societal, engineering, and mobility issues related to the automation of vehicles. They discuss the decisions programmers of automated vehicles must make to enable vehicles to perceive their environment, interact with other road users, and choose actions that may have ethical consequences. The authors further identify expectations and concerns that will form the basis for individual and societal acceptance of autonomous driving. While the safety benefits of such vehicles are tremendous, the authors demonstrate that these benefits will only be achieved if vehicles have an appropriate safety concept at the heart of their design. Realizing the potential of automated vehicles to reorganize traffic and transform mobility of people and goods requires similar care in the design of vehicles and networks. By covering all of these topics, the book aims to provide a current, comprehensive, and scientifically sound treatment of the emerging field of “autonomous driving".

Download or read book Understanding Mobility as a Service MaaS written by David A. Hensher and published by Elsevier. This book was released on 2020-05-06 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: The widespread adoption of smartphones, ridesharing and carsharing have disrupted the transport sector. In cities around the world, new mobility services are both welcomed and challenged by regulators and incumbent operators. Mobility as a Service (MaaS), an ecosystem designed to deliver collaborative and connected mobility services in a society increasingly embracing a sharing culture, is at the center of this disruption. Understanding Mobility as a Service (MaaS): Past, Present and Future examines such topics as: How likely MaaS will be implemented in one digital platform app Whether MaaS will look the same in all countries The role multi-modal contract brokers play Mobility regulations and pricing models MaaS trials, their impacts and consequences Written by the leading thinkers in the field for researchers, practitioners, and policy makers, Understanding Mobility as a Service (MaaS): Past, Present and Future serves as a single source on all the current and evolving developments, debates, and challenges. Includes case studies to show how MaaS is delivered around the world Covers foundational aspects of MaaS, clarifying what it is for those new to the concept Offers an in-depth analysis on a wide range of MaaS topics including governance, contracts, consumer and supplier preferences, links to societal objectives, the role of trials, assessments, and more

Download or read book Towards Human Like Prediction and Decision Making for Automated Vehicles in Highway Scenarios written by David Sierra Gonzalez and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the past few decades automakers have consistently introduced technological innovations aimed to make road vehicles safer. The level of sophistication of these advanced driver assistance systems has increased parallel to developments in sensor technology and embedded computing power. More recently, a lot of the research made both by industry and institutions has concentrated on achieving fully automated driving. The potential societal benefits of this technology are numerous, including safer roads, improved traffic flows, increased mobility for the elderly and the disabled, and optimized human productivity. However, before autonomous vehicles can be commercialized they should be able to safely share the road with human drivers. In other words, they should be capable of inferring the state and intentions of surrounding traffic from the raw data provided by a variety of onboard sensors, and to use this information to make safe navigation decisions. Moreover, in order to truly navigate safely they should also consider potential obstacles not observed by the sensors (such as occluded vehicles or pedestrians). Despite the apparent complexity of the task, humans are extremely good at predicting the development of traffic situations. After all, the actions of any traffic participant are constrained by the road network, by the traffic rules, and by a risk-aversive common sense. The lack of this ability to naturally understand a traffic scene constitutes perhaps the major challenge holding back the large-scale deployment of truly autonomous vehicles in the roads.In this thesis, we address the full pipeline from driver behavior modeling and inference to decision-making for navigation. In the first place, we model the behavior of a generic driver automatically from demonstrated driving data, avoiding thus the traditional hand-tuning of the model parameters. This model encodes the preferences of a driver with respect to the road network (e.g. preferred lane or speed) and also with respect to other road users (e.g. preferred distance to the leading vehicle). Secondly, we describe a method that exploits the learned model to predict the future sequence of actions of any driver in a traffic scene up to the distant future. This model-based prediction method assumes that all traffic participants behave in a risk-aware manner and can therefore fail to predict dangerous maneuvers or accidents. To be able to handle such cases, we propose a more sophisticated probabilistic model that estimates the state and intentions of surrounding traffic by combining the model-based prediction with the dynamic evidence provided by the sensors. In a way, the proposed model mimics the reasoning process of human drivers: we know what a given vehicle is likely to do given the situation (this is given by the model), but we closely monitor its dynamics to detect deviations from the expected behavior. In practice, combining both sources of information results in an increased robustness of the intention estimates in comparison with approaches relying only on dynamic evidence. Finally, the learned driver behavioral model and the prediction model are integrated within a probabilistic decision-making framework. The proposed methods are validated with real-world data collected with an instrumented vehicle. Although focused on highway environments, this work could be easily adapted to handle alternative traffic scenarios.

Download or read book A Socio technical Model of Autonomous Vehicle Adoption Using Ranked Choice Stated Preference Data written by Katherine Elizabeth Asmussen and published by . This book was released on 2020 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding the “if” and “when” of autonomous vehicle (AV) adoption is of clear interest to car manufacturers in their positioning of business processes, but also to transportation planners and traffic engineers. In this thesis, we examine the individual-level AV adoption and timing process, considering the psycho-social factors of driving control, mobility control, safety concerns, and tech-savviness. A ranked choice stated preference design is used to elicit responses from Austin area residents regarding AV adoption. Our results underscore the need to examine the adoption of technology through a psycho-social lens. In particular, technology developments and design should not be divorced from careful investigations of habits and consumption motivations of different groups of individuals in the population. The findings from our analysis are translated to specific policy actions to promote AV adoption and accelerate the adoption time frame

Download or read book Reinterpreting Vehicle Ownership in the Era of Shared and Smart Mobility written by Rounaq Basu and published by . This book was released on 2019 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: Emerging transportation technologies like autonomous vehicles and services like on-demand shared mobility are casting their shadows over the traditional paradigm of vehicle ownership. Several countries are witnessing stagnation in overall car use, perhaps due to the proliferation of access-based services and changing attitudes of millennials. Therefore, it becomes necessary to revisit this paradigm, and reconsider strategies for modeling vehicle availability and use in this new era. This thesis attempts to do that through three studies that contribute to the methodological, conceptual, and praxis literatures. The first study proposes a hybrid modeling methodology that leverages machine learning techniques to enhance traditional behavioral discrete choice models used in practice. The usefulness of this model to predict market shares of unforeseen choices like new mobility services is illustrated through an application to the off-peak car in Singapore. Our model significantly improves upon the market shares predicted by traditional models through an average reduction of 60% in RMSE. The second study shifts the focus from vehicle ownership to vehicle availability in the form of mobility bundles. We leverage Singapore’s unique policy environment to empirically model households’ preferences for unique mobility bundles that are constructed in an ordinal fashion. This is followed by an examination of car usage within the household. Significant intra-household interaction effects are found with respect to job location, in addition to the observation of gender biases in the decision-making process. The third study evaluates the effectiveness of car-lite policies that seek to replace private vehicle usage with shared and smart mobility services. Behavioral responses to the policy and associated market effects are modeled using an integrated land use transport simulator calibrated for Singapore. Initially favorable aggregate outcomes tend to disappear as short-term market effects set in. Although outcomes stabilize to a certain extent over the long-term, the initial characteristics of the study area are found to strongly influence the success of such policies.

Download or read book Road Vehicle Automation 3 written by Gereon Meyer and published by Springer. This book was released on 2016-07-01 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited book comprises papers about the impacts, benefits and challenges of connected and automated cars. It is the third volume of the LNMOB series dealing with Road Vehicle Automation. The book comprises contributions from researchers, industry practitioners and policy makers, covering perspectives from the U.S., Europe and Japan. It is based on the Automated Vehicles Symposium 2015 which was jointly organized by the Association of Unmanned Vehicle Systems International (AUVSI) and the Transportation Research Board (TRB) in Ann Arbor, Michigan, in July 2015. The topical spectrum includes, but is not limited to, public sector activities, human factors, ethical and business aspects, energy and technological perspectives, vehicle systems and transportation infrastructure. This book is an indispensable source of information for academic researchers, industrial engineers and policy makers interested in the topic of road vehicle automation.