2015 Annual Report Transportation Research Center for Livable Communities

Table of Contents Messages from the Director and Representatives TRCLC Mission and Objectives Center Personnel Research Investigators Consortia Our Research List of Research Projects Highlighted Projects Technology Transfer and Outreach Activities Student Awards Upcoming Event

Do I Care Enough? Using a Prosocial Tendencies Measure to Understand Twitter Users Sharing Behavior for Minor Public Safety Incidents

Social media has been used to assist victims of crises, especially large-scale disasters. Research describes the importance of the crowd who are the first witnesses to any sort of crime or disaster. Among others, this paper focuses on smaller scale public safety incidents such as suspicious activities, and minor robberies. We investigate whether prosocial tendencies affect Twitter users’ decisions to share minor public safety incidents on Twitter. The scale used has six subscales including: public, anonymous, dire, emotional, compliant, and altruism. The data (N=363) was collected through Mechanical Turk using an online anonymous survey. Initial results showed a positive relationship between being prosocial and sharing public safety incidents on Twitter. However, once additional variables related to Twitter use were introduced (number of public safety official accounts followed, news exposure on social media, and tweet/retweet frequency), these variables fully mediated the relationship. Limitations and design implications are discussed

Sociotechnical co-production of planning information : opportunities and limits of crowdsourcing for the geography and planning of bicycle transportation

Urban planners deploy civic technologies to engage publics with digital tools in a relative vacuum of theory, understanding of challenges, or benefits. The issue, Lewis Mumford might have framed, could be of authoritarian and democratic technics—whether the technology contributes more to top-down control or bottom-up understanding. Building from collaborative planning theory, co-production suggests ways people can leverage technologies to build urban solutions with or without professional planners. Empirical research shows that crowdsourcing to address planning questions with digital civic platforms can help fill or mitigate information gaps, including support for bicycling as a safe and comfortable travel mode. However, no research has addressed how crowdsourced information for bicycle planning offers new insights for safety, the geography of participation, or how its social construction impacts its representation of bicycling in a community. A new framework for evaluating co-productive planning is proposed, considering legitimacy, accessibility, social learning, transparency, and representation (LASTR). This dissertation addresses these concerns of safety, geography, and social construction through the LASTR framework using mixed-methods case studies in Portland, Oregon, and Austin, Texas. Bicycle volumes and street ratings through the crowdsourcing platform, along with geographic information system environmental data, and interviews with thirty-three informants form the basis for evaluating these issues. Viewed from pragmatism and social construction of technology, the social processes of planning and technological developments are intertwined and traced in tandem. The first three chapters frame the problems, build a background in theory, and describe the research questions, planning contexts, and data for analysis. The next three chapters are empirical, evaluating the use of crowdsourced information for bicycle safety, comparing the geography of crowdsourced participation with in-person meetings from both cities’ most recent bicycle planning process, and tracing the sociotechnical representation of crowdsourcing bicyclist information through interviews and case materials. The final chapter summarizes the findings and implications for practice and research. This dissertation shows that the biased representation of bicycling in these two crowdsourcing cases pose opportunities to identify safer bicycling routes and expand public participation geographies, but could exacerbate problems with aligning public improvements with the users of a specific technological approach. Further, the construct of crowdsourcing for urban planning remains flexible and therefore merits further study and knowledge transfer for practitioners and students.Community and Regional Plannin

SciTech News Volume 71, No. 2 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division 9 Aerospace Section of the Engineering Division 12 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 14 Reviews Sci-Tech Book News Reviews 16 Advertisements IEEE

Swift trust and behavioral change: facilitating factors of crowdsourcing in chronic disease prevention

Behind Internet usage habits there is a common vocabulary: trust. In order to promote preventive medicine, Internet medical care has been trying to cultivate user habits and behavior change, but whoever increases trust can go further. The Internet has accelerated the pace of work and life and generalized the temporary involvement of individuals and teams. In many organizations, there is usually no time to develop trust among team members or between the team and customers in traditional ways such as mutual familiarity, experience sharing, mutual disclosure, and verification of commitments. These new situations have led to the study of a new form of trust: "swift trust". According to Hurd et al. (2017), "swift trust" focuses on expecting that a person has the necessary attributes to be relied upon. In the "swift trust" theory, a group or individual assumes the existence of trust initially, and later verifies and adjusts trust beliefs accordingly. Faced with the problem of the rapid spread of chronic diseases and the high proportion of medical expenses needed to combat them and that have posed challenges to the national finances in China, this thesis focuses on studying the factors that may facilitate the establishment of "swift trust" in the Internet based chronic disease crowdsourcing model. Grounded on the idea that trust affects behavior and speed affects efficiency, we have reviewed extant literature and, with the help of ROST Content Mining (ROST-CM) text mining software, we dug millions of Internet data and conducted in-depth research on the "swift trust" problem. Results, later verified through two ongoing healthcare projects showed that "profession" followed by "platform", "dissemination" and "propensity" are the most critical factors that affect the establishment of swift trust. These results may be of interest to professionals, organizations and government decision makers in need of establishing and winning trust, and particularly "swift trust", as an essential ingredient in the sharing economy.Existe uma palavra comum por detrás de todos os hábitos de utilização da Internet: confiança. Com o objetivo de promover a medicina preventiva, alguns cuidados médicos prestados através da Internet têm vindo a procurar motivar os utilizadores para uma mudança de hábitos e comportamentos, mas apenas quem conseguir ganhar a confiança poderá ir mais longe. A Internet acelerou o ritmo da vida e do trabalho e generalizou a participação temporária de indivíduos e grupos. Em muitas organizações, não há tempo suficiente para se criar confiança entre os membros de um grupo ou entre grupos e indivíduos através de formas tradicionais como a convivência e o conhecimento mútuos, a partilha de experiências ou a verificação do cumprimento de compromissos. Esta situação levou ao estudo de uma nova forma de confiança: "a confiança imediata". Hurd et al. (2017) afirmam que este conceito se refere à expetativa de que uma determinada pessoa reúna os atributos necessários para ser confiável. Segundo a teoria que estuda a "confiança imediata", um grupo ou indivíduo assume desde logo a presença de confiança e reserva para mais tarde a confirmação da sua existência. Considerando os desafios colocados pelo rápido desenvolvimento de doenças crónicas num país tão populoso como a China e a necessidade de as combater, esta tese estuda os fatores que poderão facilitar a construção de "confiança imediata" no modelo de colaboração aberta através da Internet com vista à prevenção destas doenças. Partindo do princípio de que a confiança afeta os comportamentos e de que a rapidez afeta a eficiência procedeu-se à revisão de literatura sobre o tema e, com a ajuda do "software" de mineração de texto ROST-CM (ROST Content Mining) foram recolhidos e tratados milhões de dados extraídos da Internet. Os resultados foram depois confrontados com a prática de dois projetos na área da saúde e revelaram que a "profissão" seguida da "plataforma", "disseminação" e "propensão" são os fatores que mais contribuem para a formação de "confiança imediata". Os resultados obtidos poderão ser de interesse para profissionais, organizações e decisores governamentais que necessitam de construir e manter confiança e, em particular "confiança imediata", enquanto ingrediente essencial na economia de partilha

Usability of disaster apps : understanding the perspectives of the public as end-users : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Emergency Management at Massey University, Wellington, New Zealand

Listed in 2020 Dean's List of Exceptional ThesesMultiple smartphone applications (apps) exist that can enhance the public’s resilience to disasters. Despite the capabilities of these apps, they can only be effective if users find them usable. Availability does not automatically translate to usability nor does it guarantee continued usage by the target users. A disaster app will be of little or no value if a user abandons it after the initial download. It is, therefore, essential to understand the users’ perspectives on the usability of disaster apps. In the context of disaster apps, usability entails providing the elements that effectively facilitate users in retrieving critical information, and thus enabling them to make decisions during crises. Establishing good usability for effective systems relies upon focussing on the user whereby technological solutions match the user’s needs and expectations. However, most studies on the usability of disaster context technologies have been conducted with emergency responders, and only a few have investigated the publics’ perspectives as end-users. This doctoral project, written within a ‘PhD-thesis-with-publication’ format, addresses this gap by investigating the usability of disaster apps through the perspectives of the public end-users. The investigation takes an explicitly perceived usability standpoint where the experiences of the end-users are prioritised. Data analysis involved user-centric information to understand the public’s context and the mechanisms of disaster app usability. A mixed methods approach incorporates the qualitative analysis of app store data of 1,405 user reviews from 58 existing disaster apps, the quantitative analysis of 271 survey responses from actual disaster app users, and the qualitative analysis of usability inquiries with 18 members of the public. Insights gathered from this doctoral project highlight that end-users do not anticipate using disaster apps frequently, which poses particular challenges. Furthermore, despite the anticipated low frequency of use, because of the life-safety association of disasters apps, end-users have an expectation that the apps can operate with adequate usability when needed. This doctoral project provides focussed outcomes that consider such user perspectives. First, an app store analysis investigating user reviews identified new usability concerns particular to disaster apps. It highlighted users’ opinion on phone resource usage and relevance of content, among others. More importantly, it defined a new usability factor, app dependability, relating to the life-safety context of disaster apps. App dependability is the degree to which users’ perceive that an app can operate dependably during critical scenarios. Second, the quantitative results from this research have contributed towards producing a usability-continuance model, highlighting the usability factors that affect end-users’ intention to keep or uninstall a disaster app. The key influences for users’ intention to keep disaster apps are: (1) users’ perceptions as to whether the app delivers its function (app utility), (2) whether it does so dependably (app dependability), and (3) whether it presents information that can be easily understood (user-interface output). Subsequently, too much focus on (4) user-interface graphics and (5) user-interface input can encourage users to uninstall apps. Third, the results from the qualitative analysis of the inquiry data provide a basis for developing guidelines for disaster app usability. In the expectation of low level of engagement with disaster app users, the guidelines list recommendations addressing information salience, cognitive load, and trust. This doctoral project provides several contributions to the body of knowledge for usability and disaster apps. It reiterates the importance of investigating the usability of technological products for disasters and showcases the value of user-centric data in understanding usability. It has investigated usability with particular attention to the end-users’ perspectives on the context of disaster apps and, thus, produces a theoretical usability-continuance model to advance disaster app usability research and usability guidelines to encourage responsible design in practice

A two-stage approach to ridesharing assignment and auction in a crowdsourcing collaborative transportation platform.

Collaborative transportation platforms have emerged as an innovative way for firms and individuals to meet their transportation needs through using services from external profit-seeking drivers. A number of collaborative transportation platforms (such as Uber, Lyft, and MyDHL) arise to facilitate such delivery requests in recent years. A particular collaborative transportation platform usually provides a two sided marketplace with one set of members (service seekers or passengers) posting tasks, and the another set of members (service providers or drivers) accepting on these tasks and providing services. As the collaborative transportation platform attracts more service seekers and providers, the number of open requests at any given time can be large. On the other hand, service providers or drivers often evaluate the first couple of pending requests in deciding which request to participate in. This kind of behavior made by the driver may have potential detrimental implications for all parties involved. First, the drivers typically end up participating in those requests that require longer driving distance for higher profit. Second, the passengers tend to overpay under a competition free environment compared to the situation where the drivers are competing with each other. Lastly, when the drivers and passengers are not satisfied with their outcomes, they may leave the platforms. Therefore the platform could lose revenues in the short term and market share in the long term. In order to address these concerns, a decision-making support procedure is needed to: (i) provide recommendations for drivers to identify the most preferable requests, (ii) offer reasonable rates to passengers without hurting driver’s profit. This dissertation proposes a mathematical modeling approach to address two aspects of the crowdsourcing ridesharing platform. One is of interest to the centralized platform management on the assignment of requests to drivers; and this is done through a multi-criterion many to many assignment optimization. The other is of interest to the decentralized individual drivers on making optimal bid for multiple assigned requests; and this is done through the use of prospect theory. To further validate our proposed collaborative transportation framework, we analyze the taxi yellow cab data collected from New York city in 2017 in both demand and supply perspective. We attempt to examine and understand the collected data to predict Uber-like ridesharing trip demands and driver supplies in order to use these information to the subsequent multi-criterion driver-to-passenger assignment model and driver\u27s prospect maximization model. Particularly regression and time series techniques are used to develop the forecasting models so that centralized module in the platform can predict the ridesharing demands and supply within certain census tracts at a given hour. There are several future research directions along the research stream in this dissertation. First, one could investigate to extend the models to the emerging concept of Physical Internet on commodity and goods transportation under the interconnected crowdsourcing platform. In other words, integrate crowdsourcing in prevalent supply chain logistics and transportation. Second, it\u27s interesting to study the effect of Uber-like crowdsourcing transportation platforms on existing traffic flows at the various levels (e.g., urban and regional)

15-07 App-based Crowd Sourcing of Bicycle and Pedestrian Conflict Data

Most agencies and decision-makers rely on crash and crash severity (property damage only, injury or fatality) data to assess transportation safety; however, in the context of public health where perceptions of safety may influence the willingness to adopt active transportation modes (e.g. bicycling and walking), pedestrian-vehicle and other similar conflicts may represent a better performance measure for safety assessment. For transportation safety, a clear conflict occurs when two parties’ paths cross and one of the parties must undertake an evasive maneuver (e.g. change direction or stop) to avoid a crash. Other less severe conflicts where paths cross but no evasive maneuver occurs may also impact public perceptions of safety. Most existing literature on conflicts focuses on vehicle conflicts and intersections. While some research has investigated bicycle and pedestrian conflicts, most of this has focused on the intersection environment. In this project, we propose field testing a crowd-sourced data app to better understand the continuum of conflicts (bicycle/pedestrian, bicycle/vehicle, and pedestrian/vehicle) experienced by pedestrians and cyclists; the study also tests the effectiveness of the app and its associated crowd-sourced data collection. This study assesses the data quality of the crowd sourced data and compares it to more traditional data sources while performing hot spot analysis. If widely adopted, the app will enable communities to create their own data collection efforts to identify dangerous sites within their neighborhoods. Agencies will have a valuable data source at low-cost to help inform their decision making related to bicycle and pedestrian education, enforcement, infrastructure, programs and policies

Exploring, understanding, then designing: twitter users’ sharing behavior for minor safety incidents

Social media has become an integral part of human lives. Social media users resort to these platforms for various reasons. Users of these platforms spend a lot of time creating, reading, and sharing content, therefore, providing a wealth of available information for everyone to use. The research community has taken advantage of this and produced many publications that allow us to better understand human behavior. An important subject that is sometimes discussed and shared on social media is public safety. In the past, Twitter users have used the platform to share incidents, share information about incidents, victims and perpetrators, and used it to provide help in distressed locations after an attack or after a natural disaster. Public safety officials also used Twitter to disseminate information to maintain and improve safety and seek information from the crowds. The previous focus of the research is mainly on significant public safety incidents; but, incidents with less severity matter too. The focus of this dissertation is on minor incidents and the aim is to understand what motivates social media users to share those incidents to maintain and increase public safety through design suggestions.This dissertation is comprised of three completed studies. The first study attempts to understand motivations to share public safety incidents on social media under the collective action theory lens. Collective action theory assumes that rational people will not participate in a public good unless there is a special incentive or an external motivation for them. In this study, public safety is considered as the public good. This study tests people’s willingness to share incidents on social media if: the victim is someone they know, if the location of the incident is close, and if there is some coercion to influence users willingness to share. General support is found for the hypotheses and collective action theory.In the second study, the focus is on internal motivations that stem from being prosocial. An established scale that measures six different traits of prosocial behavior is used. It is hypothesizes that prosocial behavior is positively related to decisions to share incidents on social media. The study also tests other mediating variables, namely: following news outlets on Twitter, following public safety officials on social media, frequency of tweeting/retweeting. Partial support for prosocial tendencies effect on decisions to share is found. The study also discoveres that the three mediating variables (number of public safety official accounts followed, news exposure on social media, and tweet/retweet frequency) fully mediates the relationship and that they have a significant positive effect on decisions to share. The third and final study complements the previous two and helps conclude the previous findings. A 2X2X2 online experiment design is conducted. The three manipulations are the availability of location information, platform authority availability, and availability of sender authority. The study hypothesizes that the three interventions will produce a significant positive relationship with decisions to share on Twitter. It is found that location information has no effect on sharing minor incidents on Twitter, however, participants are more likely to use a fictitious button that increases local exposure to minor public safety tweets. It is also found that the authority of the sender has a significant effect on decisions to share. On the other hand, platform authority does not show an effect on decisions to share public safety incidents on Twitter