Overcoming Barriers for the Wide-scale Adoption of Standardized Real-time Transit Information

In the last few years, a real-time counterpart to GTFS, GTFS-realtime [10], has begun to emerge, with agencies sharing their real-time data in this format. Previously, real-time transit information had only been shared in proprietary formats specific to each vendor or agency. GTFS-realtime offers the opportunity for application developers to create a mobile app that can function across a large number of cities and agencies, and for practitioners and researchers to be able to easily study and compare actual system performance across different transit systems using the same tools, without the overhead of manually transforming data into a consistent format. Having real-time transit data available in a common format is a key pillar for real-time multimodal information systems

Webinar: Meeting & Exceeding Mobility User Expectations with Real-Time Transit Information

Every day transit riders ask the same question: when’s the next one coming? To answer this question, transit agencies are transitioning to providing real-time transit information through smartphones or displayed at transit stops. The proliferation of transit planning and real time arrival tools that have hit the market over the past decade is staggering. Yet with transit ridership on the decline, agencies can’t afford to ignore the importance of providing accurate, real time information to their customers. Real-time transit information improves the reliability and efficiency of passenger travel, but barriers have prevented some transit agencies from adopting the GTFSrealtime v1.0 technology. A new NITC-funded study in May led by Sean Barbeau of the University of South Florida seeks to remove some of these barriers to make real-time transit info a universal amenity. As a public agency partner, moovel focuses on delivering simple, frictionless and accurate information through mobile applications. From mobile ticketing to multi/intermodal trip planning, booking and payment, moovel’s mobile apps take a customer-first approach to enhance the customer experience through an intuitive mobile solution. This webinar will discuss the lessons learned from using GTFS and GTFS-realtime data in real-world applications and how these experiences lead to the development of the GTFS Best Practices (http://gtfs.org/best-practices/), GTFS-realtime v2.0 (https://developers.google.com/transit/gtfs-realtime/), and the open-source GTFS-realtime Validator tool (https://github.com/CUTR-at-USF/gtfs-realtime-validator). These new tools and standards will help reduce the time needed to develop, test, deploy, and maintain GTFS and GTFS-realtime feeds, which will in turn lead to better quality real-time information for transit riders and better operational and analytics information for transit agencies going forward. The presentation will also discuss the challenges and experiences faced by moovel as a vendor in working with agency data to meet modern, customer expectations in delivering accurate, real-time transportation data. KEY LEARNING OUTCOMES Understanding of how customer expectations shape the delivery of information/data Understanding of how transit agencies and their vendors can follow GTFS Best Practices and use the new GTFS-realtime v2.0 specification when implementing and maintaining data feeds, including putting in RFP requirements Challenges of working with multiple transportation providers to provide accurate real-time information Lessons learned from numerous focus groups and feedback studies Learn how to run the GTFS-realtime Validator tool on data regularly to maintain high-quality feeds Where the future of smart apps will take us and how we need to prepare for ithttps://pdxscholar.library.pdx.edu/trec_webinar/1032/thumbnail.jp

G-Sense: a scalable architecture for global sensing and monitoring

The pervasiveness of cellular phones combined with Internet connectivity, GPS embedded chips, location information, and integrated sensors provide an excellent platform to collect data about the individual and its surrounding environment. As a result, new applications have recently appeared to address large-scale societal problems as well as improve the quality of life of the individual. However, these new applications, recently called location-based services, participatory sensing, and human-centric sensing, bring many new challenges, one of them being the management of the huge amount of traffic (data) they generate. This article presents G-Sense, for Global-Sense, an architecture that integrates mobile and static wireless sensor networks in support of location-based services, participatory sensing, and human-centric sensing applications. G-Sense includes specific mechanisms to control the amount of data generated by these applications while meeting the application requirements. Furthermore, it creates a network of servers organized in a peer-to-peer architecture to address scalability and reliability issues. An example prototype application is presented along with some basic results and open research issues

A location-aware framework for intelligent real-time mobile applications

The Location-Aware Information Systems Client (LAISYC) supports intelligent, real-time, mobile applications for GPS-enabled mobile phones by dynamically adjusting platform parameters for application performance while conserving device resources such as battery life

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Overcoming Barriers to Real-time Transit Information

This project focused on the community-driven creation of the GTFS-realtime v2.0 format, which establishes better guidance for transit agencies, application developers, and automatic vehicle location system vendors on what fields are required or optional under various transit use cases

Closing the Loop: Improving Transit through Crowdsourced Information

Offering real-time arrival information to riders via mobile applications has been shown to improve the rider’s perception of transit, and even increase ridership. This direct connection to riders also offers the transit agency an opportunity to collect feedback on how transit service and infrastructure can be improved, including pedestrian and bike access to transit. These improvements will lead to an enhanced customer experience and can potentially help address Title VI access equity concerns. However, managing the sheer volume of this rider feedback can be very challenging, especially when various departments and agencies (e.g., city/county government) are involved (e.g., who owns the bench by the bus stop?). This paper discusses the design and deployment of a pilot project in Tampa, Florida, which focused on the improvement of the feedback loop from riders back to transit agencies, local government, and departments of transportation. This project made enhancements to the open-source OneBusAway mobile app, originally deployed in Tampa in 2013, to include support for the Open311 standard for issue reporting. Open311 support gives agencies the option of selecting a hosted issue management solution such as SeeClickFix.com and PublicStuff.com, or the option to utilize existing open-source Open311-compliant software. Lessons learned from regional collaboration surrounding issue reporting and infrastructure improvements are discussed, as are the technical design and challenges behind implementing such a system. The results of the first 6 months of system deployment covering 677 issue reports are presented, including specific examples of cross-jurisdictional and multimodal issues reported by the public

A Location-Aware Architecture Supporting Intelligent Real-Time Mobile Applications

This dissertation presents LAISYC, a modular location-aware architecture for intelligent real-time mobile applications that is fully-implementable by third party mobile app developers and supports high-precision and high-accuracy positioning systems such as GPS. LAISYC significantly improves device battery life, provides location data authenticity, ensures security of location data, and significantly reduces the amount of data transferred between the phone and server. The design, implementation, and evaluation of LAISYC using real mobile phones include the following modules: the GPS Auto-Sleep module saves battery energy when using GPS, maintaining acceptable movement tracking (approximately 89% accuracy) with an approximate average doubling of battery life. The Location Data Signing module adds energy-efficient data authenticity to this architecture that is missing in other architectures, with an average approximate battery life decrease of only 7%. The Session Management and Adaptive Location Data Buffering modules also contribute to battery life savings by providing energy-efficient real-time data communication between a mobile phone and server, increasing the average battery life for application data transfer by approximately 28% and reducing the average energy cost for location data transfer by approximately 38%. The Critical Point Algorithm module further reduces battery energy expenditures and the amount of data transferred between the mobile phone and server by eliminating non-essential GPS data (an average 77% reduction), with an average doubling of battery life as the interval of time between location data transmissions is doubled. The Location Data Encryption module ensures the security of the location data being transferred, with only a slight impact on battery life (i.e., a decrease of 4.9%). The LAISYC architecture was validated in two innovative mobile apps that would not be possible without LAISYC due to energy and data transfer constraints. The first mobile app, TRAC-IT, is a multi-modal travel behavior data collection tool that can provide simultaneous real-time location-based services. In TRAC-IT, the GPS Auto-Sleep, Session Management, Adaptive Location Data Buffering, Critical Point algorithm, and the Session Management modules all contribute energy savings that enable the phone\u27s battery to last an entire day during real-time high-resolution GPS tracking. High-resolution real-time GPS tracking is critical to TRAC-IT for reconstructing detailed travel path information, including distance traveled, as well as providing predictive, personalized traffic alerts based on historical and real-time data. The Location Data Signing module allows transportation analysts to trust information that is recorded by the application, while the Location Data Encryption module protects the privacy of users\u27 location information. The Session Management, Adaptive Location Data Buffering, and Critical Point algorithm modules allow TRAC-IT to avoid data overage costs on phones with limited data plans while still supporting real-time location data communication. The Adaptive Location Data Buffering module prevents tracking data from being lost when the user is outside network coverage or is on a voice call for networks that do not support simultaneous voice and data communications. The second mobile app, the Travel Assistance Device (TAD), assists transit riders with intellectual disabilities by prompting them when to exit the bus as well as tracking the rider in real-time and alerting caregivers if they are lost. In the most recent group of TAD field tests in Tampa, Florida, TAD provided the alert in the ideal location to transit riders in 100% (n = 33) of tests. In TAD, the GPS Auto-Sleep, Session Management, Adaptive Location Data Buffering, Critical Point algorithm, and the Session Management modules all contribute energy savings that enable the phone\u27s battery to last an entire day during real-time high-resolution GPS tracking. High-resolution GPS tracking is critical to TAD for providing accurate instructions to the transit rider when to exit the bus as well as tracking an accurate location of the traveler so that caregivers can be alerted if the rider becomes lost. The Location Data Encryption module protects the privacy of the transit rider while they are being tracked. The Session Management, Adaptive Location Data Buffering, and Critical Point algorithm modules allow TAD to avoid data overage costs on phones with limited data plans while still supporting real-time location data communication for the TAD tracking alert features. Adaptive Location Data Buffering module prevents transit rider location data from being lost when the user is outside network coverage or is on a voice call for networks that do not support simultaneous voice and data communications

Improving Access to Transit through Crowdsourced Information

The purpose of this research was to facilitate the ongoing collection of information from the public about potential areas of multimodal service and infrastructure improvements and easily share these problems with transit agencies, departments of transportation, and city and county governments. The research team implemented open-source software that leveraged the Open311 issue-reporting standard to capture various types of data from actual users of public transportation via the OneBusAway mobile app, a real-time transit information system. Lessons learned from regional collaboration surrounding issue reporting and infrastructure improvements are discussed, as are the technical design and challenges behind implementing such a system. The results of six months of system deployment in Hillsborough and Pinellas Counties are presented, including specific examples of cross-jurisdictional and multimodal issues reported by the public. Using this crowd-sourced data and issue management tools, transit agencies, departments of transportation, and city and county government will be able to better target improvements to bike, pedestrian, and transit infrastructure