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Evaluating interventions through long-term human mobility data: initial methods and lessons learned. The tool generated a rich dataset for research and evaluation of behavior that would otherwise be unobserved. The data enabled robust energy estimation of the program. Practice revealed a need for further refinement to reduce participant data input burden.
The rapid introduction of digital mobile communications systems is an important part of the emerging digital communications scene. These developments pose both a potential problem and a challenge. On one hand, these separate market driven developments can result in an uncontrolled mixture of analog and digital links which inhibit data modem services across the mobile/Public Switched network (PSTN). On the other hand, the near coincidence of schedules for development of some of these systems, i.e., Digital Cellular, Mobile Satellite, Land Mobile Radio, and ISDN, provides an opportunity to address interoperability problems by defining interfaces, control, and service standards that are compatible among these new services. In this paper we address the problem of providing data services interoperation between mobile terminals and data devices on the PSTN. The expected data services include G3 Fax, asynchronous data, and the government's STU-3 secure voice system, and future data services such as ISDN. We address a common architecture and a limited set of issues that are key to interoperable mobile data services. We believe that common mobile data standards will both improve the quality of data service and simplify the systems for manufacturers, data users, and service providers.
Market research revealed several key demands for an Automatic Vehicle Location (AVL) Global Positioning System (GPS) radio. The demands were for minimization of urban building blockage, easy programmability to minimize mobile data transmission costs, high accuracy for street map level coordination, interface capability with non-digital Specialized Mobile Radios (SMR), and a selling price close to that of alternatives such as Signposts and Loran-C. A team of experts was assembled to surmount these challenges and deliver a GPS radio for $500 to $1000, which operates at high accuracy in an urban environment and is plug-compatible with nearly all vehicle radios. Among the engineering and production breakthroughs described here are a unique Simultrac (Trademark) approach to satellite tracking, enabling up to eight GPS satellites to be used for position determination with a 2-channel receiver, and a receiver-in-a-microphone design. A powerful Application Specific Integrated Circuit (ASIC) allowed GPS to be brought within easy reach of millions of AVL users such as bus, taxi, and delivery vehicle fleets.
In this report, we summarize data availability of new shared mobility technologies by mobility type and region and analyze how shared mobility usage varies by time and by demographic factors in the United States. The shared mobility technologies include ridehailing from transportation network companies (TNC), bikeshare, and scooter share. There is a wide range in shared mobility usage per capita across the country, even within urban areas. We observe that there was steady growth of new shared mobility usage from 2015 to early 2020, before COVID-19 reduced overall ridership. An analysis focused on Chicago shows that despite the availability of good public transportation, high usage of new shared mobility modes is centered in high income communities, especially by households who own less vehicles. However, we find that TNC is used for first-mile and last-mile in lower income communities. Analysis on the bikeshare usage also shows that household income is shown to not be a statistically significant factor when accounting for other factors including employment density, population density, percentage of college graduates, and bike-lane proximity.
GPS-based travel surveys are widely used in mobility studies to gather crucial qualitative data, like purpose, transportation mode and replaced mode. However, survey response still poses a burden to users, especially in long-term mobility studies, leading to response fatigue. We explore a survey-assist strategy to ease this burden by a novel, user-level modeling approach that leverages past responses from each user to predict responses for new trips, without relying on external data sources like GIS data. We investigate three main algorithms for predicting responses: (i) clustering trips and extrapolating responses for similar trips, (ii) using random forest classification, and (iii) clustering that uses a hybrid algorithm to determine spatial structure, which is then fed as input to a classic random forest classifier. The clustering approach can flexibly predict responses for even complex qualitative survey questions; it achieved F-scores of 65%. The random forest pipeline uses architecture that restricts it to predicting three predetermined survey questions: trip purpose, mode, and replaced mode. However, it achieved F-scores of 78%. While the survey-assist approach has been implemented by several proprietary systems, to our knowledge, this is the first exploration in the academic literature. It follows that this is also the first rigorous evaluation of multiple algorithms that can implement the approach. The evaluation uses a large scale, publicly available, longitudinal dataset consisting of ~ 92k trips from 235 users over a period of roughly one and a half years. With this approach, travel surveys can be pre-filled with the predicted responses for each trip, thus streamlining the survey process for users. Combined with an active learning system that requests user input on low-confidence predictions, models can be updated and improved over time to better support the long-term collection of longitudinal qualitative data.
This presentation explores the interactions of Emerging Technology, Changing Urban Environments, and Urban Travel Behavior on Mobility and Energy Impacts. Key findings include quantifying impacts of emerging mobility, access and infrastructure associated with new mobility choices and services in urban areas. The results demonstrate new data collection methods and capabilities to understand and identify key levers to improve energy productivity of future mobility systems and of quickly evolving MaaS and COVID-19 contexts to monitor rapidly shifting urban mobility patterns. Under development are mode choice models for new modes versus traditional modes. Initial estimates are developed on the potential of workforce mobility strategies in shaping future energy-efficient mobility system impacts. For mode replacement and workforce mobility, this presentation offers observability into the changes in number of transactions per mode, after ride-hailing/TNC/MaaS introduction and in origins of workforce to enable user need-driven reimagining of efficient mobility systems: Seattle (SEA-TAC) airport: for every 100 new TNC transactions for ground transportation, ~27% replaced transit, 35% replaced parking, 17% replaced car rentals, and 21% replaced taxis. Similarly, at Denver International (DEN), ride-hailing transactions replaced transit, parking, car rental and taxis at a rate of 34.7%, 39.0%, 16.6%, and 9.7%, respectively. A workforce mobility tool, analysis/visualization techniques, and capabilities to understand and identify key job commute needs, new choices in access to job opportunities, and affordability aspects as levers to improve energy productivity of future integrated urban mobility systems.
Throughout the world, especially in dense urban environments, the quality of life is being negatively impacted by ever growing commute time. Travel, beyond commuting, is increasingly driven by door-to-door challenges ? not just gate-to-gate considerations. Air Mobility may be an approach to address these challenges, as it can effectively convert our 2D mobility system to a 3D mobility system, vastly increasing mobility options.
A method is being developed for utilizing readings of an ion-mobility spectrometer (IMS) to estimate molecular masses of ions that have passed through the spectrometer. The method involves the use of (1) some feature-based descriptors of structures of molecules of interest and (2) reduced ion mobilities calculated from IMS readings as inputs to (3) a neural network. This development is part of a larger effort to enable the use of IMSs as relatively inexpensive, robust, lightweight instruments to identify, via molecular masses, individual compounds or groups of compounds (especially organic compounds) that may be present in specific environments or samples. Potential applications include detection of organic molecules as signs of life on remote planets, modeling and detection of biochemicals of interest in the pharmaceutical and agricultural industries, and detection of chemical and biological hazards in industrial, homeland-security, and industrial settings.
It has been proposed to use differential minimum shift keying (DMSK) modems in spacecraft-based mobile communications systems. For an employment of these modems, it is necessary that the transmitted carrier frequency be known prior to signal detection. In addition, the time needed by the receiver to lock onto the carrier frequency must be minimized. The present article is concerned with a DMSK modem developed for the Mobile Satellite Service. This device demonstrated fast acquisition time and good performance in the presence of fading. However, certain problems arose in initial attempts to study the acquisition behavior of the AFC loop through breadboard techniques. The development of a software model of the AFC loop is discussed, taking into account two cases which were plotted using the model. Attention is given to a demonstration of the viability of the modem by an approach involving modeling and analysis of the frequency synchronizer.
Throughout the world, especially in dense urban environments, the quality of life is being negatively impacted by ever growing commute time. Travel, beyond commuting, is increasingly driven by door-to-door challenges ? not just gate-to-gate considerations. Air Mobility may be an approach to address these challenges, as it can effectively convert our 2D mobility system to a 3D mobility system, vastly increasing mobility options.
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When designing measures to control infectious disease spread, it is crucial to understand the structure of the population for which interventions are being implemented. Recent work has highlighted the need for models that incorporate demographic heterogeneity not just in age structure but also by socioeconomic status (SES). Appropriately capturing additional sources of population heterogeneity requires considerable data and model development. To understand the potential disagreement between SES-explicit or SES-agnostic disease models, we adapted Sandia’s Adaptive Recovery Model (ARM) model to consider differences in contact structure and mortality by Social Vulnerability Index (SVI) on a theoretical network. We also incorporated an Average network that did not consider SVI. By exploring disparities in vaccine and PPE uptake by SES and comparing to Average networks, as well as analyzing the influence of global vs. local contact, we found that the two model constructions often predicted different outcomes. Whether these differences are truly reflective of incorporating SES, and which model most closely represents reality, merits further investigation.
Determination of atmospheric and surface elemental and molecular composition of various solar system bodies is essential to the development of a firm understanding of the origin and evolution of the solar system. Furthermore, such data is needed to address the intriguing question of whether or not life exists or once existed elsewhere in the Solar System. As such, these measurements are among the primary scientific goals of NASA s current and future planetary missions. In recent years, significant progress toward both miniaturization and field portability of in situ analytical separation and detection devices have been made with future planetary explorations in mind. However, despite all these advances, accurate in situ identification of atmospheric and surface compounds remains a big challenge. In response to that we are developing various hardware and software tools which would enable us to uniquely identify species of interest in a complex chemical environment.
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The Telesat Mobile Incorporated (TMI) Mobile Data System (MDS) was developed to apply satellite technology to the transportation industry's requirement for a fleet management system. It will provide two-way messaging and automatic position reporting capabilities between dispatch centers and customers' fleets of trucks. The design was based on the Inmarsat L-Band space segment with system link parameters and margins adjusted to meet the land mobile satellite channel characteristics. The system interfaces with the Teleglobe Des Laurentides earth station at Weir, Quebec. The signaling protocols were derived from the Inmarsat Standard C packet signalling system with unique trucking requirements incorporated where necessary.