225 research outputs found
Cell Phone Demand and Consumer Learning - An Empirical Analysis
A structural model is used in this paper to analyze the demand and
learning behavior in cell phone market. We assume that the cell phone
consumption can be divided into a high-value part and a low-value part.
The consumers are assumed to be uncertain about the exogenous shock of
the need for high-value usage and also their preferences over the
low-value usage. Meanwhile, we assume that the consumers' knowledge
improves over time. As a result, the match between their plan choice and
consumption pattern becomes better. Such a learning behavior is
supported by the data set. Bayesian updating is used to represent the
learning. The estimates of the parameters are obtained and compared to
the benchmarks from previous research
Interest-Based Self-Organizing Peer-to-Peer Networks: A Club Economics Approach
Improving the information retrieval (IR) performance of peer-to-peer
networks is an important and challenging problem. Recently, the computer
science literature has attempted to address this problem by improving IR
search algorithms. However, in peer-to-peer networks, IR performance is
determined by both technology and user behavior, and very little
attention has been paid in the literature to improving IR performance
through incentives to change user behavior. We address this gap by
combining the club goods economics literature and the IR literature to
propose a next generation file sharing architecture. Using the popular
Gnutella 0.6 architecture as context, we conceptualize a Gnutella
ultrapeer and its local network of leaf nodes as a "club" (in
economic terms). We specify an information retrieval-based utility model
for a peer to determine which clubs to join, for a club to manage its
membership, and for a club to determine to which other clubs they should
connect. We simulate the performance of our model using a unique
real-world dataset collected from the Gnutella 0.6 network. These
simulations show that our club model accomplishes both performance
goals. First, peers are self-organized into communities of interest - in
our club model peers are 85% more likely to be able to obtain content
from their local club than they are in the current Gnutella 0.6
architecture. Second, peers have increased incentives to share content -
our model shows that peers who share can increase their recall
performance by nearly five times over the performance offered to
free-riders. We also show that the benefits provided by our club model
outweigh the added protocol overhead imposed on the network for the most
valuable peers
What’s It To You? A Survey of Online Privacy Concerns and Risks
Finding information about privacy practices can be difficult: privacy
policies often do not present this information in an accessible way.
People typically do not know how or for what purpose their personal
information, gathered online, will be used. When asked, people
frequently express concerns about their privacy, but their behavior
often does not reflect their concerns. We conducted an online survey to
examine participants' online privacy concerns, focusing especially on
the online shopping context. We asked participants about several
scenarios related to the privacy of personal information. We found that
Privacy Finder, a P3Penhanced search engine, provides information that
addresses the scenarios that participants believe are most likely to
occur. We also asked participants about a wide range of items for
purchase online to evaluate which types of items are more likely to
raise privacy concerns
Network Effects and Switching Costs In the Market for Routers and Switches
This research examines the impact of switching costs on vendor choice in
the market for routers and switches. We show that despite the use of
open standards which attempt to enhance interoperabilities for
equipments from different vendors, vendors in this market are able to
maintain high switching costs. Because routers and switches are
networked goods, switching costs may arise from prior investments made
at the same establishment and/or at other establishments within the same
firm. We study how the introduction of switches into the LAN market
affected vendor choice in routers. In particular, we provide evidence of
significant cross-product switching costs and sizeable shopping costs
when buyers purchase routers and switches simultaneously. However, we
also show that the introduction of switches may have temporarily reduced
switching costs for router buyers investing in switches
Cell Phone Demand and Consumer Learning - An Empirical Analysis
A structural model is used in this paper to analyze the demand and
learning behavior in cell phone market. We assume that the cell phone
consumption can be divided into a high-value part and a low-value part.
The consumers are assumed to be uncertain about the exogenous shock of
the need for high-value usage and also their preferences over the
low-value usage. Meanwhile, we assume that the consumers' knowledge
improves over time. As a result, the match between their plan choice and
consumption pattern becomes better. Such a learning behavior is
supported by the data set. Bayesian updating is used to represent the
learning. The estimates of the parameters are obtained and compared to
the benchmarks from previous research
Interest-Based Self-Organizing Peer-to-Peer Networks: A Club Economics Approach
Improving the information retrieval (IR) performance of peer-to-peer
networks is an important and challenging problem. Recently, the computer
science literature has attempted to address this problem by improving IR
search algorithms. However, in peer-to-peer networks, IR performance is
determined by both technology and user behavior, and very little
attention has been paid in the literature to improving IR performance
through incentives to change user behavior. We address this gap by
combining the club goods economics literature and the IR literature to
propose a next generation file sharing architecture. Using the popular
Gnutella 0.6 architecture as context, we conceptualize a Gnutella
ultrapeer and its local network of leaf nodes as a "club" (in
economic terms). We specify an information retrieval-based utility model
for a peer to determine which clubs to join, for a club to manage its
membership, and for a club to determine to which other clubs they should
connect. We simulate the performance of our model using a unique
real-world dataset collected from the Gnutella 0.6 network. These
simulations show that our club model accomplishes both performance
goals. First, peers are self-organized into communities of interest - in
our club model peers are 85% more likely to be able to obtain content
from their local club than they are in the current Gnutella 0.6
architecture. Second, peers have increased incentives to share content -
our model shows that peers who share can increase their recall
performance by nearly five times over the performance offered to
free-riders. We also show that the benefits provided by our club model
outweigh the added protocol overhead imposed on the network for the most
valuable peers
Surtrac for the People: Upgrading the Surtrac Pittsburgh Deployment to Incorporate Pedestrian Friendly Extensions and Remote Monitoring Advances
With research funding from the Traffic21 Institute\u2019s Mobility21 University Transportation Center Program, Dr. Stephen Smith, Director of the Intelligent Coordination and Logistics Laboratory at Carnegie Mellon University, developed the Scalable Urban Traffic Control (Surtrac), the world\u2019s first decentralized smart adaptive traffic signal system. This system significantly improves traffic throughput, trip delays and pollution along congested roads controlled by traffic lights. The system applies artificial intelligence to traffic signals equipped with cameras or radars adapting in realtime to dynamic traffic patterns of complex urban grids, experienced in neighborhoods like East Liberty in the City of Pittsburgh. Dr. Smith worked with the City of Pittsburgh, Southwestern Pennsylvania Commission (regional MPO), Pennsylvania Department of Transportation (PennDOT), University of Pittsburgh Medical Center, and neighborhood groups on a pilot deployment of Surtrac, which resulted in a 40% reduction in vehicle wait time and a 20% reduction in emissions. The original deployment of nine intersections in 2012 has expanded to 50 intersections and is now funded by the U.S. Department of Transportation (USDOT)\u2019s Advanced Transportation Congestion Management Technology Deployment (ATCMTD), and PennDOT grants for an additional 150 intersections in Pittsburgh. With two patents from the UTC research, Dr. Smith\u2019s Pittsburgh-based company Rapid Flow Technologies has created eight jobs and currently has commercial deployments in Atlanta, GA; Portland, ME and Needham and Quincy MA
An Empirical Analysis of Cellular Voice and Data services
Cellular telephony and associated data services has been a major social
phenomena for well over a decade now. It has changed the way - in some
countries more than others - in which people communicate. In many
countries in Northern Europe and Asia, its penetration rates are very
high and in others less so but in all cases it has engendered change at
multiple levels - socially as noted and in terms of market structure and
competition with the established Incumbent Local Exchange and Inter
Exchange service providers. However, there has been little work
published in the academic literature on user consumption of cellular
voice and data services. This has been due to the unavailability of
longitudinal data at the individual user level on their consumption of
voice and data services. We have such data from a large cellular service
provider in Asia. Demand for voice and data services is influenced by
the tariffs or 'service plans' offered by firms. In our analysis we
empirically estimate the drivers for cellular services how demographic
and plan characteristics affect the user choices. We first provide a
theoretical model and then provide insight into consumption patterns
over a one year period of cellular voice and data services and relate it
to service plan design
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Explorer-II: Wireless Self-Powered Visual and NDE Robotic Inspection System for Live Gas Distribution Mains
Carnegie Mellon University (CMU) under contract from Department of Energy/National Energy Technology Laboratory (DoE/NETL) and co-funding from the Northeast Gas Association (NGA), has completed the overall system design, field-trial and Magnetic Flux Leakage (MFL) sensor evaluation program for the next-generation Explorer-II (X-II) live gas main Non-destructive Evaluation (NDE) and visual inspection robot platform. The design is based on the Explorer-I prototype which was built and field-tested under a prior (also DoE- and NGA co-funded) program, and served as the validation that self-powered robots under wireless control could access and navigate live natural gas distribution mains. The X-II system design ({approx}8 ft. and 66 lbs.) was heavily based on the X-I design, yet was substantially expanded to allow the addition of NDE sensor systems (while retaining its visual inspection capability), making it a modular system, and expanding its ability to operate at pressures up to 750 psig (high-pressure and unpiggable steel-pipe distribution mains). A new electronics architecture and on-board software kernel were added to again improve system performance. A locating sonde system was integrated to allow for absolute position-referencing during inspection (coupled with external differential GPS) and emergency-locating. The power system was upgraded to utilize lithium-based battery-cells for an increase in mission-time. The resulting robot-train system with CAD renderings of the individual modules. The system architecture now relies on a dual set of end camera-modules to house the 32-bit processors (Single-Board Computer or SBC) as well as the imaging and wireless (off-board) and CAN-based (on-board) communication hardware and software systems (as well as the sonde-coil and -electronics). The drive-module (2 ea.) are still responsible for bracing (and centering) to drive in push/pull fashion the robot train into and through the pipes and obstacles. The steering modules and their arrangement, still allow the robot to configure itself to perform any-angle (up to 90 deg) turns in any orientation (incl. vertical), and enable the live launching and recovery of the system using custom fittings and a (to be developed) launch-chamber/-tube. The battery modules are used to power the system, by providing power to the robot's bus. The support modules perform the functions of centration for the rest of the train as well as odometry pickups using incremental encoding schemes. The electronics architecture is based on a distributed (8-bit) microprocessor architecture (at least 1 in ea. module) communicating to a (one of two) 32-bit SBC, which manages all video-processing, posture and motion control as well as CAN and wireless communications. The operator controls the entire system from an off-board (laptop) controller, which is in constant wireless communication with the robot train in the pipe. The sensor modules collect data and forward it to the robot operator computer (via the CAN-wireless communications chain), who then transfers it to a dedicated NDE data-storage and post-processing computer for further (real-time or off-line) analysis. The prototype robot system was built and tested indoors and outdoors, outfitted with a Remote-Field Eddy Current (RFEC) sensor integrated as its main NDE sensor modality. An angled launcher, allowing for live launching and retrieval, was also built to suit custom angled launch-fittings from TDW. The prototype vehicle and launcher systems are shown. The complete system, including the in-pipe robot train, launcher, integrated NDE-sensor and real-time video and control console and NDE-data collection and -processing and real-time display, were demonstrated to all sponsors prior to proceeding into final field-trials--the individual components and setting for said acceptance demonstration are shown. The launcher-tube was also used to verify that the vehicle system is capable of operating in high-pressure environments, and is safely deployable using proper evacuating/purging techniques for operation in the potentially explosive natural gas environment. The test-setting and environment for safety-certification of the X-II robot platform and the launch and recovery procedures, is shown. Field-trials were successfully carried out in a live steel pipeline in Northwestern Pennsylvania. The robot was launched and recovered multiple times, travelling thousands of feet and communicating in real time with video and command-and-control (C&C) data under remote operator control from a laptop, with NDE sensor-data streaming to a second computer for storage, display and post-processing. Representative images of the activities and systems used in the week-long field-trial are shown. CMU also evaluated the ability of the X-II design to be able to integrate an MFL sensor, by adding additional drive-/battery-/steering- and support-modules to extend the X-II train
Innovative Approaches to Educate Our Future Transportation Workforce
In April 2012, the Council of University Transportation Centers sponsored a National Transportation Workforce Summit to address the dynamic needs of the future transportation workforce. One stated objective was to promote greater visibility for transportation careers in industry, government, and academia. Since then, Carnegie Mellon university\u2019s (CMU) Technologies for Safe and Efficient Transportation (T-SET), a U.S. Department of Transportation funded University Transportation Center (UTC), has developed a strategic approach. The approach includes exposing a multidisciplinary group of students to the world of transportation, demonstrating how their skills can be applied by providing real world transportation research projects, creating job opportunities by linking students with real world transportation partners, and exposing industry to innovative transportation research
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