Increase In Market Penetration Research Articles

Evolution of technology investment and development of robotaxi services

Using artificial intelligence and autonomous driving technologies, robotaxis are expected to provide more intelligent, efficient, sustainable, and convenient services as technology advances and market penetration increases. This requires attention to technology investment and market competition at the different stages of robotaxi development. This study aims to reveal the changing rules of R&D investment and market competition for robotaxi services at various stages, considering subsidies, passenger preferences (i.e. walking aversion, safety, additional convenience), and the volatility of benefits from congestion reduction. We developed models to capture the competition between robotaxis and traditional taxis in the three phases of market development — namely, introduction, development, and dominance. The results of this study show that the optimal level of R&D investment is affected by factors such as R&D subsidies, passenger preferences, and the volatility of congestion reduction benefits and that it decreases during the development phase and may further decline during the dominance phase. Additionally, government R&D subsidies should be maintained at a reasonable level to avoid potential strategic conflicts with companies in technology investment and adoption of robotaxi services during the introduction phase. The transition from R&D subsidies to usage subsidies will depend on reaching the perceived safety threshold for robotaxi services. Reducing congestion can play a role similar to the deployment of usage subsidies to promote robotaxi services during the development phase, and can promote R&D investment, robotaxi services, and firm profits in the dominance phase. However, these benefits may be reduced by the volatility of congestion reduction. Policy recommendations and strategies are provided to promote future technological progress and market competition.

Optimizing the physical design and layout of a resilient wind, solar, and storage hybrid power plant

As wind and solar technologies improve and their costs decrease, the share of power produced by these sources will increase. As the market penetration increases, these power sources will need to provide grid services, such as dispatchability, in addition to providing energy. One way to reduce variability, provide higher quality power to the grid, and address local grid stability issues is through colocating wind and solar power plants. In addition to operating reliably during normal operating conditions, in scenarios with high penetrations of renewable generation, it is important that these hybrid plants can withstand production disruptions and continue to supply power despite prolonged resource reduction, extreme weather events, or other disruptions. In this paper, we present a methodology to optimize a wind–solar-battery hybrid power plant down to the component level that is resilient against production disruptions and that can continually produce some minimum required power. We introduce the models and assumptions we used to simulate a hybrid power plant as well as the design variable parameterization and specific methods we used to optimize the plant. We demonstrate the performance of our method by comparing a plant optimized for different objectives, generation outage durations, minimum power requirements, and power purchase agreements. Although the plant design is sensitive to model parameters and various other assumptions, our results demonstrate some of the optimal designs that occur in different scenarios and what one should expect when designing a hybrid wind–solar-storage power plant.

ILSSPP: Intelligent location sharing system with privacy preserving features for smart cities

Social networking services are increasingly accessed through mobile devices. This trend has prompted services such as Facebook and Google+to incorporate location as a de facto feature of user interaction. At the same time, services based on location such as Foursquare and Shopkick are also growing as smartphone market penetration increases. In fact, this growth is happening despite concerns (growing at a similar pace) about security and third-party use of private location information (e.g., for advertising). Nevertheless, service providers have been unwilling to build truly private systems in which they do not have access to location information. In this paper, we describe an architecture and a trial implementation of a privacy-preserving location sharing system called ILSSPP. The system protects location information from the service provider and yet enables fine grained location-sharing. One main feature of the system is to protect an individual’s social network structure. The pattern of location sharing preferences towards contacts can reveal this structure without any knowledge of the locations themselves. ILSSPP protects locations sharing preferences through protocol unification and masking. ILSSPP has been implemented as a standalone solution, but the technology can also be integrated into location-based services to enhance privacy.

Development and Evaluation of Cooperative Intersection Management Algorithm under Connected and Automated Vehicles Environment

Recent technological advancements in the automotive and transportation industry established a firm foundation for development and implementation of various connected and automated vehicle solutions around the globe. Wireless communication technologies such as the dedicated short-range communication protocol are enabling information exchange between vehicles and infrastructure. This research paper introduces an intersection management strategy for a corridor with automated vehicles utilizing vehicular trajectory-driven optimization method. Trajectory-Driven Optimization for Automated Driving provides an optimal trajectory for automated vehicles based on current vehicle position, prevailing traffic, and signal status on the corridor. All inputs are used by the control algorithm to provide optimal trajectories for automated vehicles, resulting in the reduction of vehicle delay along the signalized corridor with fixed-time signal control. The concept evaluation through microsimulation reveals that, even with low market penetration (i.e., less than 10%), the technology reduces overall travel time of the corridor by 2%. Further increase in market penetration produces travel time and fuel consumption reductions of up to 19.5% and 22.5%, respectively.

Integration of automated vehicles in mixed traffic: Evaluating changes in performance of following human-driven vehicles

The introduction of Automated Vehicles (AVs) into the transportation network is expected to improve system performance, but the impacts of AVs in mixed traffic streams have not been clearly studied. As AV’s market penetration increases, the interactions between conventional vehicles and AVs are inevitable but by no means clear. This study aims to create new knowledge by quantifying the behavioral changes caused when conventional human-driven vehicles follow AVs and investigating the impact of these changes (if any) on safety and the environment. This study analyzes data obtained from a field experiment by Texas A&M University to evaluate the effects of AVs on the behavior of a following human-driver. The dataset is comprised of nine drivers that attempted to follow 5 speed-profiles, with two scenarios per profile. In scenario one, a human-driven vehicle follows an AV that implements a human driver speed profile (base). In scenario two, the human-driven vehicle follows an AV that executes an AV speed profile. In order to evaluate safety, these scenarios are compared using time-to-collision (TTC) and several other driving volatility measures. Likewise, fuel consumption and emissions are used to investigate environmental impacts. Overall, the results show that AVs in mixed traffic streams can induce behavioral changes in conventional vehicle drivers, with some beneficial effects on safety and the environment. On average, a driver that follows an AV exhibits lower driving volatility in terms of speed and acceleration, which represents more stable traffic flow behavior and lower crash risk. The analysis showed a remarkable improvement in TTC as a result of the notably better speed adjustments of the following vehicle (i.e., lower differences in speeds between the lead and following vehicles) in the second scenario. Furthermore, human-driven vehicles were found to consume less fuel and produce fewer emissions on average when following an AV.

Estimation of Traffic Stream Density Using Connected Vehicle Data: Linear and Nonlinear Filtering Approaches.

The paper presents a nonlinear filtering approach to estimate the traffic stream density on signalized approaches based solely on connected vehicle (CV) data. Specifically, a particle filter (PF) is developed to produce reliable traffic density estimates using CV travel-time measurements. Traffic flow continuity is used to derive the state equation, whereas the measurement equation is derived from the hydrodynamic traffic flow relationship. Subsequently, the PF filtering approach is compared to linear estimation approaches; namely, a Kalman filter (KF) and an adaptive KF (AKF). Simulated data are used to evaluate the performance of the three estimation techniques on a signalized approach experiencing oversaturated conditions. Results demonstrate that the three techniques produce accurate estimates—with the KF, surprisingly, being the most accurate of the three techniques. A sensitivity of the estimation techniques to various factors including the CV level of market penetration, the initial conditions, and the number of particles in the PF is also presented. As expected, the study demonstrates that the accuracy of the PF estimation increases as the number of particles increases. Furthermore, the accuracy of the density estimate increases as the level of CV market penetration increases. The results indicate that the KF is least sensitive to the initial vehicle count estimate, while the PF is most sensitive to the initial condition. In conclusion, the study demonstrates that a simple linear estimation approach is best suited for the proposed application.

Controller Independent Software-in-the-Loop Approach to Evaluate Rule-Based Traffic Signal Retiming Strategy by Utilizing Floating Car Data

Floating car data present a cost-effective approach to observing the traffic state. This paper explores whether floating cars can substitute stationary detection devices (e.g., induction loops) for observers within traffic responsive control systems. A rule-based traffic control method at the local intersection level is proposed in this paper by utilizing the floating car data. The control method involves a three-fold approach: link-level speed forecasting, data-driven traffic flow estimation, and split optimization. To estimate traffic flow, a multivariable linear regression model is developed by utilizing forecasted link-level speed, signal control variables, and link length as predictors. The method is tested using a controller (hardware)-independent software-in-the-loop approach. Compared with the existing fixed-time control operating in Starnberg, Germany, the proposed method is able to improve the level of service of the signalized intersection when tested for different levels of market penetration of the floating cars. The findings underpin the use of floating car data in online traffic control applications; the benefits will increase with an increase in market penetration of floating cars. Overall, this paper presents a fully integrated technical system that is ready to be used in the field. The proposed system can be implemented at the tactical level of urban traffic-control hierarchy employed in Germany.

Pressure-Pair-Based Floor Localization System Using Barometric Sensors on Smartphones.

As smartphone technology advances and its market penetration increases, indoor positioning for smartphone users is becoming an increasingly important issue. Floor localization is especially critical to indoor positioning techniques. Numerous research efforts have been proposed for improving the floor localization accuracy using information from barometers, accelerometers, Bluetooth Low Energy (BLE), and Wi-Fi signals. Despite these existing efforts, no approach has been able to determine what floor smartphone users are on with near 100% accuracy. To address this problem, we present a novel pressure-pair based method called FloorPair, which offers near 100% accurate floor localization. The rationale of FloorPair is to construct a relative pressure map using highly accurate relative pressure values from smartphones with two novel features: first, we marginalized the uncertainty from sensor drifts and unreliable absolute pressure values of barometers by paring the pressure values of two floors, and second, we maintained high accuracy over time by applying an iterative optimization method, making our method sustainable. We evaluated the validity of the FloorPair approach by conducting extensive field experiments in various types of buildings to show that FloorPair is an accurate and sustainable floor localization method.

Potential Effects of Automated Vehicles on Road Transportation: A Literature Review

Abstract Automated vehicles (AVs) are one of the emerging technologies that can perform the driving task themselves. The market penetration of AVs is expected to get growth in the close future. Therefore, it is crucial to have an overall clue on how they play the role in the road transportation sector. Automation might be assumed to have a beneficial impact on many aspects related to road transportation. The current paper attempts to investigate this rough assumption by reviewing the literature on the potential effects of automated vehicles on road transportation. A comprehensive look at the overall potential effects of automated vehicles will show the entire picture, and not just a cropped portion of that, to the researchers, decision makers, and practitioners and helps them to identify the negative and positive effects as well as challenges and uncertainties towards this new technology. In this paper, literature findings on the potential effects of automated vehicles on traffic flow, pedestrians mobility, travel demand and travel pattern, safety and security, and energy consumption and emissions are reviewed and discussed. According to the literature, it is concluded that AVs, as their market penetration increases, promisingly improve the capacity of a road network, eliminates human driver errors, and provide better mobility for groups of people who are currently facing travel-restriction conditions. However, the long-term effects of AVs especially on energy consumption, emission, pedestrian interaction, safety and security has uncertainty due to the complexity of predicting the future mobility pattern.

Impact of cooperative adaptive cruise control on multilane freeway merge capacity

ABSTRACTCooperative Adaptive Cruise Control (CACC) allows vehicles to exchange real-time operational information wirelessly, enabling vehicles to travel in strings with shorter than normal time gaps between adjacent vehicles and ultimately increasing the freeway capacity. This study is intended to investigate the impact of CACC vehicle string operation on the capacity of multilane freeway merge bottlenecks, commonly found at on-ramp merging areas on urban freeways. Simulation experiments were conducted using CACC car-following models derived from field test data, together with lane-changing models of CACC vehicles and manually driven vehicles, as well as a maximum CACC string length and inter-string time gap constraint. Simulation results reveal that the freeway capacity increases quadratically as the CACC market penetration increases, with a maximum value of 3080 veh/hr/lane at 100% market penetration. The disturbance from the on-ramp traffic causes the merge bottleneck and can reduce the freeway capacity by up to 13% but the bottleneck capacity still increases in a quadratic pattern as CACC market penetration becomes larger. The findings suggest that there is a need to implement advanced merging assistance systems with CACC at merge bottlenecks for achieving the capacity improvement comparable with the observations at homogeneous freeway segments.

Cost-Benefit Analysis of V2G Implementation in Distribution Networks Considering PEVs Battery Degradation

Charging of plug-in electric vehicles (PEVs), especially as market penetration increases, is an important challenge for today's power systems. This paper presents a stochastic methodology for smart charging of PEVs. All of the associated uncertainties are taken into account in the proposed methodology. Moreover, a comprehensive model for impact of charging/discharging strategies on the battery pack degradation in the vehicle has been included. The proposed approach is applied to a typical distribution network that contains wind-based distributed generation (DG) units. A cost-benefit analysis is carried out to extract the benefits of Vehicle to Grid (V2G) implementation in distribution network. The simulation results show that the V2G implementation without considering battery degradation is economical. However, when considering a battery degradation cost, V2G implementation without wind generation is not beneficial, while it is economical when considering wind DG. Moreover, the smart charging is economical in all conditions and also it reduces the battery degradation cost in comparison with uncoordinated charging.

Probe vehicle lane identification for queue length estimation at intersections

ABSTRACTVehicles instrumented with Global Positioning Systems, also known as GPS probe vehicles, have become increasingly popular for collecting traffic flow data. Previous studies have explored the probe vehicle data for estimating speeds and travel time; however, there is very limited research on predicting queue dynamics from such data. In this research, a methodology was developed for identifying the lane position of the GPS-instrumented vehicles when they are standing in the queue at signalized intersections with multiple lanes, particularly in the case of unequal queue. Various supervised and unsupervised clustering methods were tested on data generated from a microsimulation model. Among the tested methods, the Optimal Bayes Rule that utilizes probability density functions estimated using bivariate statistical mixture models was found to be effective in identifying the lanes. The methodology for lane identification was tested for queue length estimation. This research confirms that the lane identification is an important step required prior to the queue length estimation. The accuracies of the models for lane identification and queue length estimation were evaluated at varying levels of demand and probe vehicle market penetrations. In general, as the market penetration increases, the accuracy improves as expected. The result shows that 40% market penetration rate is adequate to reach about 90% accuracy.

A network level connectivity robustness measure for connected vehicle environments

This study introduces a new CONnectivity ROBustness model (CONROB) to assess vehicle-to-vehicle communication in connected vehicle (CV) environments. CONROB is based on Newton’s universal law of gravitation and accounts for multiple factors affecting the connectivity in CV environments such as market penetration, wireless transmission range, spatial distribution of vehicles relative to each other, the spatial propagation of the wireless signal, and traffic density. The proposed methodology for the connectivity robustness calculation in CONROB accounts for the Link Expiration Time (LET) and the Route Expiration Time (RET) that are reflected in the stability of links between each two adjacent vehicles and the expiration time of communication routes between vehicles. Using a 117sq-km (45-square mile) network in Washington County, located west of Portland city, Oregon, a microscopic simulation model (VISSIM) was built to verify CONROB model. A total of 45 scenarios were simulated for different traffic densities generated from five different traffic demand levels, three levels of market penetration (5%, 15%, and 25%), and three transmission range values [76 (250), 152 (500), and 305 (1000)m (ft)]. The simulation results show that the overall robustness increases as the market penetration increases, given the same transmission range, and relative traffic density. Similarly, the overall connectivity robustness increases as the relative traffic density increases for the same market penetration. More so, the connectivity robustness becomes more sensitive to the relative traffic density at higher values of transmission range and market penetration. Multiple regression analysis was conducted to show the significant effect of relative traffic density, transmission range, and market penetration on the robustness measure. The results of the study provide an evidence of the ability of the model to capture the effect of the different factors on the connectivity between vehicles, which provides a viable tool for assessing CV environments.

Dependence of the Growth Parameters on the In-Plane Distribution of 150 mm φ Size SiC Epitaxial Wafer

Silicon carbide (SiC) power devices are expected to be useful in low-loss power conversion equipment in wide-ranging industrial areas. 4H-SiC Schottky barrier diodes (SBDs) and metal oxide semiconductor field-effect transistors (MOSFETs) have been launched on the commercial market, and many results obtaining outstanding performance of 4H-SiC switching devices have been reported [1-. Presently, SiC chip cost is a big problem preventing an increase in market penetration of such commercial devices. Therefore, 150 mm size wafers are urgently required reducing the cost per unit area for SiC epitaxial layers. Recently, some SiC wafer vendors announced that 150 mm SiC substrate would be released [4, and some groups have reported results of epitaxial growth on 150 mm SiC substrate [6-9].

Examining Social and Organizational Factors of Physician Integration

Introduction Few can argue with the assumptions underlying the centrality of physician integration. Strange Bedfellows or a Marriage Made in Heaven? is a well-conceived research effort where multiple data sources provide a wide range of exploratory analyses. In this study, physician integration refers specifically to the economic alignment of physicians with organizations. The work focuses on marketbased arrangements for hospital and system organizational linkages with physicians. The organizational environments and markets are analyzed as structural variables related to alignment. The process variables consist of building trust, placing physicians in management and governance, and developing physician leadership. This is a good research contribution with results and implications of fundamental importance to improving organizational effectiveness in managed care settings. In thinking about the authors' research, I am reminded of Rashi Fein, the distinguished Harvard health economist, who said that policy makers and health analysts do not seem to understand that we all live in a society rather than merely an economy (Fein 1974). Dr. Zuckerman et al.'s research involving economic markets and social processes relates to the fact that economic strategies and policies are best understood if considered in the context of social conditions and policies. Dr. Zuckerman et al. point out that social processes at the organizational level can facilitate or, in other cases, impede the successful integration of physicians. I will group my comments stemming from this research into three areas: 1. the importance of process specification in the managed care marketplace; 2. social forces and interests that influence learning in organizations attempting physician integration; and 3. the importance of understanding an organization's culture early in the planning for such strategic initiatives. Managed Care Process Model It is instructive to look at the range of processes that might force the integration of physician and administrator teams. Figure 1 is a framework that describes many of the processes that need to be managed as managed care market penetration increases. As the processes move clockwise, managed care market penetration increases, and physicians are required to work more closely with administrators to determine and manage the relationship between low cost and high quality. The set of processes on the right side of the model focuses on systems intended to support the analysis of cost reduction efforts by administrators. These processes seem to be the drivers of organizational performance in the condition of low market penetration of managed care. This roughly corresponds to Stages 1 and 2 of the market stages identified by the authors. In this situation, the support systems are limited to cost identification and management. There seems to be relatively little necessity for forced or required interaction in order to reduce costs or for sophisticated information systems permitting the analysis of both costs and quality. As managed care penetration increases, however, the mutual dependence of administrators and physicians increases, and they are required to interact when documenting cost/quality relationships that demonstrate value. These forced interactions require teamwork based on the performance of the distinct competencies of physicians and administrators. At the same time, the support systems required to inform decision making in these interactions is very sophisticated and complicated. These new systems require information that is comprehensive enough to analyze both cost and quality dimensions. These expensive healthcare information systems are few in number, and for the most part do not permit comparisons with external organizations for purposes of benchmarking. This framework suggests that physicians and administrators are forced to work together in new and complicated ways, and the boundaries of both professional knowledge and the domains of organizational influence can become blurred. …

Assessing the Effect of Increased Managed Care on Hospitals

This study uses a new relative risk methodology developed by the author to assess and compare certain performance indicators to determine a hospital's relative degree of financial vulnerability, based on its location, to the effects of increased managed care market penetration. The study also compares nine financial measures to determine whether hospital in states with a high degree of managed-care market penetration experience lower levels of profitability, liquidity, debt service, and overall viability than hospitals in low managed care states. A Managed Care Relative Financial Risk Assessment methodology composed of nine measures of hospital financial and utilization performance is used to develop a high managed care state Composite Index and to determine the Relative Financial Risk and the Overall Risk Ratio for hospitals in a particular state. Additionally, financial performance of hospitals in the five highest managed care states is compared to hospitals in the five lowest states. While data from Colorado and Massachusetts indicates that hospital profitability diminishes as the level of managed care market penetration increases, the overall study results indicate that hospitals in high managed care states demonstrate a better cash position and higher profitability than hospitals in low managed care states. Hospitals in high managed care states are, however, more heavily indebted in relation to equity and have a weaker debt service coverage capacity. Moreover, the overall financial health and viability of hospitals in high managed care states is superior to that of hospitals in low managed care states.

Hydrogen as a future transportation fuel

A smooth transition from a petroleum-driven transportation system to clean-burning automobiles with the performance and range of today's gasoline cars is plausible using high-efficiency hydrogen-fueled hybrid-electric vehicles. The introduction of hydrogen (H 2) vehicles will reduce U.S. dependence on oil imports, virtually eliminate automotive urban air pollution, accelerate the development of cost-effective renewable energy, and help stabilize greenhouse-gas emissions. Based on an economic and technical analysis, H 2 vehicles, when first introduced, can be cost-competitive with battery-powered electric vehicles. As market penetration increases, H 2-vehicle fueling costs would become competitive with the fueling costs of today's gasoline vehicles (5 ¢/mi). Hydrogen production at filling stations, vehicle fleets, and homes would circumvent many start-up issues and would use existing natural gas and/or electricity energy infrastructures to begin the transition towards a clean, flexible, sustainable, and secure transportation fuel.

The value of photovoltaic electricity for society

This paper addresses several issues to be considered when assessing the value of PV electricity. The value of PV has been examined from various perspectives—consumer, utility, and environmental—and for central and decentralized PV systems. Aggregating these benefits leads to the value of PV for society. In this context, economic and ecological aspects have been considered. furthermore, feedback from consumers drawing electricity directly from the sun have been taken into account. This is expressed mainly as energy conservation and load-shift effects triggered by changes in consumer behaviour due to decentralized PV systems. Finally, it is proposed that, collectively, the benefits of PV systems will ensure its continued promotion and development as an energy resource, resulting in a presumably slow but steady increase in market penetration.

The economics of motorist information systems revisited

The literature on the implications of the implementation of motorist information systems is ambiguous, particularly with respect to the economic consequences. This paper aims to shed more light on the potential economic costs and benefits of these new technologies by reviewing and combining results obtained in the literature. Furthermore, future research directions, essential for an increased understanding of these systems, are pointed out. It is first argued that a motorist information system is not a normal economic good; the benefits accruing to the equipped road users are dependent on the level of market penetration. At most levels of market penetration a motorist information system is most likely to generate a positive external effect for non‐equipped motorists and a negative external effect for those already equipped. However, in terms of travel time, the equipped road users will outperform the non‐equipped ones. Next, the economic consequences of these peculiarities are identified, particularly with respect to the role of the government. Attention is also paid to the traffic generating properties and the market potential of motorist information systems. It is concluded that the implementation of a motorist information system will, owing to an efficiency improvement on the road network, generate more traffic. The size of this newly generated traffic is uncertain and dependent on the kind of information system, the behavioural responses of the road users, the particular network under consideration and the level of market penetration. This paper finally concludes that, particularly in networks with recurrent congestion, the benefits to equipped drivers diminish as the level of market penetration increases. These systems have a better economic perspective in volatile road networks, i.e. networks with non‐recurrent congestion.