Pricing Plans Research Articles

Tying Medicare Part B Drug Prices to International Reference Pricing Will Devastate R&D.

According to Secretary Azar of Health and Human Services, implementing international reference pricing (IPI) in Medicare Part B will have minimal impacts. He has stated, "These savings, while substantial for American patients and taxpayers, cannot possibly pull out more than 1 percent of R&D." As companies traditionally spend 20% of free cash flow on R&D, we have measured the IPI impact according to industry standard metrics. The potential negative impacts of the international reference pricing plan, as it is currently structured, are numerous. Companies are likely to avoid developing Medicare Bart B physician-administered drugs in the future if it comes to fruition. Further, if distributing in any of the included countries in the benchmarking exercise that traditionally have prices far below that of the United States has the impact of creating lower US prices where the industry currently derives more than 80% of their global profit, companies will simply not seek market access in those benchmarked countries and patients in those countries will not receive the medicines they need. The idea that companies will be able to unilaterally raise prices in Europe defies logic and practice. Many countries in the EU have been threatening IP rights under the TRIPS clauses of the WTO for several years because of their belief that pharmaceutical pricing is unacceptably high right now, without the IPI. Harnessing real-world evidence would allow for increased competition by faster time to market. One wonders why an approach encompassing the improved time to market was not considered, as the reference pricing proposal as it stands now, ultimately, will reduce R&D budgets, impair the overall investment climate, and deprive patients the new medicines.

Practical Cost Analysis of Genomic Data in the Cloud

Abstract Background The growth of clinical NGS testing, together with new CAP checklist items stating that read-level (raw) NGS data must be retained for a minimum of 2 years, has prompted laboratories to reevaluate their NGS data storage solutions. In this context, cloud-based storage has several advantages, such as low per-GB prices, scalability, and minimal fixed costs; nonetheless, several perceived disadvantages, including increased complexity and security/regulatory considerations, have hindered adoption. Furthermore, despite the ostensibly simple usage-based pricing plans, practical cost analysis of cloud storage for NGS data storage is not straightforward. Methods We developed an easy-to-use tool designed specifically for cost and usage estimation for laboratories performing clinical NGS testing (https://ngscosts.info). Our tool enables quick exploration of dozens of storage options across three major cloud providers and provides complex cost and usage forecasts over 10- to 20-year timeframes. Parameters include current test volumes (and customizable file sizes), expected test volume growth rate, data compression techniques, data retention times across storage tiers, and case reaccess rates. Outputs include an easy-to-visualize chart of total data stored, yearly and lifetime costs, and a “cost per test” estimate. Results The per-GB storage prices among major cloud providers have dropped ~6 to 7× over the past 10 years; in addition, there currently exists an up to 20× price difference between storage tiers. Lower prices are typically offset by either increased access/transfer costs or slower retrieval rates. Two factors were found to markedly affect the average cost per test: (1) total file size, including the use of NGS-specific file compression techniques, and (2) storage tiers and rapid transfer to “cold” or archival storage. In contrast, some factors were not found to affect total costs, including costs associated with reaccess from archival storage tiers, and discounts associated with increased volumes. When data from NGS testing were stored in “hot” storage for 1 year, then archived for 9 years, typical costs per exome were ~$3/test and genomes ranged from $40/test to $66/test, depending on compression algorithms used. Overall, reaccess of data from cold storage added 2% or less to the total cost per test (with an estimated reaccess rate of 10% of cases per year). Conclusions Steady declines in cloud storage pricing, as well as new options for storage and retrieval, make storing clinical NGS data on the cloud economical and friendly to laboratory workflows. Taken together, laboratories should not be concerned about reaccess costs but rather should focus on data compression techniques and archival storage tiers for long-term data. Our web-based tool makes it possible to explore and compare cloud storage solutions and provide forecasts specifically for clinical NGS laboratories.

The Application Programmer Interface (API) in Modern Cartography: Development and Prospects

Abstract. Application Programmer Interfaces have been a part of the cartographic landscape since 2006 with the introduction of the Google Maps API. Essentially a library of code that provides access to a variety of mapping functions, APIs have since been the basis of online, tile-based, Multiscale Pannable (MSP) mapping. While the Google Maps API is still the most widely used with more than 4.6 billion websites embedding a Google Map, a variety of other mapping APIs have been introduced primarily to circumvent Google’s pricing structure.The cost for using Google Maps on websites has changed over the years. From 2005 to 2011, the use of Google Maps was free no matter how many maps were referenced by a website. From then to 2016, Google limited map downloads to 25,000 map loads a day for 90 consecutive days. In 2016, the 90 consecutive days was removed so the number of maps downloaded could not exceed 25,000 on any given day. If it did, the website developer needed to register their site and pay a fee for maps produced over this limit.In April 2018, Google announced the launch of a new name for the Google Maps API – Google Maps Platform – and a new pricing plan. A free tier continues to be offered through a US $200 monthly credit but now requires creating an account and entering a credit card number. In this new online platform, it is still possible to use the Google Maps API without incurring any cost by limiting the number of daily downloads so that the monthly quota of 28,000 map downloads is not exceeded.The problem is not the use of the Google Maps API but the Google map tiles. The look of maps from Google and the associated interface have become so popular that users avoid using other kinds of maps – even those from Apple. Users complain that the tiles from other vendors implement a different color scheme or highlight different features. They also complain that these maps appear more slowly. While other APIs can use Google Map tiles, including the popular Leaflet API, their use is still subject to the same pricing structure.With the help of the Internet, maps from Google have become the standard maps. All other renditions of the world are seen to be inferior and not worthy of examination. They are simply interesting oddities. While some can adapt to alternative representations, most choose not to. This Google Map phenomena is examined along with lessons from the historical progression of online mapping.

The cost-effectiveness of competing congestion pricing plans in New York city

ObjectivesAs nations urbanize, novel ways are needed to manage increasing automobile traffic in city centers. One innovative and efficient way of reducing traffic congestion is to charge automobiles when they enter congested areas of a city, a strategy referred to as congestion pricing. A number of cities worldwide have adopted congestion pricing with great success. To varying degrees, congestion pricing has lowered congestion, increased revenue for public transportation systems, reduced pollution, and increased residents’ levels of physical activity. New York City is also adopting congestion pricing, and considered a large number of models for doing so. MethodsWe compare the costs and health benefits of two competing congestion pricing plans, Fix NYC (a higher-cost but geographically-focused proposal) and Move NY (a lower-cost city-wide proposal focused on equality), that were considered by the municipal and state governments. We use a Markov model to estimate the effects of these two congestion pricing plans in 2019 over a period of 10 years. ResultsWe find Fix NYC's and Move NY plans produce similar cost savings and gains in life relative to the status quo. Fix NYC would save about $24,805 and Move NY would save about $24,777 per capita. Each would result in a gain of 0.10 QALYs per capita, with the only differences notable at 4 decimal points of rounding error. ConclusionsImplementing congestion pricing in New York City would save both money and lives. Both of the plans we evaluate are roughly equal in value, and this is likely true of the plans that fall in-between these two plans in design. These findings suggest that congestion pricing is highly cost-effective, but that the program components do not matter much from a health standpoint.

Time-Dependent Pricing for Multimedia Data Traffic: Analysis, Systems, and Trials

The explosive growth of multimedia data traffic in wired and wireless networks have led Internet service providers (ISPs) to use penalty mechanisms like throttling, capping, overage fees to manage network congestion; however, such measures are harmful to the Internet ecosystem. Therefore, we use ideas from economics to create incentive-based, as opposed to penalty-based, solutions for data plans. In particular, we explore time-dependent pricing (TDP) - a form of dynamic pricing that manages congestion by offering time-varying discounts to incentivize users to shift some data traffic temporally. To realize TDP data plans in practice, we provide (i) an optimization model to compute time-dependent prices, (ii) a system implementation for deployment in operational networks, and (iii) experiments with two cellular networks for demonstrating feasibility. Our results show that the users respond to such pricing plans by using higher volume of traffic in lower-priced (off-peak) periods and benefit from a lower $/GB fee, while the ISPs benefit from a higher revenue due to increase in off-peak usage and lower peak-to-average traffic ratio in their network. This suggests that such a pricing solution can incentivize users to modify their usage behavior and enable better revenue management in multimedia-rich networks.

Filling Two Needs With One Deed: Combo Pricing Plans for Computing-Intensive Multimedia Applications

In this paper, we examine new plan pricing schemes for multimedia applications that offload computing-intensive tasks to computing servers incurring both communication and computing costs. Pricing schemes offered to users include: 1) a pay-as-you-go payment for usage of communication or computing resources, 2) an upfront data (resp. computing) plan for unlimited usage of communication (resp. computing) resources during a period, and 3) an upfront combo plan for unlimited usage of both communication and computing resources during a period. We aim to solve an online plan reservation problem: the amount of resources needed by a task is only known when it arrives, i.e., the future is unknown. However, even if the resource usage of future tasks is known in advance, the plan reservation problem is NP-hard and thus challenging. To tackle this problem, we propose a randomized online reservation (ROR) scheme to reserve plans probabilistically, where the probability is determined by the recent usage of resources. The performance gap (competitive ratio) between our proposed scheme and the optimal solution is analyzed and derived in closed-form, and this gap is proved to be the minimum among all online algorithms which do not know the usage of future tasks. Trace-driven simulations verify the cost advantage of ROR and characterize how different prices of plans influence users' plan reservation strategies.

Securing Aggregate Queries for DNA Databases

This paper addresses the problem of sharing person-specific genomic sequences without violating the privacy of their data subjects to support large-scale biomedical research projects. The proposed method builds on the framework proposed by Kantarcioglu et al. [1] but extends the results in a number of ways. One improvement is that our scheme is deterministic, with zero probability of a wrong answer (as opposed to a low probability). We also provide a new operating point in the space-time tradeoff, by offering a scheme that is twice as fast as theirs but uses twice the storage space. This point is motivated by the fact that storage is cheaper than computation in current cloud computing pricing plans. Moreover, our encoding of the data makes it possible for us to handle a richer set of queries than exact matching between the query and each sequence of the database, including: (i) counting the number of matches between the query symbols and a sequence; (ii) logical OR matches where a query symbol is allowed to match a subset of the alphabet thereby making it possible to handle (as a special case) a “not equal to” requirement for a query symbol (e.g., “not a G”); (iii) support for the extended alphabet of nucleotide base codes that encompasses ambiguities in DNA sequences (this happens on the DNA sequence side instead of the query side); (iv) queries that specify the number of occurrences of each kind of symbol in the specified sequence positions (e.g., two ‘A’ and four ‘C’ and one ‘G’ and three ‘T’, occurring in any order in the query-specified sequence positions); (v) a threshold query whose answer is ‘yes’ if the number of matches exceeds a query-specified threshold (e.g., “7 or more matches out of the 15 query-specified positions”). (vi) For all query types, we can hide the answers from the decrypting server, so that only the client learns the answer. (vii) In all cases, the client deterministically learns only the query's answer, except for query type (v) where we quantify the (very small) statistical leakage to the client of the actual count.

Renewable energy's vanishing premium in Texas's retail electricity pricing plans

Traditionally, there has been a premium in green pricing plans for electricity supply with high renewable energy content. Using a sample of 710 plans offered in December 2018 by Texas's competitive retailers to residents in Dallas, Houston, Corpus Christi and Abilene, we document that there is no longer a statistically significant renewable energy premium in Texas's retail electricity pricing. Attributable to the declining cost of renewable energy, this finding's policy implication is that continuing development of renewable energy is unlikely to adversely impact Texas's residents.

Optimal pricing strategies for delivery alliance in online retailing platform

PurposeNowadays, some online retailing platforms emerge to integrate transport capacity to provide standard distribution service for sellers. Such an integrated form of service is defined as delivery alliance (DA). To have a better understanding of how to price the service, this study aims to fixate on the seller’s problems and builds a series of profit maximization models in accordance with the two-sided market pricing theory within a platform business model.Design/methodology/approachIn the present study, some optimization models are built in the two-sided market type and the optimal solutions are found in a three-dimensional decision space. By using the basic model as the benchmark, some optimization problems of DA in realistic situations are discussed. Particularly, a power-law-distribution model is established to deal with the uncertainty in forecasting. Also, a price-sensitive model and a loss-aversion model are presented to describe the various reactions of sellers to charging modes. Finally, some combined situations are discussed and the strategies are compared under the mentioned models.FindingsBy selecting the basic model as the benchmark, the specific pricing strategies are found for each context to yield the optimal profits. The flexibility of pricing strategy in the basic model and rigid pricing strategies in extended models, are discussed. As a result, the guidelines for the online retailing platforms are developed on designing and pricing the DA service.Research limitations/implicationsFirst, it would be interesting to expand the pricing plan of the platform. For instance, menu pricing and quantity discount have not been considered, which are common in practice. The time discounting has also been ignored. If the time value were calculated, the contract fees would be more critical due to the earliest of collecting money. Finally, those joiners who have huge order sizes are crucial for the ecosystem indeed, but arouse no attention. While in reality, they may have more power to bargain with the platform. Thus, how the platform competition affects the pricing strategies needs future research.Originality/valueThe optimal pricing strategies under these models are analytically found out, and it is shown that the presented models result in the same scale of joiners and profits in optimization. This suggests that DA works well in various behavioral contexts. This also suggests that DA is a significant controller in service quality improvement. Then, the optimal pricing strategies are compared among all the models. During this, it is discovered that the realistic contexts might reduce the profit, whereas an appropriate pricing strategy can pull this back without loss of service quality.

Bi-objective optimization of retailer’s profit and customer surplus in assortment and pricing planning

We develop a bi-objective optimization for a retailer product line, composed of substitutable variants, that seeks to maximize the retailer’s profit and consumer surplus concurrently, where the surplus is measured as a utility scaled in dollars. With fast-moving consumer goods in mind, we utilize a deterministic, price-dependent, utility model to develop the demand function. The problem is then formulated as a bi-objective mixed-integer nonlinear program. The e-constraint method is used to fully develop the set of Pareto optimal solutions for this problem. Specifically, we derive analytical bounds on the values of e that lead to Pareto optimal solutions. We then propose a scheme with simple steps that allows developing as many efficient solutions, as needed. To overcome the computational burden, we adopt a linearization scheme, which recast the problem into an integer-linear program that proves to be solvable within couple CPU seconds for large, industry-size, instances with tens of products and numerous consumer segments. Numerical examples on small- and large-size instances indicate that providing the customer with a high surplus level typically requires low prices and, surprisingly, small assortments (to reduce fixed costs). Another counter-intuitive observation that we make is that the price of a product sometimes raises when the required customer surplus level in the market is increased. This is due to cannibalization among products and change in the optimal assortment structure.

Optimal day-ahead scheduling for commercial building-level consumers under TOU and demand pricing plan

In this paper, we solve the day-ahead scheduling programming problem for commercial building-level consumers with combined time-of-use ($/kWh) and demand ($/kW) pricing plans. Aiming at minimizing the monthly charge, the problem formulation is proposed as a generic algorithm that produces day-ahead building operation schedules. It considers the influences of daily peak load during on-peak hours and daily energy consumption on the monthly charge. The aggregation model of building-level space conditionings is built for scheduling demo. To obtain the near global optimum, a multi sub-swarms particle swarm optimization (MSPSO) is proposed by introducing the ideas of mutation operation, work hierarchy and iterative regrouping into particle swarm optimization (PSO). It improves the population diversity for enhancing the global searching ability and the ability of escaping from local optimum. The PSO comparison shows that MSPSO has better convergence performance with higher stability compared with some classical PSOs. Furthermore, a commercial office-style building with space conditionings is simulated. Using TOU and demand pricing plan from Duke Energy, numerical results demonstrate that the proposed day-ahead scheduling algorithm and the improved MSPSO can reduce the monthly charge by 30% and 17%, respectively.

Drug Pricing Plan Could Boost Step Therapy, Formulary Restrictions

Back to table of contents Previous article Next article Professional NewsFull AccessDrug Pricing Plan Could Boost Step Therapy, Formulary RestrictionsLinda M. RichmondLinda M. RichmondSearch for more papers by this authorPublished Online:25 Apr 2019https://doi.org/10.1176/appi.pn.2019.5a29AbstractAPA submitted formal comments to the Trump administration asking for patient protections under its proposal to lower prescription drug prices in Medicare Part D plans and Medicaid managed care organizations.APA is urging the Trump administration to include patient protections in its proposal for lowering prescription drug prices in Medicare Part D plans and Medicaid managed care organizations to avoid increased use of utilization management tools, such as step therapy, prior authorization, and formulary restrictions.APA filed formal comments on the proposal last month with the Health and Human Services’ Office of Inspector General on behalf of its 38,500 members. The administration proposed the controversial plan aimed at lowering prescription drug prices in February (Psychiatric News, March 14, 2019).If finalized, the rule would upend the current way drugs are priced by barring the practice of drugmakers’ giving rebates to certain health plans and their pharmacy benefit managers (PBMs) under contract with them.“We support the administration’s efforts to enhance price transparency and lower out-of-pocket costs of medications,” wrote APA CEO and Medical Director Saul Levin, M.D., M.P.A., in formal comments submitted April 8. “However, we are concerned about unintended consequences that may result from the proposed rule, such as additional use of utilization management protocols.”The concern is that if the proposal reduces profits for the plans or PBMs by doing away with the rebates, the plans may react by adopting additional utilization management practices, to the detriment of patients and physicians.“As you move forward with this effort, we urge you to include protections to prohibit the use of additional utilization management tools (i.e., step therapy, prior authorization, and formulary restrictions). These tools increase provider burden while reducing patient access to critical, life-saving mental health and substance use disorder medications,” Levin wrote.The proposed rule would impact the 44 million mostly senior citizens enrolled in Medicare Part D drug plans and the 65 million lower-income individuals enrolled in Medicaid managed care plans. The changes, however, could eventually trickle down to the rest of the health care marketplace.Health care policy analysts have pointed out that the proposal would raise monthly premiums across the board by as much as 22% while providing uncertain benefits for many enrollees. Although the rule would allow drugmakers to start giving rebates to plan enrollees at the point of sale to lower their costs, there is nothing in the rule to compel drugmakers to do so. Since many medications would not be eligible for the rebates, including generic drugs or drugs for which there is no substitute, patients taking these drugs would not see lower costs.Moreover, patients taking medications in the Part D “protected classes,” which include antipsychotics, antidepressants, and anticonvulsants, would not likely see reduced drug prices.“A beneficiary using high-cost drugs in protected classes is less likely to benefit from a reduced pharmacy purchase price, because manufacturers generally offer low or no rebates to plans for these drugs, since drugs in protected classes must be included on Part D plan formularies,” according to the proposed rule. ■APA’s letter can be read here. The proposed rule “Fraud and Abuse; Removal of Safe Harbor Protection for Rebates Involving Prescription Pharmaceuticals and Creation of New Safe Harbor Protection for Certain Point-of-Sale Reductions in Price on Prescription Pharmaceuticals and Certain Pharmacy Benefit Manager Service Fees” can be accessed here. ISSUES NewArchived

Privacy-Preserving Electricity Billing System Using Functional Encryption

The development of smart meters that can frequently measure and report power consumption has enabledelectricity providers to offer various time-varying rates, including time-of-use and real-time pricing plans. High-resolution power consumption data, however, raise serious privacy concerns because sensitive information regarding an individual’s lifestyle can be revealed by analyzing these data. Although extensive research has been conducted to address these privacy concerns, previous approaches have reduced the quality of measured data. In this paper, we propose a new privacy-preserving electricity billing method that does not sacrifice data quality for privacy. The proposed method is based on the novel use of functional encryption. Experimental results on a prototype system using a real-world smart meter device and data prove the feasibility of the proposed method.

CVM을 이용한 지역축제 입장료 경제적 가치추정 연구 : 성산일출제를 중심으로

The purpose of this study is to estimate economic value of Seongsan Sunrise Festival and identify factors which influence willingness to pay of festival-goers. To this end, this study adopted double-bounded dichotomous choice CVM for analysis. For this study, a survey was conducted for local and domestic tourists visited the Seongsan Ilchul Festival and utilize SPSS 21.0 and STATA 14.0 programs for analysis. The study result showed that age and income bracket play significant influence on willingness to pay(WTP) of tourists while visit experience, gender, and level of education are not that significantly influential. By utilizing coefficient estimated from logic model, individual WTF for ticket price of Seongsan Sunrise Festival can be priced at 2,070 KRW. For the pricing plan, local tourists are expected to pay up to 2,008 KRW while domestic tourists priced at 2,160 KRW. This study is expected to provide a fundamental foundation for settling adequate ticket pricing policy and practical strategy for fee-based policy of local festivals.

Train delay analysis and prediction based on big data fusion

Abstract Despite the fact that punctuality is an advantage of rail travel compared with other long-distance transport, train delays often occur. For this study, a three-month dataset of weather, train delay and train schedule records was collected and analysed in order to understand the patterns of train delays and to predict train delay time. We found that in severe weather train delays are determined mainly by the type of bad weather, while in ordinary weather the delays are determined mainly by the historical delay time and delay frequency of trains. Identifying the factors closely correlated with train delays, we developed a machine-learning model to predict the delay time of each train at each station. The prediction model is useful not only for passengers wishing to plan their journeys more reliably, but also for railway operators developing more efficient train schedules and more reasonable pricing plans.

Compromised supply chain planning under dominant systems with inventory policies and cost uncertainty

In practice, companies with dominant power influence partner operations for their benefits. This paper proposes the supply chain (SC) planning models that determine the optimal sale, pricing, production, and inventory plans of each SC member based on the dominant policy. The theoretical contributions of this paper include a development of linear mathematical functions and explicitly considering the inventory turnover as one of the business performance measures in addition to profit and revenue. The fuzzy multi-objective linear programming models (FMOLP) under dominant systems are developed. A case study in plastic fashion accessory industry in Thailand is performed. The performance measures are translated into satisfaction levels and plotted in a radar chart with an area of possibility, which clearly shows the sensitivity degree of uncertainty. Practical contribution is that this paper recommends suitable pricing and inventory plans based on the dominant policy. [Submitted: 15 August 2018; Accepted: 18 March 2019]

Factors affecting Customer Retention in the Restaurant Industry: Moderating Role of Restaurant Location

This study investigated the main effects of service quality and price fairness on customer retention. The study also investigated the moderating role of restaurant location in service quality, price fairness-customer retention relationships. Data collected from 182 customers visiting selected restaurants located in Multan city of Pakistan. The collected data relates to the chosen variables i.e. service quality, price fairness, restaurant location, and customer retention. The significant positive results of linear regression analysis revealed that service quality, price fairness and restaurant location predicted the customer retention. The results of step-wise multiple regression analysis revealed that restaurant location positively moderated the relationship of service quality and customer retention. It also revealed that restaurant location positively moderated the relationship of price fairness and customer retention. This study may have practical implications for human resource managers regarding recruitment decisions. It may have importance for restaurant managers or owners regarding quality of service and pricing plans which are true reflection of the services provided. Similarly, they may give due consideration to the location factor while launching a food service business. On the basis of the findings of the study, conclusions have been made and recommendations and directions for further research have been offered.

Pricing On-Demand Services: How Best to Combine Access and Usage Fees?

Common pricing plans for pricing digital goods and services that are consumed in multiple units repeatedly over time include a simple per-unit plan (also called pay as you go or usage-based pricing), a per-period plan (all you can eat buffet or membership pricing), and a combination of the two via either a two-part tariff (2PT) that imposes both fees on all buyers or a self-selection menu that allows buyers to pick one type of fee. Although the latter two plans combine the two fees in very disparate ways, we find that both plans generate roughly the same profit, with each having only a small advantage under different conditions. Crucially, though, the menu has a significant advantage on market coverage. We show that in heterogeneous markets the profit advantage of two-instrument plans over an appropriate single instrument plan (the best of per-unit and per-period) is most significant when satiation-rates (rate of change in marginal valuation) are similar across consumers. Otherwise, an appropriate single instrument plan does a reasonable job of balancing profitability and tariff complexity. When the firm wants to implement a `simple' single-instrument plan but is uncertain about satiation-rates then employing a per-unit plan is less risky because on average it is more profitable compared to a per-period plan. The per-unit plan also generates higher market coverage compared to the per-period plan across the spectrum of scenarios.

Social Information Filtering-Based Electricity Retail Plan Recommender System for Smart Grid End Users

Rapid growth of data in smart grids provides great potentials for the utility to discover knowledge of demand side and design proper demand side management schemes to optimize the grid operation. The overloaded data also impose challenges on the data analytics and decision making. This paper introduces the service computing technique into the smart grid, and proposes a personalized electricity retail plan recommender system for residential users. The proposed personalized recommender system (PRS) is based on the collaborative filtering technique. The energy consumption data of users are firstly collected from the smart meter, and then key energy consumption features of the users are extracted and stored into a user knowledge database (UKD), together with the information of their chosen electricity retail plans. For a target user, the recommender system analyzes his/her energy consumption pattern, find users having similar energy consumption patterns with him/her from the UKD, and then recommend most suitable pricing plan to the target user. Experiments are conducted based on actual smart meter data and retail plan data to verify the effectiveness of the proposed PRS.

Analysis of a joint entry- and distance-based cordon pricing scheme: a dynamic modeling approach

Transportation demand management (TDM) covers strategies for reducing traffic congestion within the affected urban areas. Congestion pricing includes a branch of TDM strategies; among them, the entry-based cordon pricing, i.e., applying charge on entry, is the most popular because of practicality and social acceptance. Many researchers have investigated different second-best approaches for evaluations of cordon pricing plans, mostly by applying static traffic assignment methods. In this paper, a joint entry- and distance-based scheme is proposed to circumvent the deficiencies intrinsic to each. The optimal joint design is considered as the solution to an optimization problem, in which an equilibrium dynamic traffic assignment model is used to take account of flow variations and represent congestion effects more realistically. The problem is solved for the network of Sioux Falls by using an enumeration algorithm, and the solution is compared with those obtained for distinct entry- and distance-based schemes. Based on the results, the joint tolling has the best performance in reducing the total travel time of the travelers and in alleviating the congestion level inside the cordoned area, while generating a higher level of revenue from tolls. Furthermore, the numerical experiments show the unreliability of the results by static against dynamic modeling approach.