2,373 research outputs found

    Feasibility of Equity-driven Taxi Pricing Strategy based on Double Auction Mechanism in Bangkok Metropolitan Region, Thailand

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    Passenger rejection by taxi drivers impacts the travel behaviour in many cities and suburban areas, often leaving those potential customers in non-popular zones stranded without access to taxis. To overcome this problem, many practices have been implemented, such as penalties to drivers, bans, and new pricing strategies. This paper presents a double auction taxi fare scheme, which gives both passengers and taxi drivers to influence the price, coupled with a clustering method to discourage strategic service rejection in the case study of Bangkok Metropolitan Region, Thailand, which has detailed data availability and uneven taxi journey distributions. The double auction mechanism is tailored to 2019 taxi trips, service rejection complaints, and local travel behaviour to boost transportation equity. To benchmark the performance of the new double auction scheme, a bespoke agent-based model of the taxi service in Bangkok Metropolitan Region at different rejection rates of 0%-20% was created. On one hand, the current rejection behaviour was modelled, and on the other, the double auction pricing strategy was applied. The results indicate that the double auction strategy generates a spatially distributed accessibility and leads to a higher taxi assignment success rate by up to 30%. The double auction scheme increases pickups from locations that are 20-40 km from central Bangkok by 10-15%, despite being areas of low profit. Due to the changing taxi travel landscape and longer taxi journeys, the total air pollutant emissions from the taxis increase by 10% while decreasing local emissions within central areas of Bangkok by upto 40%. Using a 5 Baht average surcharge, the total revenue drops by 20%. The results show that an equity-driven pricing strategy as an implementation of transport policy would be beneficial.Comment: 21 pages, 10 figures, 1 table, as accepted at Transportation Research Board Conference 202

    Pricing the Cloud: An Auction Approach

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    Cloud computing has changed the processing and service modes of information communication technology and has affected the transformation, upgrading and innovation of the IT-related industry systems. The rapid development of cloud computing in business practice has spawned a whole new field of interdisciplinary, providing opportunities and challenges for business management research. One of the critical factors impacting cloud computing is how to price cloud services. An appropriate pricing strategy has important practical means to stakeholders, especially to providers and customers. This study addressed and discussed research findings on cloud computing pricing strategies, such as fixed pricing, bidding pricing, and dynamic pricing. Another key factor for cloud computing is Quality of Service (QoS), such as availability, reliability, latency, security, throughput, capacity, scalability, elasticity, etc. Cloud providers seek to improve QoS to attract more potential customers; while, customers intend to find QoS matching services that do not exceed their budget constraints. Based on the existing study, a hybrid QoS-based pricing mechanism, which consists of subscription and dynamic auction design, is proposed and illustrated to cloud services. The results indicate that our hybrid pricing mechanism has potential to better allocate available cloud resources, aiming at increasing revenues for providers and reducing expenses for customers in practice

    Hybrid Mechanisms for On-Demand Transport

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    Online advertising: analysis of privacy threats and protection approaches

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    Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft

    An Overview and Examination of the Indian Services Sector

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    India’s service sector has grown rapidly since the 1990s. Domestic demand for services has increased as incomes have risen, triggering the expansion of industries such as banking, education, and telecommunications. Exports have also increased rapidly, led by information technology and business process outsourcing (IT-BPO). India’s ability to offer low-cost, high-quality IT-BPO services has made it a world leader in this industry. However, employment in services has not grown as quickly as output. The majority of India’s jobseekers are low-skilled, but demand for workers is growing fastest in higher-skill industries. The supply of highly-skilled workers has not kept pace with demand, causing wages to increase faster for these workers than for lower-skilled ones. India’s government has supported the growth of service industries through a mix of deregulation, liberalization, and incentive programs, such as the Software Technology Parks of India. Nevertheless, burdensome regulations, poor infrastructure, and foreign investment restrictions continue to affect service firms’ ability to do business. USITC analysis suggests that additional liberalization would lead to an increase in India’s imports of services

    An investigation into dynamical bandwidth management and bandwidth redistribution using a pool of cooperating interfacing gateways and a packet sniffer in mobile cloud computing

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    Mobile communication devices are increasingly becoming an essential part of almost every aspect of our daily life. However, compared to conventional communication devices such as laptops, notebooks, and personal computers, mobile devices still lack in terms of resources such as processor, storage and network bandwidth. Mobile Cloud Computing is intended to augment the capabilities of mobile devices by moving selected workloads away from resource-limited mobile devices to resource-intensive servers hosted in the cloud. Services hosted in the cloud are accessed by mobile users on-demand via the Internet using standard thick or thin applications installed on their devices. Nowadays, users of mobile devices are no longer satisfied with best-effort service and demand QoS when accessing and using applications and services hosted in the cloud. The Internet was originally designed to provide best-effort delivery of data packets, with no guarantee on packet delivery. Quality of Service has been implemented successfully in provider and private networks since the Internet Engineering Task Force introduced the Integrated Services and Differentiated Services models. These models have their legacy but do not adequately address the Quality of Service needs in Mobile Cloud Computing where users are mobile, traffic differentiation is required per user, device or application, and packets are routed across several network domains which are independently administered. This study investigates QoS and bandwidth management in Mobile Cloud Computing and considers a scenario where a virtual test-bed made up of GNS3 network software emulator, Cisco IOS image, Wireshark packet sniffer, Solar-Putty, and Firefox web browser appliance is set up on a laptop virtualized with VMware Workstation 15 Pro. The virtual test-bed is in turn connected to the real world Internet via the host laptop's Ethernet Network Interface Card. Several virtual Firefox appliances are set up as endusers and generate traffic by launching web applications such as video streaming, file download and Internet browsing. The traffic generated by the end-users and bandwidth used is measured, monitored, and tracked using a Wireshark packet sniffer installed on all interfacing gateways that connect the end-users to the cloud. Each gateway aggregates the demand of connected hosts and delivers Quality of Service to connected users based on the Quality of Service policies and mechanisms embedded in the gateway. Analysis of the results shows that a packet sniffer deployed at a suitable point in the network can identify, measure and track traffic usage per user, device or application in real-time. The study has also demonstrated that when deployed in the gateway connecting users to the cloud, it provides network-wide monitoring and traffic statistics collected can be fed to other functional components of the gateway where a dynamical bandwidth management scheme can be applied to instantaneously allocate and redistribute bandwidth to target users as they roam around the network from one location to another. This approach is however limited and ensuring end-to-end Quality of Service requires mechanisms and policies to be extended across all network layers along the traffic path between the user and the cloud in order to guarantee a consistent treatment of traffic

    e-Reverse logistics for remanufacture-to-order : an online auction-based and multi- agent system supported solution

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    Due to the rapid obsolescent nature of consumer products, the remanufacture-to-stock strategy, in which remanufacturers tend to collect certain amount of end-of-life products, remanufacturing them as many as they can and keep these remanufactured products in stock waiting for customers come to buy, is not always an optimal solution. Under this circumstance, remanufacture-to-order policy, as an effective complement, provides a good trade-off for remanufacturers between meeting consumers’ demand and, in the meantime, keeping the inventory cost at a lower level. To remanufacture the used items, the manufacturer must retrieve them from the market where they are dispersed among consumers. This is accomplished by means of a reverse logistics chain that is comparable to the new product distribution system in reverse. However, the current reverse logistics do not respond to remanufacture-to-order at an efficient level. Therefore it is a necessity to develop a novel infrastructure, which can deal with these issues. This paper presents a framework called e-reverse logistics that aims at filling this gap. The major features and architecture of the proposed e-reverse logistics are detailed in this work

    Enabling and Understanding Failure of Engineering Structures Using the Technique of Cohesive Elements

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    In this paper, we describe a cohesive zone model for the prediction of failure of engineering solids and/or structures. A damage evolution law is incorporated into a three-dimensional, exponential cohesive law to account for material degradation under the influence of cyclic loading. This cohesive zone model is implemented in the finite element software ABAQUS through a user defined subroutine. The irreversibility of the cohesive zone model is first verified and subsequently applied for studying cyclic crack growth in specimens experiencing different modes of fracture and/or failure. The crack growth behavior to include both crack initiation and crack propagation becomes a natural outcome of the numerical simulation. Numerical examples suggest that the irreversible cohesive zone model can serve as an efficient tool to predict fatigue crack growth. Key issues such as crack path deviation, convergence and mesh dependency are also discussed
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