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The blocking probability of a finite-source bufferless queue is a fixed point of the Engset formula, for which we prove existence and uniqueness. Numerically, the literature suggests a fixed point iteration. We show that such an iteration can fail to converge and is dominated by a simple Newton's method, for which we prove a global convergence result. The analysis yields a new Tur\'an-type inequality involving hypergeometric functions, which is of independent interest., Comment: 13 pages, 2 figures, 4 tables. Error in statement of Lemma 11 in [v2] fixed. Error in proof of Lemma 2 in [v3] fixed

We refer to a vehicle pool as a number of vehicles at a single location used for the same purpose. We focus on the problem of sizing vehicle pools for a finite set of subscribers who can use the pool. Our goal is to minimize the number of vehicles in the pool while still meeting nearly all subscriber requests. Formally, we propose three analytical techniques to size a vehicle pool for a finite population of subscribers, according to the pools' busy period demand to guarantee all requests are served with probability $\hbox{1} - \epsilon$ , i.e., a quality-of-service (QOS) guarantee. Moreover, we propose an additional heuristic sizing method, which requires no prior data about pool demand. Although this method does not provide probabilistic bounds on QOS, we show in practice that it still achieves a high QOS. We evaluate our sizing methodologies using seven years of data from a local car share, using three performance metrics: availability (percentage of requests served), utilization (the percentage of time that vehicles in the pool are used) and member-to-vehicle ratio (the size of the pool relative to the size of its user population). We show that our methods perform well with respect to these metrics.

We study whether taxi companies can simultaneously save petroleum and money by transitioning to electric vehicles. We propose a process to compute the return on investment of transitioning a taxi corporation’s fleet to electric vehicles. We use Bayesian data analysis to infer the revenue changes associated with the transition. We do not make any assumptions about the vehicles’ mobility patterns; instead, we use a time-series of GPS coordinates of the company’s existing petroleum-based vehicles to derive our conclusions. As a case study, we apply our process to a major taxi corporation, Yellow Cab San Francisco (YCSF). Using current prices, we find that transitioning their fleet to battery electric vehicles and plug-in hybrid electric vehicles is profitable for the company. Furthermore, given that gasoline prices in San Francisco are only 5.4 % higher than the rest of the United States, but electricity prices are 75 % higher; taxi companies with similar practices and mobility patterns in other cities are likely to profit more than YCSF by transitioning to electric vehicles.

Electric Vehicles (EVs) are envisioned to play a large role in the transition from fossil fuel to renewables based transportation. However, their sales thus far are nominal compared to traditional car sales. It has been difficult for manufacturers to measure owners' initial perceptions in order to build improved vehicles more drivers are likely to adopt. Sentiments towards EVs have mostly been determined using either field trials or large surveys of drivers, both of which are problematic. We build a system that mines EV owners' sentiments from online forums. Our system has three main uses. First, it graphs the percentage of positive and negative opinions for each vehicle feature of interest, e.g., battery capacity, giving the user a high level product overview. There is currently no easily-consumable review system for EVs. Second, it allows the user to read opinions about the specific features they are most interested in without searching though irrelevant text. In our case study, we find only 3% of the comments on EV ownership forums express opinions on the features. The system therefore reduces the space of text the user must read by 97%, even assuming they wish to read all opinions about all features. Finally, in addition to mining the same perceptions found during expensive field trials, our system finds perceptions that were only realized after the owners possessed their EVs for an extended period of time, i.e., perceptions not available during shorter trials. The system extracts and classifies opinions with a precision and recall of 60%, which is on par or better than previous opinion mining systems.

The Ontario electrical grid is sized to meet peak electricity load. If this worst-case load were reduced, the government and Ontario tax-(payers could defer large infrastructural costs, reducing the cost of generation and electricity prices. Storage, batteries that can store energy during times of low load and be discharged during times of peak load, is one proposed solution to reducing peak load. We evaluate the effect of storage on the electrical grid under different customer electricity pricing schemes. We find that for existing pricing schemes, adopting storage is not profitable. Furthermore, r as the level of storage adoption in the population increases, pricing schemes that incentivize charging at times known to all homeowners will eventually increase the peak load rather than decrease it. However, in some circumstances particular levels of homeowner i storage adoption helps the grid reduce peak load, and thus the grid t may choose to subsidize the cost of storage. We discuss hypothetical pricing schemes under which storage adoption is profitable for homeowners.

Mobile phones play an increasingly important role on our everyday lives. It is becoming increasingly important to provide a reliable and easy to use method for securing these devices against unauthorized access. This paper describes an approach to adapt iris recognition for resource-constrained mobile phones by reducing its computational complexity. Experimental results indicate adequate run time and quality of recognition that is comparable to other, more complex iris recognition systems developed for mobile devices.