Taxi service is an indispensable part of public transport in modern cities. The taxi system is operated by a large number of self-controlled drivers lacking of centralized scheduling and control, which makes it inefficient, difficult to analyze and optimize. It is thus important to take into account taxi drivers' strategic behavior in order to optimize taxi systems' efficiency. This paper reviews existing taxi system researches for modeling taxi system dynamics, introduces the taxi system efficiency optimization problem, and presents a game theoretic approach for optimizing the efficiency of taxi systems. Challenges and open issues in the taxi system efficiency optimization problem are also discussed.

We consider the online setting where a user would like to continuously release a time-series of data that is correlated with his private data, to a service provider in the hope of deriving some utility. Due to correlations, the continual observation of the released time-series puts the user at risk of inference of his private data by an adversary. To protect the user from inference attacks on his private data, the time-series is randomized prior to its release according to a probabilistic privacy mapping. The privacy mapping should be designed in a way that balances privacy and utility requirements over time.Our contributions are threefold. First, we formalize the framework for the design of utility-aware privacy mappings for time-series data, under both online and batch models. We provide a sequential scheme that allows to design online privacy mappings at scale, that account for privacy risk from the history of released data and future releases to come. Second, we prove the equivalence of the optimal mappings under the batch and the online models, in the case where the time-series samples are independent across time. We further show that there exists a gap between optimal batch and online privacy mappings when certain conditions are not satisfied.Finally, we evaluate the performance of the framework over synthetic and real-world time-series data. In particular, we show that smart-meter data can be randomized for privacy purposes to prevent disaggregation of per-device energy consumption, while preserving the utility.

This paper introduces a novel autonomous interactive learning cooperative (ILCP) who receives expected value and variance of load from consumers and participates in the electricity market on their behalf. Using an axiomatic approach, the share of each consumer's payment as well as its weight in calculating the modification of total day-ahead load are formulated. This scheme applies double-seasonal smoothing exponential, a recent load forecasting technique, and a classifier for real-time to day-ahead price direction forecasting (Gaussian Naïve Bayes). In addition to this, the ILCP employs interactive cooperative algorithms for both trading cooperative and consumer side. The ILCP scheme is investigated and its performance is compared to those of non-cooperative real-time pricing (RTP), LCP (non-interactive learning cooperative) and CP (non-interactive non-learning cooperative). The developed system was implemented using PJM(world's largest  wholesale electricity market) real-time and day-ahead data for 2013 and half of 2014; real load profiles were selected from a set of 579 residential and commercial consumers, and weather data were applied to forecasting electricity price direction. We demonstrate the advantages of ILCP to lower the average electricity cost and to reduce unit price variations.

In the presented research project, a software and hardware infrastructure for parking space focussed inter-modal route planning in a public pilot region in Berlin is developed. One central topic is the development of a prediction system which gives an estimated occupancy for the parking spaces in the pilot region for a given date and time in the future. Occupancy data will be collected online by roadside parking sensors developed within the project. The occupancy prediction will be implemented using “Neural Gas” machine learning in combination with a proposed method which uses data threads to improve the prediction quality. In this paper, a short overview of the whole research project is given. Furthermore, the concept of the software framework and the learning methods are presented and first collected data is shown. The prediction method using data threads is explained in more detail.

Recent increase in interest in using rotorcraft (helicopters and tilt-rotor craft) for public transportation has spurred research in making rotorcraft less noisy, particularly as they land. The ground noise associated with landing trajectories followed by rotorcraft depends in part on the changes in altitude and velocity of the rotorcraft during flight. Acoustic models of ground noise taking altitude and velocity effects into account can be used in an optimization process to determine a set of potentially quieter pilot operations. However, optimizing solely for acoustic properties produces patterns that abstract away from the environment in which the trajectory is flown. A quiet procedure flown over a residential area can create considerable annoyance. To overcome this limitation of acoustic-based optimization we propose a hybrid cost model for optimization that combines acoustic criteria with a land use model that views noise-sensitive areas around landing facilities as weighted obstacles. The result is a 3D route planning problem with obstacles. We introduce a system, called NORA (Noise Optimization for Rotorcraft Approach) that allows for the computation of trajectories that simultaneously solve for acoustically quiet patterns that also avoid land sensitive areas.

In most urban area, traffic issue related to parking (e.g. economic(time) and environmental loss in finding a parking space) has been significant, and parking management strategies to set optimal price are often necessary for the parking agencies. In order to revise parking fee appropriately without a reduction of parking demand, a pricing procedure in accordance with characteristic of parking utilization is expected. On the other hand, a large amount of parking data is accumulated automatically with introduction of online parking systems. In this study, we analyze massive parking accounting data, whose data size is22.5 million accounting data in the past year about 1,050 parking lots,and discuss the characteristics of parking utilization. Moreover parking duration model is developed from the accounting data for each cluster to estimate the parking demand (i.e., parking time) after changing price. As an example of appropriate price procedure, we evaluate the setting of an upper limitation of parking charge with parking demand patterns calculated from the accounting data and the parking duration model.

Buildings make a city’s landscape and are home to its people. The demand for smart buildings and housing is growing by the need for cities to make their buildings more efficient, green and livable. This emergent intelligence is underpinned by the use of Information and Communications Technology (ICT) linked by Pervasive Sensing and real-time data analytics. In a typical growth of smart buildings, Smart Campuses are going to be amazing community hubs which will be more sustainable, efficient and supportive of its inhabitants. In this regard, huge amount of useful and real-time generated data are being analyzed to help people and machines infer instant decisions in relation to energy efficiency. However, because of different terminologies used by different players, structural, representational and semantic heterogeneity constrain the interoperability between applications and misleads to adaptive and context-aware control behavior. In this paper, the focus is to alleviate the current problem by designing a semantic framework that represents the smart campus data and activities in an ontological model. Also, the framework is deepened by an Artificial Intelligent (AI) method using Weighted Case Based Reasoning (WCBR) for enabling context awareness. An illustration will be the elaboration of an adaptive and autonomous control of HVAC (Heating Ventilation and Air Conditioning) system, in this example the WCBR is discussed and case representation, case adaptation, and similarity computation are sketched in detail.

Infants at risk for cerebral palsy are at a severe disadvantage in learning to crawl as compared with typically developing infants. An assistive system is being created at the University of Oklahoma to improve these children's crawling abilities. The infants are: outfitted with a suit that allows kinematic reconstruction of their movements; EEG monitoring of their neural responses; and placed in an assistive robot that can amplify the effectiveness of their crawling actions and reduce the required weight bearing for successful prone locomotion. The system can also map their attempted motions into a library of recognized movements, and create directed robot motion even when the subject has not generated any propulsive forces on their own.

Cochlear implants (CIs) are an effective intervention for individuals with severe-to-profound sensorineural hearing loss. Currently, no tuning procedure exists that can fully exploit the technology. We propose online unsupervised algorithms to learn features from the speech of a severely-to-profoundly hearing-impaired patient round-the-clock and compare the features to those learned from the normal hearing population using a set of neurophysiological metrics. Experimental results are presented. The information from comparison can be exploited to modify the signal processing in a patient’s CI to enhance his audibility of speech.

In this challenge paper, we consider the importance of preferences in smart homes and assistive environments and discuss the potential application of models and algorithms developed within the computational preferences community. We suggest the value of future research collaborations.