Operating vehicles in adversarial environments between a recurring origin-destination pair requires new planning techniques. Such a technique, presented in this paper, is a game inspired by Ruckle’s original contribution. The goal of the first player is to minimize the expected casualties undergone by a moving agent. The goal of the second player is to maximize this damage. The outcome of the game is obtained via a linear program that solves the corresponding minmax optimization problem over this outcome. The formulation originally proposed by Feron and Joseph is extended to different environment models in order to compute routing strategies over unstructured environments. To compare these methods for increasingly accurate representations of the environment, a grid-based model is chosen to represent the environment and the existence of a sufficient network size is highlighted. A global framework for the generation of realistic routing strategies between any two points is described. Finally the practicality of the proposed framework is illustrated on real world environments.

We propose and evaluate a number of solutions to the problem of calculating the cost to serve each location in a single-vehicle transport setting. Such cost to serve analysis has application both strategically and operationally in transportation. The problem is formally given by the traveling salesperson game (TSG), a cooperative total utility game in which agents correspond to locations in a travelling salesperson problem (TSP). The cost to serve a location is an allocated portion of the cost of an optimal tour. The Shapley value is one of the most important normative division schemes in cooperative games, giving a principled and fair allocation both for the TSG and more generally. We consider a number of direct and sampling-based procedures for calculating the Shapley value, and present the first proof that approximating the Shapley value of the TSG within a constant factor is NP-hard. Treating the Shapley value as an ideal baseline allocation, we then develop six proxies for that value which are relatively easy to compute. We perform an experimental evaluation using Synthetic Euclidean games as well as games derived from real-world tours calculated for fast-moving consumer goods scenarios. Our experiments show that several computationally tractable allocation techniques correspond to good proxies for the Shapley value.

In this paper we describe our ongoing work on SustaInno; an open-source search repository of innovations related to sustainability. SustaInno utilizes advanced information retrieval and text processing methods on technical innovations (initially patent data) to provide its users with practical, applicable, and detailed solutions to their sustainability related challenges. For example, problems like urban heat islands and rainwater waste are of major concern to most urban cities. Using our repository, decision makers can get quite in-depth solutions on practical approaches to address these and many other problems. The novelty of our work stems from three main factors: (1) such a repository does not exist,(2) it is focused on sustainability innovations which are of great importance for the creation of sustainable living environment, and (3) it provides a set of open-source tools and open-access datasets that could accelerate the dissemination of knowledge about sustainability.

We aim to profile characteristics of areas of variant units across a district, city or a country. Studying attributes of areas can be very useful in several situations. In the past, research has focused mainly on studying specific char- acteristics of areas using a few selected attributes. In this paper we propose an alternative view on neighbourhood profiles. Instead of characterising a neighbourhood through a set of attributes such as those collected by the census, we propose use of a low-dimensional fea- ture representation, or embedding, created from one or more input sources. The purpose of the embeddings is having a generic representation for entities that can do well across several downstream tasks such as regression for attributes prediction.

Assuming computational technologies as a dominant factor in forming new scientific methods during the last century, we review the field of computational urban modeling based on the ways different approaches deal with evolving computational and informational capacities. We claim that during the last few years, due to advancements in ubiquitous computing the flow of unstructured data streams have changed the landscape of empirical modeling and simulation. However, there is a conceptual mismatch between the state of the art in urban modeling paradigms and the capacities offered by these urban data streams. We discuss some alternative mathematical methodologies that introduce an abstraction from the traditional urban modeling methodologies.

We consider the problem of indoor human trajectory identification using odometry data from smartphone sensors. Given a segmented trajectory, a simplified map of the environment, and a set of error thresholds, we implement a map-matching algorithm in a urban setting and analyze the accuracy of the resulting path. We also discuss aggregation of user step data into a segmented trajectory. Besides providing an interesting application of learning human motion in a constrained environment, we examine how the uncertainty of the snapped trajectory varies with path length. We demonstrate that as new segments are added to a path, the number of possibilities for earlier segments decreases monotonically. Applications of this work in an urban setting are discussed, as well as future plans to develop a formal theory of odometry-based map-matching.

This is especially the media as an event unfolds. This methodology consists of case in emergency situations, where the spread of a false rumour three main steps: (i) collection of (source) tweets posted during can have dangerous consequences. For instance, in a an emergency situation, sampling in such a way that situation where a hurricane is hitting a region, or a terrorist it is manageable for human assessment, while generating attack occurs in a city, access to accurate information is a good number of rumourous tweets from multiple stories, crucial for finding out how to stay safe and for maximising (ii) collection of conversations associated with each of the citizens' wellbeing. This is even more important in cases source tweets, which includes a set of replies discussing the where users tend to pass on false information more often source tweet, and (iii) collection of human annotations on than real facts, as occurred with Hurricane Sandy in 2012 the tweets sampled. We provide a definition of a rumour (Zubiaga and Ji 2014). Hence, identifying rumours within a which informs the annotation process. Our definition draws social media stream can be of great help for the development on definitions from different sources, including dictionaries of tools that prevent the spread of inaccurate information.

Energy consumption in commercial and educational buildings is impacted by group activities such as meetings, workshops, classes and exams, and can be reduced by scheduling these activities to take place at times and locations that are favorable from an energy standpoint. This paper improves on the effectiveness of energy-aware room-booking and occupancy scheduling approaches, by allowing the scheduling decisions to rely on an explicit model of the building's occupancy-based HVAC control. The core component of our approach is a mixed-integer linear programming (MILP) model which optimally solves the joint occupancy scheduling and occupancy-based HVAC control problem. To scale up to realistic problem sizes, we embed this MILP model into a large neighbourhood search (LNS). We obtain substantial energy reduction in comparison with occupancy-based HVAC control using arbitrary schedules or using schedules obtained by existing heuristic energy-aware scheduling approaches.

The New York Police Department (NYPD) is tasked with responding to a wide range of incidents that are reported through the city's 911 emergency hotline. Currently, response resources are distributed within police precincts on the basis of high-level summary statistics and expert reasoning. In this paper, we describe our first steps towards a better understanding of 911 call activity: temporal behavioral clustering, predictive models of call activity, and anomalous event detection. In practice, the proposed techniques provide decision makers granular information on resource allocation needs across precincts and are important components of an overall data-driven resource allocation policy.

The number of elderly and frail individuals in need of daily assistance increases and the available human resources will certainly be insufficient. To remedy this situation, smart habitats are considered by many researchers as an innovative avenue to help support the needs of elders. It aims at providing cognitive assistance in taking decisions by giving hints, suggestions, and reminders with different kinds of effectors to residents. To implement such technology, the first challenge we need to overcome is the recognition of the ongoing activity. In the literature, some researchers have proposed solutions based on cameras, binary sensors, radio-frequency identification and load signatures of appliances but all these types of approaches have certain limitations to perform a complete recognition. In order to provide additional and useful information, a complementary activity recognition system, based on environmental sounds and able to detect errors related to cognitive impairment, is presented in this paper. The entire system relies on a discrete wavelet transform, the zero-crossing rate and C4.5 algorithm. This system has been implemented and deployed in a real smart-home prototype. This paper also present the results of a first set of experiments conducted on this system with real cases scenarios.