Much AI research on computational sustainability has focused on monitoring, modeling, analysis, and optimization of existing systems and processes. In this article, we present another exciting and promising paradigm for AI research on computational sustainability that emphasizes design of new systems and processes, and, in particular, on biologically inspired design. We first characterize biologically inspired design, then examine its relationship with environmental sustainability, next present a computational model of the process of biologically inspired design, and finally describe a few computational systems for supporting biologically inspired design practice.

Cities and their employment catchment areas are focus points of economic activity, transportation, and social interactions. The need for land use and transport inte- grated modelling (LUTI modelling) as a decision aid tool in urban planning, has become apparent. Instanti- ating such models on cities, requires a substantial data collection, model structuring and parameter estimation effort; for conciseness, the latter is referred to here as calibration. This work is a partial effort towards the integrated calibration of LUTI models. It considers one of the most widely used LUTI models and softwares, Tranus. The usual calibration approach for Tranus is briefly reviewed. It is then reformulated as an optimisa- tion problem, in order to make it amenable to the sys- tematic incorporation of constraints on parameters and additional data and to form a clear basis for future fully integrated calibration. The problem at hand concerns a dynamic system; an approach is shown how to “elimi- nate” parts of the dynamics in order to ease the param- eter optimisation. We also discuss how to validate cali- bration results and propose to use synthetic data gener- ated from real world problems in order to assess conver- gence properties and accuracy of calibration methods.

Discovering congestion patterns from table-formed traffic reports is critical for traffic bottleneck analysis. However, patterns mined by existing algorithms often do not satisfy user requirements and are not actionable for traffic management. Traffic officers may not pursue the most frequent patterns but expect mining outcomes showing the dependence between congestion and various kinds of road properties for traffic planning. Such multi-view analysis requires to integrate user preferences of data attributes into pattern mining process. To tackle this problem, we propose a multi-view attributes reduction model for discovering the patterns of user interests, in which user views are interpreted as preferred attributes and formulated by attribute orders. Based on the pattern discovery model, a workflow is built for traffic bottleneck analysis, which consists of data preprocessing, preference representation and congestion pattern mining. Our approach is validated on the reports of road conditions from Shanghai, which shows that the resultant multi-view findings are effective for analyzing congestion causes and traffic management.

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.

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.

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.

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.

This paper presents an exploratory study conducted to understand how audio-visual prompts are understood by people on an emotional level as a first step towards the more challenging task of designing emotionally aligned prompts for persons with cognitive disabilities such as Alzheimer’s disease and related dementias (ADRD). Persons with ADRD often need assistance from a caregiver to complete daily living activities such as washing hands, making food, or getting dressed. Artificially intelligent systems have been developed that can assist in such situations. This paper presents a set of prompt videos of a virtual human ‘Rachel’, wherein she expressively communicates prompts at each step of a simple hand washing task, with various human-like emotions and behaviors. A user study was conducted for 30 such videos with respect to three basic and important dimensions of emotional experience: evaluation, potency, and activity. The results show that, while people generally agree on the evaluation (valence: good/bad) of a prompt, consensus about power and activity is not as socially homogeneous. Our long term aim is to enhance such systems by delivering automated prompts that are emotionally aligned with individuals in order to help with prompt adherence and with long-term adoption.