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.

Reducing an automobile's energy consumption will lower its dependency on fossil fuel and extend the travel range of electric vehicles. Automobile Climate Control Systems (CCS) are known to be heavy energy consumers. To help reduce CCS energy consumption, this paper presents an adaptive automated agent, MDP Agent for Climate control Systems -- MACS, which provides drivers advice as to how to set their CCS. First, we present a model which has 78% accuracy in predicting drivers' reactions to different advice in different situations. Using the prediction model, we designed a Markov Decision Process which solution provided the advising policy for MACS. Through empirical evaluation using an electric car, with 83 human subjects, we show that MACS successfully reduced the energy consumption of the subjects by 33% compared to subjects who were not equipped with MACS. MACS also outperformed the state-of-the-art Social agent for Advice Provision (SAP).

We introduce an application of self-driving vehicle technology to the problem of towing aircraft at busy airports from gate to runway and runway to gate. Autonomous towing can be supervised by human ramp- or ATC controllers, pilots, or ground crew. The controllers provide route information to the tugs, assisted by an automated route planning system. The planning system and tower and ground controllers work in conjunction with the tugs to make tactical decisions during operations to ensure safe and effective taxiing in a highly dynamic environment. We argue here for the potential for significantly reducing fuel emissions, fuel costs, and community noise, while addressing the added complexity of air terminal operations by increasing efficiency and reducing human workload. This paper describes work-in-progress for developing concepts and capabilities for autonomous engines-off taxiing using towing vehicles.

In this paper, economic principles and the paradigm of a game are used to create a signal control strategy. The game structure is not formal (as in game theory), but the idea of a game is used nonetheless. That is, instead of using the standard techniques of minimum greens, maximum greens, and gaps to control the signal indications, an economically based game structure is employed. The intersection’s space is viewed as a scarce commodity whose use is determined through a bidding process. Movement Managers manage the vehicle departures for specific turning movements. Arriving motorists pay the Movement Managers an initial fee, and make voluntary contributions as they perceive necessary to arrange times of entry for them. Movement Managers submit bids for use of the intersection’s space and the highest bidders win. Distributed processing and connected vehicle technology are seen as the mechanisms by which implementation would be feasible. The value in such an idea is that one can study and reach an understanding of the economics that underlie effective traffic control.

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.

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.