DClEVerNet: Deep Combinatorial Learning for Efficient EV Charging Scheduling in Large-scale Networked Facilities

With the electrification of transportation, the rising uptake of electric vehicles (EVs) might stress distribution networks significantly, leaving their performance degraded and stability jeopardized. To accommodate these new loads cost-effectively, modern power grids require coordinated or ``smart'' charging strategies capable of optimizing EV charging scheduling in a scalable and efficient fashion. With this in view, the present work focuses on reservation management programs for large-scale, networked EV charging stations. We formulate a time-coupled binary optimization problem that maximizes EV users' total welfare gain while accounting for the network's available power capacity and stations' occupancy limits. To tackle the problem at scale while retaining high solution quality, a data-driven optimization framework combining techniques from the fields of Deep Learning and Approximation Algorithms is introduced. The framework's key ingredient is a novel input-output processing scheme for neural networks that allows direct extrapolation to problem sizes substantially larger than those included in the training set. Extensive numerical simulations based on synthetic and real-world data traces verify the effectiveness and superiority of the presented approach over two representative scheduling algorithms. Lastly, we round up the contributions by listing several immediate extensions to the proposed framework and outlining the prospects for further exploration

Towards Autonomous and Safe Last-mile Deliveries with AI-augmented Self-driving Delivery Robots

In addition to its crucial impact on customer satisfaction, last-mile delivery (LMD) is notorious for being the most time-consuming and costly stage of the shipping process. Pressing environmental concerns combined with the recent surge of e-commerce sales have sparked renewed interest in automation and electrification of last-mile logistics. To address the hurdles faced by existing robotic couriers, this paper introduces a customer-centric and safety-conscious LMD system for small urban communities based on AI-assisted autonomous delivery robots. The presented framework enables end-to-end automation and optimization of the logistic process while catering for real-world imposed operational uncertainties, clients' preferred time schedules, and safety of pedestrians. To this end, the integrated optimization component is modeled as a robust variant of the Cumulative Capacitated Vehicle Routing Problem with Time Windows, where routes are constructed under uncertain travel times with an objective to minimize the total latency of deliveries (i.e., the overall waiting time of customers, which can negatively affect their satisfaction). We demonstrate the proposed LMD system's utility through real-world trials in a university campus with a single robotic courier. Implementation aspects as well as the findings and practical insights gained from the deployment are discussed in detail. Lastly, we round up the contributions with numerical simulations to investigate the scalability of the developed mathematical formulation with respect to the number of robotic vehicles and customers

Multi-Agent Chance-Constrained Stochastic Shortest Path with Application to Risk-Aware Intelligent Intersection

In transportation networks, where traffic lights have traditionally been used for vehicle coordination, intersections act as natural bottlenecks. A formidable challenge for existing automated intersections lies in detecting and reasoning about uncertainty from the operating environment and human-driven vehicles. In this paper, we propose a risk-aware intelligent intersection system for autonomous vehicles (AVs) as well as human-driven vehicles (HVs). We cast the problem as a novel class of Multi-agent Chance-Constrained Stochastic Shortest Path (MCC-SSP) problems and devise an exact Integer Linear Programming (ILP) formulation that is scalable in the number of agents' interaction points (e.g., potential collision points at the intersection). In particular, when the number of agents within an interaction point is small, which is often the case in intersections, the ILP has a polynomial number of variables and constraints. To further improve the running time performance, we show that the collision risk computation can be performed offline. Additionally, a trajectory optimization workflow is provided to generate risk-aware trajectories for any given intersection. The proposed framework is implemented in CARLA simulator and evaluated under a fully autonomous intersection with AVs only as well as in a hybrid setup with a signalized intersection for HVs and an intelligent scheme for AVs. As verified via simulations, the featured approach improves intersection's efficiency by up to $200\%$ while also conforming to the specified tunable risk threshold

Measuring the predictability of life outcomes with a scientific mass collaboration.

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences

Priorities for protected area research

A hundred research priorities of critical importance to protected area management were identified by a targeted survey of conservation professionals; half researchers and half practitioners. Respondents were selected to represent a range of disciplines, every continent except Antarctica and roughly equal numbers of men and women. The results analysed thematically and grouped as potential research topics as by both practitioners and researchers. Priority research gaps reveal a high interest to demonstrate the role of protected areas within a broader discussion about sustainable futures and if and how protected areas can address a range of conservation and socio-economic challenges effectively. The paper lists the hundred priorities structured under broad headings of management, ecology, governance and social (including political and economic issues) and helps contribute to setting future research agendas

Priorities for protected area research

CITATION: Dudley, N. et al. 2018. Priorities for protected area research. Parks, 24(1):35-50, doi:10.2305/IUCN.CH.2018.PARKS-24-1ND.en.The original publication is available at https://parksjournal.comA hundred research priorities of critical importance to protected area management were identified by a targeted survey of conservation professionals; half researchers and half practitioners. Respondents were selected to represent a range of disciplines, every continent except Antarctica and roughly equal numbers of men and women. The results analysed thematically and grouped as potential research topics as by both practitioners and researchers. Priority research gaps reveal a high interest to demonstrate the role of protected areas within a broader discussion about sustainable futures and if and how protected areas can address a range of conservation and socio-economic challenges effectively. The paper lists the hundred priorities structured under broad headings of management, ecology, governance and social (including political and economic issues) and helps contribute to setting future research agendas.Publisher's versio

Priorities for protected area research

A hundred research priorities of critical importance to protected area management were identified by a targeted survey of conservation professionals; half researchers and half practitioners. Respondents were selected to represent a range of disciplines, every continent except Antarctica and roughly equal numbers of men and women. The results analysed thematically and grouped as potential research topics as by both practitioners and researchers. Priority research gaps reveal a high interest to demonstrate the role of protected areas within a broader discussion about sustainable futures and if and how protected areas can address a range of conservation and socio-economic challenges effectively. The paper lists the hundred priorities structured under broad headings of management, ecology, governance and social (including political and economic issues) and helps contribute to setting future research agendas.</p