A new approach to service provisioning in ATM networks

The authors formulate and solve a problem of allocating resources among competing services differentiated by user traffic characteristics and maximum end-to-end delay. The solution leads to an alternative approach to service provisioning in an ATM network, in which the network offers directly for rent its bandwidth and buffers and users purchase freely resources to meet their desired quality. Users make their decisions based on their own traffic parameters and delay requirements and the network sets prices for those resources. The procedure is iterative in that the network periodically adjusts prices based on monitored user demand, and is decentralized in that only local information is needed for individual users to determine resource requests. The authors derive the network's adjustment scheme and the users' decision rule and establish their optimality. Since the approach does not require the network to know user traffic and delay parameters, it does not require traffic policing on the part of the network

A Comprehensive Survey of Potential Game Approaches to Wireless Networks

Potential games form a class of non-cooperative games where unilateral improvement dynamics are guaranteed to converge in many practical cases. The potential game approach has been applied to a wide range of wireless network problems, particularly to a variety of channel assignment problems. In this paper, the properties of potential games are introduced, and games in wireless networks that have been proven to be potential games are comprehensively discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on Communications, vol. E98-B, no. 9, Sept. 201

Identifying Listed Properties Based on Imagery

Generally, the present disclosure is directed to identifying properties that are listed for sale or similarly available based on imagery of the properties. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict that a property is listed for sale or similarly available and/or characteristics about the listing based on imagery of the property, including imagery containing listing signage

Dynamic pricing and long-term planning models for managed lanes with multiple entrances and exits

Express lanes or priced managed lanes provide a reliable alternative to travelers by charging dynamic tolls in exchange for traveling on lanes with no congestion. These lanes have various locations of entrances and exits and allow travelers to adapt their route based on the toll and travel time information received at a toll gantry. In this dissertation, we incorporate this adaptive lane choice behavior in improving the dynamic pricing and long-term planning models for managed lanes with multiple entrances and exits. Lane choice of travelers minimizing their disutility is affected by the real-time information about tolls and travel time through variable message signs and perceived information from past experiences. In this dissertation, we compare various adaptive lane choice models differing in their reliance on real-time information or historic information or both. We propose a decision route lane choice model that efficiently compares the disutility over multiple routes on an express lane. Assuming drivers’ disutility is only affected by tolls and travel times, we show that the decision route model generates only up to 0.93% error in expected costs compared to the optimal adaptive lane choice model, making it a suitable choice for modeling lane choice of travelers. Next, using the decision route lane choice framework, we improve the current dynamic pricing models for express lanes that commonly ignore adaptive lane choice, assume simplified traffic dynamics, and/or are based on simplified heuristics. Formulating the dynamic pricing problem as an MDP, we optimize the tolls for various objectives including maximizing revenue and minimizing total system travel time (TSTT). Three solution algorithms are evaluated: (a) an algorithm based on value-function approximation, (b) a multiagent reinforcement learning algorithm with decentralized tolling at each gantry, and (c) a deep reinforcement learning assuming partial observability of traffic state. These algorithms are shown to outperform other heuristics such as feedback control heuristics by generating up to 10% higher revenues and up to 9% lower delays. Our findings also reveal that the revenue-maximizing optimal policies follow a “jam-and-harvest” behavior where the toll-free lanes are pushed towards congestion in the earlier time steps to generate higher revenue later, a characteristic not observed for the policies minimizing TSTT. We use reward shaping methods to overcome the undesired behavior of toll policies and confirm transferability of the algorithms to new input domains. We also offer recommendations on real-time implementations of pricing algorithms based on solving MDPs. Last, we incorporate adaptive lane choice in existing long-term planning models for express lanes which commonly represent these lanes as fixed-toll facilities and ignore en route adaptation of lane choices. Defining the improved model as an equilibrium over adaptive lane choices of self-optimizing travelers and formulating it as a convex program, we show that long-term traffic forecasts can be underestimated by up to 45% if adaptive route choice is ignored. For solving the equilibrium, we develop a gradient-projection algorithm which is shown to be efficient than existing link-state algorithms in the literature. Additionally, we estimate the sensitivity of equilibrium expected costs with demand variation by formulating it as a convex program solved using a variant of the gradient projection algorithm proposed earlier. This analysis simplifies a complex express lane network as a single directed link, allowing integration of adaptive lane choice for planning of express lanes without significantly altering the components of traditional planning models. Overall these models improve the state-of-the-art of pricing and planning for managed lanes useful for evaluating future express lane projects and for operations of express lanes with multiple objectives.Civil, Architectural, and Environmental Engineerin

Generating Travel Itineraries Based on User Interests

Generally, the present disclosure is directed to generating a travel itinerary for a user based on the user’s interests. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict interest of a user and generate a travel itinerary based on user preferences and interests

Reinforcement Learning Based Cooperative P2P Energy Trading between DC Nanogrid Clusters with Wind and PV Energy Resources

In order to replace fossil fuels with the use of renewable energy resources, unbalanced resource production of intermittent wind and photovoltaic (PV) power is a critical issue for peer-to-peer (P2P) power trading. To resolve this problem, a reinforcement learning (RL) technique is introduced in this paper. For RL, graph convolutional network (GCN) and bi-directional long short-term memory (Bi-LSTM) network are jointly applied to P2P power trading between nanogrid clusters based on cooperative game theory. The flexible and reliable DC nanogrid is suitable to integrate renewable energy for distribution system. Each local nanogrid cluster takes the position of prosumer, focusing on power production and consumption simultaneously. For the power management of nanogrid clusters, multi-objective optimization is applied to each local nanogrid cluster with the Internet of Things (IoT) technology. Charging/discharging of electric vehicle (EV) is performed considering the intermittent characteristics of wind and PV power production. RL algorithms, such as deep Q-learning network (DQN), deep recurrent Q-learning network (DRQN), Bi-DRQN, proximal policy optimization (PPO), GCN-DQN, GCN-DRQN, GCN-Bi-DRQN, and GCN-PPO, are used for simulations. Consequently, the cooperative P2P power trading system maximizes the profit utilizing the time of use (ToU) tariff-based electricity cost and system marginal price (SMP), and minimizes the amount of grid power consumption. Power management of nanogrid clusters with P2P power trading is simulated on the distribution test feeder in real-time and proposed GCN-PPO technique reduces the electricity cost of nanogrid clusters by 36.7%.Comment: 22 pages, 8 figures, to be submitted to Applied Energy of Elsevie