The ability to estimate future velocity on a road network is relevant for applications such as vehicle navigation systems and emergency vehicle dispatching. The existence of traffic congestion severely impacts travellers’ travel time. In this paper, we investigate the use of congestion prediction in velocity forecasting models. Using a data-driven approach, we classify traffic observations into classes with and without congestion. We find that this classification improves velocity forecasting, showing that using congestion as an attribute reduces the MAE by at least 6.15% for different machine and deep learning models including random forests, multi-layer perceptrons and recurrent neural networks. We propose a random forest model that identifies the future congestion state from past traffic velocity and volume data and then use it to build new short-term velocity forecasting models. These models reduce the MAE prediction error up to 3.37% over the best models that do not consider congestion. This improvement represents overcoming a 53.75% of the error due to not precisely knowing the future congestion state.

In this work we consider the shortest path problem with uncertainty in arc lengths and convex risk measure objective. We explore efficient implementations of sample average approximation (SAA) methods to solve shortest path problems when the conditional value at risk and entropic risk measures are used and there is correlation present in the uncertain arc lengths. Our work explores the use of different decomposition techniques to achieve an efficient implementation of SAA methods for these nonlinear convex integer optimization problems. A computational study shows the effect of geometry, uncertainty correlation and variance, and risk measure parameters on efficiency and accuracy of the methods developed. Data and the online supplement are available at https://doi.org/10.1287/ijoc.2017.0795 .

Demand-responsive transport (DRT) systems provide flexible transport services for passengers who request door-to-door rides in shared-ride mode without fixed routes and schedules. DRT systems face interesting coordination challenges. For example, one has to design cost-sharing mechanisms for offering fare quotes to potential passengers so that all passengers are treated fairly. The main issue is how the operating costs of the DRT system should be shared among the passengers (given that different passengers cause different amounts of inconvenience to the other passengers), taking into account that DRT systems should provide fare quotes instantaneously without knowing future ride request submissions. We determine properties of cost-sharing mechanisms that make DRT systems attractive to both the transport providers and passengers, namely online fairness, immediate response, individual rationality, budget balance, and ex-post incentive compatibility. We propose a novel cost-sharing mechanism, which is called Proportional Online Cost Sharing (POCS), which provides passengers with upper bounds on their fares immediately after their ride request submissions despite missing knowledge of future ride request submissions, allowing them to accept their fare quotes or drop out. We examine how POCS satisfies these properties in theory and computational experiments.

Key in the efforts to deter and prevent nuclear terrorism is the ability to detect the presence of possible nuclear threats in a given area. Resources capable of detecting such threats are limited, expensive, and only capable of scanning a certain total area in a given amount of time. This limit on the ability to detect nuclear threats makes imperative the development of efficient deployment strategies of the detection resources. In this work, we propose a Stackelberg game-based model to determine the optimal patrolling strategy of security assets over a network in the presence of a strategic adversary that seeks to place a nuclear threat on edges of the network. To efficiently solve this model, we introduce a novel decomposition of the problem which requires the solution of a multivehicle rural Chinese postman problem (CPP). Our theoretical contributions present hardness and approximation results for the k-vehicle rural CPP. Our computational results demonstrate the benefit of this decomposition for the nuclear threat detection security problem. © 2014 Wiley Periodicals, Inc. NETWORKS, Vol. 64(3), 181–191 2014

Given the real-world deployments of attacker-defender Stackelberg security games, robustness to deviations from expected attacker behaviors has now emerged as a critically important issue. This paper provides four key contributions in this context. First, it identifies a fundamentally problematic aspect of current algorithms for security games. It shows that there are many situations where these algorithms face multiple equilibria, and they arbitrarily select one that may hand the defender a significant disadvantage, particularly if the attacker deviates from its equilibrium strategies due to unknown constraints. Second, for important subclasses of security games, it identifies situations where we will face such multiple equilibria. Third, to address these problematic situations, it presents two equilibrium refinement algorithms that can optimize the defender's utility if the attacker deviates from equilibrium strategies. Finally, it experimentally illustrates that the refinement approach achieved significant robustness in consideration of attackers' deviation due to unknown constraints.

Attacker-defender Stackelberg games have become a popular game-theoretic approach for security with deployments for LAX Police, the FAMS and the TSA. Unfortunately, most of the existing solution approaches do not model two key uncertainties of the real-world: there may be noise in the defender's execution of the suggested mixed strategy and/or the observations made by an attacker can be noisy. In this paper, we provide a framework to model these uncertainties, and demonstrate that previous strategies perform poorly in such uncertain settings. We also provide RECON, a novel algorithm that computes strategies for the defender that are robust to such uncertainties, and provide heuristics that further improve RECON's efficiency.

Recent real-world deployments of Stackelberg se-curity games make it critical that we address hu-man adversaries ’ bounded rationality in comput-ing optimal strategies. To that end, this paper provides three key contributions: (i) new efficient algorithms for computing optimal strategic solu-tions using Prospect Theory and Quantal Response Equilibrium; (ii) the most comprehensive experi-ment to date studying the effectiveness of different models against human subjects for security games; and (iii) new techniques for generating representa-tive payoff structures for behavioral experiments in generic classes of games. Our results with human subjects show that our new techniques outperform the leading contender for modeling human behav-ior in security games. 1

Security games, and important class of Stackelberg games, are used in deployed decision-support tools in use by LAX police and the Federal Air Marshals Service. The algorithms used to solve these games find optimal randomized schedules to allocate security resources for infrastructure protection. Unfortunately, the state of the art algorithms either fail to scale or to provide a correct solution for large problems with arbitrary scheduling constraints. We introduce ASPEN, a branch-and-price approach that overcomes these limitations based on two key contributions: (i) A column-generation approach that exploits a novel network flow representation, avoiding a combinatorial explosion of schedule allocations; (ii) A branch-and-bound algorithm that generates bounds via a fast algorithm for solving security games with relaxed scheduling constraints. ASPEN is the first known method for efficiently solving massive security games with arbitrary schedules.

In this paper we consider the problem of allocating personal TV advertisements when the viewers' viewing capacities are uncertain. We focus on the Mobile TV medium as a personal platform taking into account special constraints that are motivated by the TV medium. Since the problem is NP-Hard, we present a sequential solution procedure and propose several heuristic algorithms for solving it. Through computational experiments on different scenarios of uncertainty, the performances of these heuristics were compared to the performance in the deterministic case where the actual viewing capacities are known in advance. The performance of these heuristics was close to that of the deterministic even for scenarios where uncertainty was very high while the CombinedRatingRobust algorithm seems to be the best for most scenarios. In general, the heuristic algorithms that consider uncertainty in advance seem to be preferable over those that only adapt to it after it has been discovered. Though this paper is oriented for Mobile TV, the proposed algorithms are suitable for other personal push-advertisement methods such as SMS (Short Message Service) and MMS (Multimedia Messaging Service).

Personal advertisements are the next-generation in the world of advertisement. In this article we consider the personal advertisement allocation problem with constraints that are motivated by the TV and Mobile advertisement worlds. This problem is a version of the Generalized Multi-Assignment Problem, defined as an extension of GAP with assignment restrictions and all-or-nothing constraints. We present an Integer Programming (IP) model of the problem, prove that it is an NP-hard problem, and propose heuristic algorithms to solve it. Through computational experiments, we compare the performance of these heuristics to solutions obtained with IP solvers. We show that the Backtrack Heuristic outperforms the others and on average it attains 98% of the possible revenue. Though this paper is Mobile TV oriented, the results suit any other personal advertisement "push" method in the mobile market such as SMS (Short Message Service) and MMS (Multimedia Messaging Service).

Abstract In this article, we are interested in routing vehicles to service a large‐scale bioterrorism emergency. We describe the specifics of routing vehicles in such a large‐scale emergency and decompose the problem into two stages: a planning stage and an operational stage. In the planning stage, we generate the routes well in advance of any emergency. In the operational stage, we take into account the planned routes and the information revealed at the time of the emergency, to decide the delivery quantity and any adjustments to the routes. We propose mathematical formulations and solution approaches for both stages. Lastly, we demonstrate the effectiveness of our formulations and solution procedures in developing robust routes through numerical experiments. © 2009 Wiley Periodicals, Inc. NETWORKS, 2009

The actual performance of a wireless sensor network (WSN) can be severely influenced by uncertainty present in the environment where it is deployed. For example, the distance between nodes, the quality of the communication channel, and the energy consumed in transmission are all problem parameters that may be subject to uncertainty in real domains and can affect performance. In this paper we consider optimization models of WSN subject to distance uncertainty for three classic problems in energy limited WSNs: minimizing the energy consumed, maximizing the data extracted, and maximizing the network lifetime. We use robust optimization to take into account the uncertainty present. In a robust optimization model the uncertainty is represented by considering that the uncertain parameters belong to a bounded, convex uncertainty set U. A robust solution is the one with best worst case objective over this set U. We show that solving for the robust solution in these problems is just as difficult as solving for the problem without uncertainty. Our computational experiments show that, as the uncertainty increases, a robust solution provides a significant improvement in worst case performance at the expense of a small loss in optimality when compared to the optimal solution of a fixed scenario.

Abstract We consider the problem of expanding arc capacities in a network subject to demand and travel time uncertainty. We propose a robust optimization approach to obtain capacity expansion solutions that are insensitive to this uncertainty. Our results show that, under reasonable assumptions for network flow applications, such robust solutions can be computed by solving tractable conic linear problems. For example, the robust solution for a multicommodity flow problem is obtained by solving a linear program if the problem has a single source and sink per commodity and the uncertainty in demand and travel time is given by independent bounded polyhedral sets. Preliminary computational results show that the robust solution is attractive, as it can reduce the worst case cost by more than 20%, while incurring a 5% loss in optimality when compared to the optimal solution of a representative scenario. © 2007 Wiley Periodicals, Inc. NETWORKS, Vol. 50(2), 136–145 2007

The current practice in wireless sensor networks (WSNs) is to develop functional system designs and protocols for information extraction using intuition and heuristics, and validate them through simulations and implementations. We address the need for a complementary formal methodology by developing nonlinear optimization models of static WSN that yield fundamental performance bounds and optimal designs. We present models both for maximizing the total information gathered subject to energy constraints (on sensing, transmission, and reception), and for minimizing the energy usage subject to information constraints. Other constraints in these models correspond to fairness and channel capacity (assuming noise but no interference). We also discuss extensions of these models that can handle data aggregation, interference, and even node mobility. We present results and illustrations from computational experiments using these models that show how the optimal solution varies as a function of the energy/information constraints, network size, fairness constraints, and reception power. We also compare the performance of some simple heuristics with respect to the optimal solutions.