Multiagent incentive contracts are advanced techniques for solving decentralized decisionmaking p... more Multiagent incentive contracts are advanced techniques for solving decentralized decisionmaking problems with asymmetric information. The principal designs contracts aiming to incentivize non-cooperating agents to act in his or her interest. Due to the asymmetric information, the principal must balance the efficiency loss and the security for keeping the agents. We prove both the existence conditions for optimality and the uniqueness conditions for computational tractability. The coupled principal-agent problems are converted to solving a Hamilton-Jacobi-Bellman equation with equilibrium constraints. Extending the incentive contract to a multiagent setting with history-dependent terminal conditions opens the door to new applications in corporate finance, institutional design, and operations research.
Dynamic contracts with multiple agents is a classical decentralized decision-making problem with ... more Dynamic contracts with multiple agents is a classical decentralized decision-making problem with asymmetric information. In this paper, we extend the single-agent dynamic incentive contract model in continuous-time to a multi-agent scheme in finite horizon and allow the terminal reward to be dependent on the history of actions and incentives. We first derive a set of sufficient conditions for the existence of optimal contracts in the most general setting and conditions under which they form a Nash equilibrium. Then we show that the principal's problem can be converted to solving Hamilton-Jacobi-Bellman (HJB) equation requiring a static Nash equilibrium. Finally, we provide a framework to solve this problem by solving partial differential equations (PDE) derived from backward stochastic differential equations (BSDE).
Saigal, Romesh. Linear programming : a modem integrated analysis / Romesh Saigal. p. cm.-(Interna... more Saigal, Romesh. Linear programming : a modem integrated analysis / Romesh Saigal. p. cm.-(International series in operations research & management science; 1) Includes bibliographical references and index.
Real-time parking occupancy information is valuable for guiding drivers' searching for parking sp... more Real-time parking occupancy information is valuable for guiding drivers' searching for parking spaces. Recently many parking detection systems using range-based on-vehicle sensors are invented, but they disregard the practical difficulty of obtaining access to raw sensory data which are required for any feature-based algorithm. In this paper, we focus on a system using short-range radars (SRR) embedded in Advanced Driver Assistance System (ADAS) to collect occupancy information, and broadcast it through a connected vehicle network. The challenge that the data transmitted through ADAS unit has been encoded to sparse points is overcome by a statistical method instead of feature extractions. We propose a two-step classification algorithm combining Mean-Shift clustering and Support Vector Machine to analyze SRR-GPS data, and evaluate it through field experiments. The results show that the average Type I error rate for off-street parking is 15.23% and for on-street parking is 32.62%. In both cased the Type II error rates are less than 20%. Bayesian updating can recursively improve the mapping results. This paper can provide a comprehensive method to elevate automotive sensors for the parking detection function.
The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the ... more The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the "cloud" will be substituted by the "crowd" where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics/models while keeping their personal data stored locally. This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privacypreserving model training, coined as federated learning (FL). This article provides a vision for IoFT and a systematic overview of current efforts towards realizing this vision. Specifically, we first introduce the defining characteristics of IoFT and discuss FL data-driven approaches, opportunities, and challenges that allow decentralized inference within three dimensions: (i) a global model that maximizes utility across all IoT devices, (ii) a personalized model that borrows strengths across all devices yet retains its own model, (iii) a meta-learning model that quickly adapts to new devices or learning tasks. We end by describing the vision and challenges of IoFT in reshaping different industries through the lens of domain experts. Those industries include manufacturing, transportation, energy, healthcare, quality & reliability, business, and computing.
International Series in Operations Research & Management Science, 1995
Saigal, Romesh. Linear programming : a modem integrated analysis / Romesh Saigal. p. cm.-(Interna... more Saigal, Romesh. Linear programming : a modem integrated analysis / Romesh Saigal. p. cm.-(International series in operations research & management science; 1) Includes bibliographical references and index.
We develop an integrative framework to predict the wind power output, considering many uncertaint... more We develop an integrative framework to predict the wind power output, considering many uncertainties. For probabilistic wind power forecasts, all the sources of uncertainties arising from both wind speed prediction and wind-to-power conversion process should be collectively addressed. To this end, we model the wind speed using the inhomogeneous geometric Brownian motion and convert the wind speed's prediction density into the wind power density in a closed-form. The resulting wind power density allows us to quantify prediction uncertainties through prediction intervals and to forecast the power that can minimize the expected prediction cost with unequal penalties on the overestimation and underestimation. We evaluate the predictive power of the proposed approach using data from commercial wind farms located in different sites. The results suggest that our approach outperforms alternative approaches in terms of multiple performance measures.
Macroscopic traffic flow models are widely used to estimate traffic status on a freeway, and the ... more Macroscopic traffic flow models are widely used to estimate traffic status on a freeway, and the fundamental diagram (FD), that establishes a relationship between density and flux, is essential for these models to be effective. By visualizing the 2005 traffic trajectory data from Next Generation SIMulation (NGSIM) project on US Highway 101 and Interstate 80 in California, we observed three states of traffic condition: free flow, mildly congested flow, and highly congested flow; and the resulting shockwaves. The FD is critical in understanding the shockwave phenomenon of congested flow. Therefore, there is a need to develop a more accurate FD that captures the complexity of the density-flux relation observed in the empirical data. For this we develop a three phase FD of traffic flow. In our previous work, a log piecewise linear FD was shown to fit better than some other forms of FD on 2009 traffic data from Interstate 95 in Virginia. However, the previous FD considered two phases, free and congested flow. The three phase modification we propose here is able to distinguish between the highly and the mildly congested flow and explains the resulting shockwaves. The phase transition from mildly to highly congested state changes the nature of the flux-density function from a concave to a convex function of density. In the concave phase a forward moving and in the convex phase a backward moving shock results. This FD can also explain the rarefaction waves that arise in traffic flow. NGSIM data covers every vehicle within its range and we focus our study to the innermost lane. The visualized data is used to fit the three phases. This involved solving an optimization problem to determine the values of the parameters. A close relationship between the backward shockwaves during highly congested phase and the convex part of the FD is observed. A single parameter value (slope of the log-linear function) is shown to predict the various conditions of the traffic: free, mildly and highly congested flow. The proposed FD can be used in conjunction with any macroscopic traffic flow model, such as LighthillWhithamRichards (LWR), for traffic flow estimation with greatly improved accuracy. A three-phase FD and a stochastic macroscopic traffic flow model can reliably predict future traffic status, and provide advanced information to drivers and transportation authorities for travel planning, traffic management, and other real-time applications.
Real-time parking occupancy information is critical for a parking management system to facilitate... more Real-time parking occupancy information is critical for a parking management system to facilitate drivers to park more efficiently. Recent advances in connected and automated vehicle technologies enable sensor-equipped cars (probe cars) to detect and broadcast available parking spaces when driving through parking lots. In this paper, we evaluate the impact of market penetration of probe cars on the system performance, and investigate different parking guidance policies to improve the data acquisition process. We adopt a simulation-based approach to impose four policies on an offstreet parking lot influencing the behavior of probe cars to park in assigned parking spaces. This in turn effects the scanning route and the parking space occupancy estimations. The last policy we propose is a near-optimal guidance strategy that maximizes the information gain of posteriors. The results suggest that an efficient information gathering policy can compensate for low penetration of connected and automated vehicles. We also highlight the policy trade-off that occur while attempting to maximize information gain through explorations and improve assignment accuracy through exploitations. Our results can assist urban policy makers in designing and managing smart parking systems.
Multiagent incentive contracts are advanced techniques for solving decentralized decisionmaking p... more Multiagent incentive contracts are advanced techniques for solving decentralized decisionmaking problems with asymmetric information. The principal designs contracts aiming to incentivize non-cooperating agents to act in his or her interest. Due to the asymmetric information, the principal must balance the efficiency loss and the security for keeping the agents. We prove both the existence conditions for optimality and the uniqueness conditions for computational tractability. The coupled principal-agent problems are converted to solving a Hamilton-Jacobi-Bellman equation with equilibrium constraints. Extending the incentive contract to a multiagent setting with history-dependent terminal conditions opens the door to new applications in corporate finance, institutional design, and operations research.
Dynamic contracts with multiple agents is a classical decentralized decision-making problem with ... more Dynamic contracts with multiple agents is a classical decentralized decision-making problem with asymmetric information. In this paper, we extend the single-agent dynamic incentive contract model in continuous-time to a multi-agent scheme in finite horizon and allow the terminal reward to be dependent on the history of actions and incentives. We first derive a set of sufficient conditions for the existence of optimal contracts in the most general setting and conditions under which they form a Nash equilibrium. Then we show that the principal's problem can be converted to solving Hamilton-Jacobi-Bellman (HJB) equation requiring a static Nash equilibrium. Finally, we provide a framework to solve this problem by solving partial differential equations (PDE) derived from backward stochastic differential equations (BSDE).
Saigal, Romesh. Linear programming : a modem integrated analysis / Romesh Saigal. p. cm.-(Interna... more Saigal, Romesh. Linear programming : a modem integrated analysis / Romesh Saigal. p. cm.-(International series in operations research & management science; 1) Includes bibliographical references and index.
Real-time parking occupancy information is valuable for guiding drivers' searching for parking sp... more Real-time parking occupancy information is valuable for guiding drivers' searching for parking spaces. Recently many parking detection systems using range-based on-vehicle sensors are invented, but they disregard the practical difficulty of obtaining access to raw sensory data which are required for any feature-based algorithm. In this paper, we focus on a system using short-range radars (SRR) embedded in Advanced Driver Assistance System (ADAS) to collect occupancy information, and broadcast it through a connected vehicle network. The challenge that the data transmitted through ADAS unit has been encoded to sparse points is overcome by a statistical method instead of feature extractions. We propose a two-step classification algorithm combining Mean-Shift clustering and Support Vector Machine to analyze SRR-GPS data, and evaluate it through field experiments. The results show that the average Type I error rate for off-street parking is 15.23% and for on-street parking is 32.62%. In both cased the Type II error rates are less than 20%. Bayesian updating can recursively improve the mapping results. This paper can provide a comprehensive method to elevate automotive sensors for the parking detection function.
The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the ... more The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the "cloud" will be substituted by the "crowd" where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics/models while keeping their personal data stored locally. This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privacypreserving model training, coined as federated learning (FL). This article provides a vision for IoFT and a systematic overview of current efforts towards realizing this vision. Specifically, we first introduce the defining characteristics of IoFT and discuss FL data-driven approaches, opportunities, and challenges that allow decentralized inference within three dimensions: (i) a global model that maximizes utility across all IoT devices, (ii) a personalized model that borrows strengths across all devices yet retains its own model, (iii) a meta-learning model that quickly adapts to new devices or learning tasks. We end by describing the vision and challenges of IoFT in reshaping different industries through the lens of domain experts. Those industries include manufacturing, transportation, energy, healthcare, quality & reliability, business, and computing.
International Series in Operations Research & Management Science, 1995
Saigal, Romesh. Linear programming : a modem integrated analysis / Romesh Saigal. p. cm.-(Interna... more Saigal, Romesh. Linear programming : a modem integrated analysis / Romesh Saigal. p. cm.-(International series in operations research & management science; 1) Includes bibliographical references and index.
We develop an integrative framework to predict the wind power output, considering many uncertaint... more We develop an integrative framework to predict the wind power output, considering many uncertainties. For probabilistic wind power forecasts, all the sources of uncertainties arising from both wind speed prediction and wind-to-power conversion process should be collectively addressed. To this end, we model the wind speed using the inhomogeneous geometric Brownian motion and convert the wind speed's prediction density into the wind power density in a closed-form. The resulting wind power density allows us to quantify prediction uncertainties through prediction intervals and to forecast the power that can minimize the expected prediction cost with unequal penalties on the overestimation and underestimation. We evaluate the predictive power of the proposed approach using data from commercial wind farms located in different sites. The results suggest that our approach outperforms alternative approaches in terms of multiple performance measures.
Macroscopic traffic flow models are widely used to estimate traffic status on a freeway, and the ... more Macroscopic traffic flow models are widely used to estimate traffic status on a freeway, and the fundamental diagram (FD), that establishes a relationship between density and flux, is essential for these models to be effective. By visualizing the 2005 traffic trajectory data from Next Generation SIMulation (NGSIM) project on US Highway 101 and Interstate 80 in California, we observed three states of traffic condition: free flow, mildly congested flow, and highly congested flow; and the resulting shockwaves. The FD is critical in understanding the shockwave phenomenon of congested flow. Therefore, there is a need to develop a more accurate FD that captures the complexity of the density-flux relation observed in the empirical data. For this we develop a three phase FD of traffic flow. In our previous work, a log piecewise linear FD was shown to fit better than some other forms of FD on 2009 traffic data from Interstate 95 in Virginia. However, the previous FD considered two phases, free and congested flow. The three phase modification we propose here is able to distinguish between the highly and the mildly congested flow and explains the resulting shockwaves. The phase transition from mildly to highly congested state changes the nature of the flux-density function from a concave to a convex function of density. In the concave phase a forward moving and in the convex phase a backward moving shock results. This FD can also explain the rarefaction waves that arise in traffic flow. NGSIM data covers every vehicle within its range and we focus our study to the innermost lane. The visualized data is used to fit the three phases. This involved solving an optimization problem to determine the values of the parameters. A close relationship between the backward shockwaves during highly congested phase and the convex part of the FD is observed. A single parameter value (slope of the log-linear function) is shown to predict the various conditions of the traffic: free, mildly and highly congested flow. The proposed FD can be used in conjunction with any macroscopic traffic flow model, such as LighthillWhithamRichards (LWR), for traffic flow estimation with greatly improved accuracy. A three-phase FD and a stochastic macroscopic traffic flow model can reliably predict future traffic status, and provide advanced information to drivers and transportation authorities for travel planning, traffic management, and other real-time applications.
Real-time parking occupancy information is critical for a parking management system to facilitate... more Real-time parking occupancy information is critical for a parking management system to facilitate drivers to park more efficiently. Recent advances in connected and automated vehicle technologies enable sensor-equipped cars (probe cars) to detect and broadcast available parking spaces when driving through parking lots. In this paper, we evaluate the impact of market penetration of probe cars on the system performance, and investigate different parking guidance policies to improve the data acquisition process. We adopt a simulation-based approach to impose four policies on an offstreet parking lot influencing the behavior of probe cars to park in assigned parking spaces. This in turn effects the scanning route and the parking space occupancy estimations. The last policy we propose is a near-optimal guidance strategy that maximizes the information gain of posteriors. The results suggest that an efficient information gathering policy can compensate for low penetration of connected and automated vehicles. We also highlight the policy trade-off that occur while attempting to maximize information gain through explorations and improve assignment accuracy through exploitations. Our results can assist urban policy makers in designing and managing smart parking systems.
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