Traditional roadway safety assessment heavily relies on historical crash data, overlooking real-time factors such as driver behaviors and current traffic conditions and lacking forward-looking analysis for predicting future trends. This study introduces an enhanced innovative data fusion method based on the safe route mapping (SRM) methodology with combined use of historical crash data and real-time data, leveraging a custom-built Android app to amalgamate road and vehicle data effectively, showcasing notable advancements in real-time risk assessment. The enhanced safe route mapping (ESRM) framework monitors driver actions and road conditions meticulously. Data collected from drivers is analyzed on a central server using facial recognition algorithm to detect signs of fatigue and distractions, assessing overall driving competence. Simultaneously, roadside cameras capture live traffic data, analyzed using a specialized video analytics method to track vehicle speed and paths. The fusion of these data streams enables the introduction of a predictive model, Light gradient boosting machine (GBM), forecasting potential immediate issues for drivers. Predicted risk scores are integrated with historical crash data using a Fuzzy logic model, delineating risk levels for different road sections. The performance of ESRM model is tested using real-world data and a driving simulation, demonstrating remarkable accuracy, especially in accounting for real-time fusion of driver behavior and traffic conditions. The resultant visual risk heatmap aids authorities in identifying safer routes, proactive law enforcement deployment, and informed trip planning based on real-time risk levels. This study not only underscores the importance of real-time data in roadway safety but also paves the way for data-driven, dynamic risk assessment models, potentially reducing road accidents and fostering a safer driving environment.

With the emerging connected-vehicle technologies and smart roadways, the need for intelligent adaptive traffic signal controls (ATSC) is more than ever before. This paper first proposes an Accumulated Exponentially Weighted Waiting Time-based Adaptive Traffic Signal Control (AEWWT-ATSC) model to calculate priorities of roadways for signal scheduling. As the size of the traffic network grows, it adds great complexities and challenges to computational efficiencies. Considering this, we propose a novel Distributed Multi-agent Reinforcement Learning (DMARL) with a graph decomposition approach for large-scale ATSC problems. The decomposition clusters intersections by the level of connectivity (LoC), defined by the average residual capacities (ARC) between connected intersections, enabling us to train subgraphs instead of the entire network in a synchronized way. The problem is formulated as a Markov Decision Process (MDP), and the Double Dueling Deep Q Network with Prioritized Experience Replay is utilized to solve it. Under the optimal policy, the agents can select the optimal signal durations to minimize the waiting time and queue size. In evaluation, we show the superiority of the AEWWT-ATSC based RL methods in different densities and demonstrate the DMARL with a graph decomposition approach on a large graph in Manhattan, NYC. The approach is generic and can be extended to various types of use cases.

The use of systematic techniques with historical crash data and qualitative measures has long been a common practice to identify the problematic road features and develop countermeasures to mitigate the crash risk in crash-prone locations. This paper proposes a novel approach,Safe Route Mapping(SRM) model that integrates crash-based estimates with conflict risks computed from driver-based data to score the safety of roadways. An advanced Safety Performance Function (SPF) estimates the number of crashes, and a driver-based model computes dynamic conflict risk measures from driver and traffic data. In real-life implementations of the proposed methodology, the driver-based data and traffic data can be collected from vehicles or infrastructure-based data sources, including smartphones. We demonstrate the methodology using real historical crash data and simulated driver-based data obtained from VISSIM and SSAM. We show safety risk heat maps for the example roadway and illustrate how these maps change with driver types and traffic volumes. The proposed methodology fills the existing gaps in the use of near real-time dynamic data to designate safe corridors, dispatch law enforcement, and plan safety projects. Drivers can also use the road heat maps for situational awareness and trip planning.

Traffic congestion is an inescapable problem that frustrates drivers in megacities. Although there is hardly a way to eliminate the congestion, it is possible to mitigate the impact through predictive methods. This paper develops a data-driven optimization approach for the dynamic shortest path problems (DSPP), considering traffic safety for urban navigations. The dynamic risk scores and travel times at different times and locations are estimated by the Safe Route Mapping (SRM) methodology and Long Short-Term Memory (LSTM) with Autoencoder, respectively, where possible variations in the future are considered. The DSPP is formulated as a mixed-integer linear programming problem under risk constraints to minimize the total travel cost, defined as the weighted sum of distance and travel time. To improve the efficiency of the DSPP, we design an improved tabu search with alternative initial-solution algorithms to accommodate various problem scales. Moreover, subgraph and self-adaptive insertion techniques are adopted as acceleration strategies to enhance computational efficiency further. Numerical experiments investigate the computational performance and the solution quality of our algorithm. The result shows satisfactory solution quality and computational efficiency with the proposed acceleration strategies compared to the CPLEX solver, a label-setting algorithm, and a state-of-the-art algorithm. Our algorithm can also compete with Google Maps regarding the travel cost in a real network in Manhattan, NY, USA, which is promising for Urban Navigations.

Future roadways will have a mix of autonomous and automated vehicles with regular vehicles that require human operators. To ensure the safety of all the road users in such a network, it is necessary to enhance the performance of the present Advanced Driver Assistance System (ADAS) for lower classes of vehicles. Real-time driving safety risk prediction is an essential element of an ADAS. In this paper, we propose a novel data-driven approach to predict traffic safety risk that can be customized for individual drivers by including driver-specific variables. In particular, we have used the elastic net regularized multinomial logistic regression and data from the Second Strategic Highway Research Program (SHRP 2) naturalistic driving study to build the predictive models. This paper rigorously examines the variables in the data set and performs data preparation and feature engineering steps to enhance the prediction performance with respect to model predictors. The model produces good results, and model adaptation/extensions for further improvements are discussed in the conclusion section. Two versions of the model are presented according to the level of warnings that the model can generate based on driving conditions. This paper provides a brief overview of the potential applications of the work.

This work integrates a physics-based model with a data driven time-series model to forecast and optimally manage building energy. Physical characterization of the building is partially captured by a collection of zonal energy balance equations with parameters estimated using a least squares estimation (LSE) technique and data initially generated from the EnergyPlus building model. A generalized Cochran-Orcutt estimation technique is adopted to describe the data generated from these simulations. The combined forecast model is then used in a model predictive control (MPC) framework to manage heating and cooling set points. This work is motivated by the practical limitations of simulation-based optimizations. Once the forecast model is established capturing sufficient statistical variability and physical behavior of the building, there will be no more need to run EnergyPlus in the optimization routine. The proposed methodology lends itself for real-life implementation of building energy management systems where predictive control is desired to reduce energy use and avoid demand charges and occupant discomfort. At each time step, it determines the optimal set point values of all building's zones and updates these values over time. In practice, the proposed control strategy can be implemented in commercial smart energy boxes to optimally control total daily energy-use costs.

In this paper, we analyze and quantify functional value streams of energy storage under different forms (state in which energy is stored) and network location (e.g., transmission or distribution sides). First, we consider a transmission-connected thermal form of storage coupled with concentrating solar power. Using the California power grid as a case study, we show that the coupled unit is significantly less volatile in its response and, through its load following, day-ahead dispatching, and regulation capabilities, it produces significant cost savings. Another case study examines the value of the electrical form of storage in a distribution network and in coupling with photovoltaics. The energy storage is used to mitigate component overloads and to reduce the subsequent infrastructure investments, and smooth out the intermittency of renewable generation. Storage value is quantified formally and under optimal control configuration of storage charge and discharging. Finally, a case study is constructed to examine the functional value of substation-sited storage with respect to investment deferral for the substation transformer.

Applications such as layered manufacturing, or in general, solid free-form fabrication, pose a major challenge on online process control. For these parts to be functional, it is imperative that their mechanical and structural properties are strictly kept within respective tolerances. To that end, no internal or external defects, especially voids, can be tolerated. Since these parts are made layer by layer, it is necessary to inspect top surface and boundary of each layer before the next layer is deposited. Two issues are of major concern here: 1) inspection must be nondestructive, that is, layer surface and boundary must not be touched by the inspecting instruments and 2) the time window for inspection and any corrective measures should not exceed the maximum time limits necessary for two adjacent layers to properly bind together. Here, we present a closed-loop online process control model, where the process feedbacks are obtained from a 3-D imaging system, and where the process dynamics model takes into account the correlation and dependency between adjacent layers. To ensure that the feedback is performed within the tolerable time windows, our 3-D image processing parametric model takes advantage of physical characteristic of layer surface, and uses Gaussian function as a shape descriptor of image units. We obtain 2-D profiles from representative signature(s), and then sweep along road path defined in the CAD model. The idea of reconstructing representative signature comprises some accuracy, but compared with classical shape-from-shading method, the proposed approach is computationally more efficient. The 3-D quality measures (such as volume or depth of voids) are then fed to process dynamics model, which computes the necessary compensation on the deposition flow rate for the next layer. We examine three process dynamics models to And out that a fuzzy model which takes into account correlation between adjacent layers and includes locally linear submodels for underfills and overfills is the most appropriate.

In this paper we study the application of snake-based shape-from-shading to surface quality inspection in Layered Manufacturing. A cross-section of a road surface is modeled using the concept of snake and recovered from its grayscale image, called process signature. An iterative SFS procedure approximates the shape of the snake-modeled surface cross-section with the one obtained from the signature. Two snake SFS techniques are introduced, featuring constant and adaptive spacing between snake elements. Surface recovery of 12 simulated Layered Manufacturing roads is recovered using both techniques, in order to evaluate the accuracy and robustness of the proposed methodology. Snake SFS method is compared to some existing techniques. It is shown that our approach outperforms these earlier techniques when processing images with specular reflectivity and self-shadowing.

In this paper, we provide our preliminary results on failure/fault analysis for an autonomous agent whose behavior is influenced by disturbances from its environment. It is assumed that failures are unobservable and only detected through some observable symptoms. Furthermore, faults may be observable directly, but the conditions leading to them are unknown a priori. Both the agent and its environment may contribute to any given failure or fault. The main results of this paper relate to the development of a diagnoser model which is built by a parallel composition of Petri net models of the underlying components. This model includes both the normative and disruptive behavior of the agent and its perception of the environment. The uncontrollability of the environment leads to a non-deterministic diagnosis behavior. The likelihood of each possible state of the agent's diagnoser is updated every time that a new controllable action is taken or new sensory information is received. At each updating, if the likelihood of a failure or fault state is higher than a specified threshold, then an alarm is raised indicating a disruptive event.

We describe here how to obtain a model for the admissible behavior of a Discrete Event System that is represented by a safe Petri Net model. The events of this Petri Net model may be controllable or uncontrollable. Also given is a sequential specification which is modeled with a special state machine. Then, using the condition and event arcs of Net ConditionlEvent Systems, a combined model of plant and specification is obtained. We use only the structure of this combined model to develop a method which gives the admissible behavior of the system. Thus, we avoid the complexity of a complete state enumeration.

We describe here how to obtain a model for the admissible behavior of a discrete event system that is represented by a safe Petri net (PN) model. The transitions of this PN model may be controllable or uncontrollable. Also given is a sequential specification which is modeled with a special state machine. Then, using the condition and event arcs of net condition/event systems, a combined model of plant and specification is obtained. We use only the structure of this combined model to develop a method which gives the admissible behavior of the system. Thus, we avoid the complexity of a complete state enumeration.

We propose an approach for integration of different levels of logistics and control in a uniform methodology. The main issue is that the formalisms we use on different levels allow fast and easy re-configuration at all levels, from business level down to shop floor control. The proposed technology promotes the plug & play concept and utilizes a similar formalism across all layers of an enterprise and between enterprises within a supply chain. Each plug & play component has embedded in it a controller which can be re-configured dynamically and automatically according to the rules and protocols defined at the upper layers of enterprise or the network it belongs to. Each component also embeds a monitoring system, which provides feedback on its status and if necessary can participate in a distributed diagnosis in the case of faults, drifts, and other disturbances. The paper presents the concepts we have been developing so far and addresses future work on integration of our approaches.

A systematic multi-agent controller synthesis approach is presented in this paper. An abstract format for multi-agent task sequence is defined first. The typical elementary task sequences in multi-agent collaboration applications and the corresponding Petri Nets (PN) models are presented. This correspondence is used as templates to map a task sequence to a synthesized multi-agent PN controller model. We demonstrate the proposed approach using a two-agent controller synthesis problem in a layered manufacturing application.

In this paper, we introduce the concept of switching from one supervisory control to another one when changes in the state of an embedded sensory network occur. The sensory devices are assumed to be single functional, that is, they are programmed to observe and report only one event. The switching model is built based on the two criteria of language observability and state synchronization. Switching model is the basis for a megacontroller, which monitors the state of the sensory network embedded in the plant as well as the state of the supervisor in charge. Every time the state of this network changes, the megacontroller reconfigures the control system by finding an appropriate supervisor.

Despite the efforts in scheduling and control of flexible manufacturing systems (FMSs) with resource constraints, the current pool of scheduling techniques faces two major drawbacks: modeling and complexity. Modeling is the task of converting the FMS data to a set of information, ready to be processed by a scheduling algorithm. Complexity has a direct relation with the amount of effort required to execute a scheduling algorithm successfully on the information set generated in the modeling phase. In this paper, we use finite automata (FA) theory to develop a modeling formalism and its accompanying scheduling algorithm for control and scheduling of FMS with resource constraints. While the FA-based modeling is completely automatic and does not need any human-designer interference, its related algorithm is both effective and efficient. We use IDEF3 standard to capture the FMS activities and resource data. We propose a three-step procedure. In the first step, the IDEF3 data set is converted to a finite automaton, preserving the activity precedence relationships. In the second step, the resulted finite automaton is decomposed to smaller (in size) scheduling problems that can be independently optimized. In the third step, a heuristic scheduling algorithm is used to handle each problem separately. We applied the developed procedure to 100 problems. The results are satisfactory and promising.

In order to reduce manufacturing control software design cycle and costs, it is imperative to reuse off-the-shelf software components, ensure system properties at design time, and automate software design process. To this end, a component and an agent-based plant modeling of three structures and systematic controller synthesis approaches are proposed under a hierarchical flexible manufacturing system (FMS) architecture. That is, the static structure is modeled using class diagrams of Unified Modeling Language (UML); the dynamic structure is modeled using Petri net (PN); the functional logic is modeled using rule sets. A plant PN controller can be derived systematically with the approach proposed in this article while the desirable system behaviors (e.g., liveness and cyclicness) will be obtained.

In practice, a controlled system consists of two distinct entities: the plant and the controller. These two entities are coupled by a feedback mechanism to give the closed-loop behavior of the controlled system. Under this structure, the controlled behavior of the system can be analyzed by simulation which is costly and time consuming. In this study, we synthesize a combined plant and controller model. We consider the practical issues and propose a method which combines the advantages of Petri nets (PN) and the supervisory controller design based on Formal Language Theory. The resultant combined model is a PN and represents the controlled closed-loop behavior of the system. This model can be used for the functional and the performance analysis of the system, verification of the controller and the control code generation.

We present a supervisory control architecture with control redundancy. We assume an adaptive architecture for the problem of switching from one control module to another when sensors used for observing events in the plant fail or become unavailable. It is assumed that there is an actual controller and a number of virtual controllers. The actual controller is in real feedback loop with the plant whereas the virtual ones are in virtual feedback such that the coupled behavior of any of these control modules with plant conforms to what is called normality criterion in the context of partial observation. Every time that a failure occurs and the environment changes, the actual controller is replaced by one of these virtual ones. The selection is based on some optimality criterion and user defined constraints.

We address the problem of obtaining a model for the admissible behavior of a discrete event system in the presence of uncontrollable events. We start with a safe Petri net model of the plant and a sequential specification modeled with a special state machine. Then, a combined model of plant and specification is obtained by using the condition and event arcs of net condition/event systems. Using the structure of this combined model, we are able to develop a method to obtain the admissible behavior which avoids the complexity of a complete state enumeration.

To build high quality functional parts in layered manufacturing process, it is imperative to achieve deposition accuracy. To this end, we propose a real time coordination controller and a tool path based deposition planning scheme respectively. The paper presents the motivations and ideas of these two approaches and makes a comparison.

Layered manufacturing is an additive manufacturing process where the parts are built layer by layer based on the geometry provided by a CAD system. There are several different technologies for layered manufacturing. We focus on fused deposition of ceramics. Here layers are formed by depositing material through a liquefier-head on adjacent roads in the shape of contours and/or rasters. As this technology is being pushed into new frontiers to build functional parts, the quality of the fabricated parts is becoming a major driving force for improving the technology. We present our preliminary results on an online monitoring system that can be used to detect defects. The ultimate objective is to use such a system to detect and remove the defects on any given layer prior to the next layer.

Direct layered manufacturing (LM) is an additive manufacturing process where part is built layer by layer based on its CAD file. Indirect LM is a process where mold for a part is built by direct LM process. In this work we demonstrate a machine vision setup for surface voids detection which can be used for surface quality inspection of parts built by indirect LM and certain direct LM techniques.

The Ramadge-Wonham (RW) framework for designing a supervisory controller for a given plant and user specification, has been widely addressed by many researchers. The framework accepts a generator (or plant model), G, and a specification (or imposed constraint on the plant), H, and generates the supervisory controller, S. It assumes that plant is known and deterministic. However, in many practical cases the designer is not completely aware of the real generator G/sub R/. In this paper the above assumption is removed. It is assumed that G is not fully known. Using the available G and the user specification H, the RW procedure is applied to a supervisor S. Then the family of (real) generators, {G/sub R/}, which can be supervised under S while satisfying the specification H is introduced and different cases are discussed. We establish the mathematical framework for one of the cases where some events originally controllable become uncontrollable or vice versa. It is argued that the complement of {G/sub R/} which violates the specification H can be partially synthesized by available recovery and diagnosis techniques in RW literature if the violation is already known. Otherwise the violated specification must be handled in run time.

This paper presents the concept of process signature for the use of online defect detection in layered manufacturing. To achieve the high quality of parts built by the fused deposition of ceramics, an online process monitoring system is implemented to detect the processing defects. Using the process signature extracted from the original image captured by the monitoring system, an expected image is created which is then compared to the original image to detect and identify the defects.

In an earlier paper, the authors (1997) developed a technique called "relative temporal analysis" to study the performance of timed Petri nets, where depending on the temporal constraints imposed on the transitions, conflict was possible between the processes modelled by the Petri net. The essence of the technique focused on the transformation of the real time trajectory of the processes into a time domain where only their relative shifts were considered. Within this domain the shifts are limited to a bounded and well defined region. Here, we extend this technique to more complex systems where structural conflict is also possible. Within a regenerative cycle of the system, each and every path should be followed according to a set of prespecified frequencies. We use the relative temporal technique to synthesize an optimal sequential controller to schedule these paths.

There are several techniques for the analysis of the dynamics of timed Petri nets. When the net is not decision free, the existing methods come short of an efficient analysis. In this paper, we present a method, we call "relative temporal analysis", to analyze the dynamics of a system with two processes and a shared resource. It is possible to find the resource utilization sequence, the waiting time period for each process and identify possible conflicts through this technique. Based on this technique we also build control charts which can be used for control purposes such as obtaining "optimal" conflict resolution schemes.

We study a Petri net model of a system composed of two processes sharing a resource. Conflicts may occur over the usage of the shared resource, thus making the system nondeterministic. Therefore, in the context of minimax algebra, it cannot be formulated as a linear system in order to compute its performance measures. However, if the sequence by which the resource is allocated to the two processes is known, we can transform the system into a decision-free net. For this system with an imposed constraint on the resource allocation frequencies, we show that the optimal allocation sequence is the most regular integer sequence satisfying that constraint. We also discuss the periodic behavior of this system under no constraints on the resource allocation frequencies.>

Petri nets have been shown to be quite powerful in specifying the control logic for hierarchical systems. Each level of the hierarchy may be modeled by one or more Petri nets. Each Petri net receives tokens from only one higher-level Petri net. Also, any two adjacent Petri nets (in the same level) may pass tokens only through the immediately-higher-level Petri net. Some results concerning the analysis of this type of hierarchical Petri net structures are shown. The authors concentrate on the conservativeness property.>

The author develops a Petri-net-based shop-floor controller with a modular structure. This structure is made possible through the use of adaptive Petri nets. The controller is then analyzed using the theory of Petri nets. The issue of automatic recovery of the controller from failures is discussed. It is shown that in the presence of conflicts, the recovery scheme depends on the scheduling policies.>

We consider a flexible manufacturing cell with two workstations, a single material handling device, and a storage space. Two layouts are considered for the storage space; in one layout the two workstations are sharing a common storage space, whereas, in the other one each workstation has its own local storage. For each layout, we develop a queueing network model. These models explicitly consider the material handling system delay times. In these models, it is also assumed that a workstation could be blocked due to the material handling device. Furthermore, in the local storage layout, a workstation could also be blocked due to the other workstation. Using these models we compare the two layouts and study the effects of mean and variance of the material handling system delay times on workstation utilization. We also discuss some issues related to the design of these cells.