On-line calibration of behavior parameters for behavior-consistent route guidance

Traffic management and route guidance are optimization problems by nature. In this article, we consider algorithms for centralized route guidance and discuss fairness aspects for the individual user resulting from optimal route guidance policies. The first part of this article deals with the mathematical aspects of these optimization problems from the viewpoint of network flow theory. We present algorithms which solve the constrained multicommodity minimum cost flow problem (CMCF) to optimality. A feasible routing is given by a flow x, and the cost of flow x is the total travel time spent in the network. The corresponding optimum is a restricted system optimum with a globally controlled constrained or fairness factor L > 1. This approach implements a compromise between user equilibrium and system optimum. The goal is to find a route guidance strategy which minimizes global and community criteria with individual needs as constraints. The fairness factor L restricts the set of all feasible routes to the subset of acceptable routes. This might include the avoidance of routes which are much longer than shortest routes, the exclusion of certain streets, preferences for scenic paths, or restrictions on the number of turns to be taken. Most remarkably is that the subset of acceptable routes can also be interpreted as a mental map of routes. In the second part we apply our CMCF algorithms in a large scale multi-agent transportation simulation toolkit, which is called MATSIM-T. We use as initial routes the ones computed by our CMCF algorithms. This choice of initial routes makes it possible to exploit the optimization potential within the simulation much better then it was done before. The result is a speed up of the iteration process in the simulation. We compare the existing simulation toolkit with the new integration of CMCF to proof our results.

One of the major research directions in multi-agent systems is dedicated to learning how to coordinate and whether individual agents' decisions can lead to globally optimal or at least acceptable solutions. Our long term goal is to study the effect of several types of information to guide the decision process of the individual agents. This present paper addresses simulation of agents' decision-making regarding route choice, and the role of an information component. This information can be provided by group colleagues, by acquaintances from other groups (small-world), or by route guidance. Besides, we study the role of agents lying about their choices. We compare these scenarios, concluding that information (from some kind of source) is beneficial in general: lying helps only to a certain extent, and route guidance is the best type of information.

For nonlinear discrete systems with dual unknown inputs, there are many limitations regarding previous nonlinear filters. This paper proposes two new, improved square-root cubature Kalman filtering (ISRCKF) algorithms to estimate system states and dual unknown inputs. Improved square-root cubature Kalman filtering 1 (ISRCKF1) introduces an innovation that first obtains the unknown input estimates from the measurement equation, then updates the innovation to derive the unknown input estimates from the state equation, then uses the already obtained estimates of the dual unknown inputs to correct the one-step estimate of the state, and finally the minimum variance unbiased estimate of the state is obtained. Improved square-root cubature Kalman filtering 2 (ISRCKF2) builds a unified innovation feedback model, then applies the minimum variance unbiased estimation (MVUE) criterion to obtain the estimates of system states and dual unknown inputs, refining a more concise recursive filter but requiring stronger assumptions. Finally, simulation results demonstrate that the above two algorithms can achieve the optimal estimates of system states and dual unknown inputs simultaneously, and ISRCKF2 further enhances the accuracy of both state and dual unknown inputs estimation, which verifies the validity of the proposed algorithms.

State estimation is an essential part of every energy control management system. Accurate estimation of state or operating state is essential for security control and monitoring of power systems. Power system state estimation is a procedure to estimate true state from the inexact state of a power system. The conventional state estimator provides estimates of the power system states, i.e., bus voltages and angles which is obtained. State estimation is a computational technique for electrical power system. It empowers the calculation of the power flows of the electrical power system which are not observed or not directly measured. State estimation is a computer program that detects, isolate and eliminate the incorrect or bad measurement data and estimates the accurate state. The magnitudes of bus voltage and phase angle are the states variables for an electrical power system. This paper outlines Weighted Least Square (WLS) estimation techniques and simulated estimation for standard IEEE systems.

Urban multi-criteria optimized route guidance by considering unspecified site satisfaction, an extended type of urban multi-objective optimized route selection, called as both NP-Hard problems and one of the branches of multi-criteria shortest path problems (MSPP). It is not only suggests a route based on route guidance principles and optimized due to routing criteria but also passes through all unspecified site(s) such as gas stations, banks determined by drivers. By proposing a novel approach on the bases of route guidance navigation system principles, virus theory (viral infection and local/site infection) and by GIS and GA utilization, this paper is come up to rate of search improvement in urban multi-criteria optimized route guidance by considering unspecified site satisfaction on real network with multiple dependent criteria. Tests of route selection for a part of north-west of Tehran traffic network are conducted and the results show the efficiency of the algorithm and support our analyses.KeywordsGISNetwork analysisMulti-criteria shortest path problemMulti objective Genetic algorithm

A previously developed Deep Reinforcement Learning-based Vehicle Routing (DRL-VR) algorithm aims to be used for providing the shortest Origin-Destination (OD) travel time path in dynamic traffic environment. However, several issues may still arise regarding uncertainty associated with mixed traffic conditions coexisting Automated Vehicles (AV) and Human-driven Vehicles (HV), particularly in a wide-area urban road network. To develop a robust and interoperable route guidance algorithm based on the DRL approach, this study proposes Transfer learning-based deep reinforcement Learning Algorithm for Route Guidance (TLARG). It is an extended framework on the previous approach by incorporating transfer learning scheme that enables the DRL model of TLARG to converge even in a wide-area urban road network. The TLARG is evaluated in terms of OD travel time based on diverse OD trips with different urban road networks, including narrow- and wide-area road networks. This research conducts several evaluation studies based on microscopic traffic simulation experiments. The simulation result shows that the TLARG enables the agent to complete its OD trips not only with flexible routes but also with reductions in travel time depending on given traffic situations irrespective of network type. Furthermore, it demonstrates that the robustness of the proposed approach by measuring the error of Estimated Time of Arrival (ETA) for various OD trips in different urban road networks under the mixed traffic conditions. Such findings suggest that the TLARG has great potential to enhance the punctuality of mobility service by providing robust route guidance, even in the era of coexisting AVs and HVs.

A Microscopic Traffic Simulator for evaluation of dynamic traffic management systems

Navigation or route guidance systems are designed to provide drivers with real-time travel information and the associated recommended routes for their trips. Classical route choice models typically rely on utility theory to represent drivers' route choice behavior. Such choices, however, may not be optimal from both the individual and the system perspectives. This is simply due to the fact that drivers usually have imperfect knowledge about the time-varying traffic conditions. In this article, we explore and propose a new model-free deep reinforcement learning (DRL) approach to solving the adaptive route guidance problem based on microsimulation. The proposed approach consists of three interconnected algorithms, including a network edge labeling algorithm, a routing plan identification algorithm, and an adaptive route guidance algorithm. Simulation experiments on both a toy network and a real-world network of Suzhou, China, are performed to demonstrate the effectiveness of the proposed approach in terms of guiding a single vehicle as well as multiple vehicles through complex traffic environments. Comparative results confirm that the DRL approach outperforms the traditional shortest path method by further reducing the average travel time in the network.

Nonlinear state estimation is a pre-requisite for advanced process control and fault diagnosis tasks. In literature, various recursive nonlinear filtering techniques have been proposed and used for state estimation of nonlinear systems. Over the last few years, Moving Horizon Estimation (MHE) is increasingly being used for state estimation of nonlinear systems. Moving horizon estimation works with a window of data and hence requires additional online computation compared to recursive nonlinear filters. However, MHE performs both smoothing and filtering and thus has the potential to obtain more accurate state estimates as compared to the recursive filters. Most of the available comparisons of MHE with recursive filters are based on simulation case studies where the true states and parameters, as well as noise processes are exactly known. In this work, we apply MHE to a three-tank experimental setup and compare its performance with various nonlinear filters available in literature such as Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Gaussian Sum EKF (GSEKF). We present some of the challenges in experimental implementation of state estimation approaches. These include presence of unknown disturbances, non-whiteness of noise signals as well as lack of accurate measurements. We then discuss the approach followed for obtaining model parameters and noise characterization to make the models amenable for filter implementation. It is found that EKF, GSEKF and UKF perform as well as MHE as far as accuracy is concerned, but require significantly lower computational efforts.

State estimation is a key Energy Management System (EMS) function, responsible for estimating the best state of the power system. As the power system goes larger and complex in structure, state estimation plays a vital role in system control, which demands accurate estimate of the system state. This paper presents a bird's eye view on four methodologies namely weighted least squares, decoupled weighted least squares, regularized least squares and decoupled regularized least squares, based on our comprehensive survey of the available literature and the case studies.

With the widespread use of computers in modern assessment, online calibration has become increasingly popular as a way of replenishing an item pool. The present study discusses online calibration strategies for a joint model of responses and response times. The study proposes likelihood inference methods for item paramter estimation and evaluates their performance along with optimal sampling procedures. An extensive simulation study indicates that the proposed online calibration strategies perform well with relatively small samples (e.g., 500∼800 examinees). The analysis of estimated parameters suggests that response time information can be used to improve the recovery of the response model parameters. Among a number of sampling methods investigated, A-optimal sampling was found most advantageous when the item parameters were weakly correlated. When the parameters were strongly correlated, D-optimal sampling tended to achieve the most accurate parameter recovery. The study provides guidelines for deciding sampling design under a specific goal of online calibration given the characteristics of field-testing items.

Distribution system state estimation is essential for real-time monitoring and control of distribution systems. In recent years, an increasing number of Automated Meter Reading (AMR) systems have been installed to provide customer consumption information. The deployment of smart meters in low voltage (LV) distribution networks, within the context of Advanced Metering Infrastructure (AMI) systems, will enhance the electric energy consumption data storage, processing and analysis capabilities. Because of the limited number of real-time measurements in distribution systems, increased smart metering data availability from LV consumers is necessary to provide load estimates, which can be treated as pseudo-measurements to guarantee the network observability and estimate the system states. This paper presents two distinctive load estimation methods which utilize near real-time consumption data and short past consumption data, respectively. The accuracy of estimated loads is tested with a combined load and state estimation approach. Numerical results with a distribution system will show the effectiveness of the proposed algorithms.

Filter Design for System Modeling, State Estimation and Fault Diagnosis analyzes the latest methods in the design of filters for system modeling, state estimation and fault detection with the intention of providing a new perspective of both theoretical and practical aspects.This book also includes fault diagnosis techniques for unknown but bounded systems, their real applications on modeling and fault diagnosis for lithium battery systems, DC-DC converters and spring damping systems. It proposes new methods based on zonotopic Kalman filtering, a variety of state estimation methods of zonotope and its derived algorithms, a state estimation method based on convex space, set inversion interval observer filtering-based guaranteed fault estimation and a novel interval observer filtering-based fault diagnosis. The methods presented in this text are more practical than the common probabilistic-based algorithms, since these can be applied in unknown but bounded noisy environments. This book will be an essential read for students, scholars and engineering professionals who are interested in filter design, system modeling, state estimation, fault diagnosis and related fields.

We address the problem of crowd congestion at venues like theme parks, museums and world expos by providing route guidance to multiple selfish users (with budget constraints) moving through the venue simultaneously. To represent these settings, we introduce the Selfish Orienteering Problem (SeOP) that combines two well studied problems from literature, namely Orienteering Problem (OP) and Selfish Routing (SR). OP is a single agent routing problem where the goal is to minimize latency (or maximize reward) in traversing a subset of nodes while respecting budget constraints. SR is a game between selfish agents looking for minimum latency routes from source to destination along edges of a network available to all agents. Thus, SeOP is a multi-agent planning problem where agents have selfish interests and individual budget constraints. As with Selfish Routing, we employ Nash Equilibrium as the solution concept in solving SeOP. A direct mathematical program formulation to find a Nash equilibrium in SeOP cannot scale because the number of constraints is quadratic in the number of paths, which itself is an exponential quantity. To address scalability issues, we make two key contributions. First, we provide a compact non-pairwise formulation with linear number of constraints in the number of paths to enforce the equilibrium condition. Second, we introduce DIRECT, an incremental and iterative master-slave decomposition approach to compute an approximate equilibrium solution. Similar to existing flow based approaches, DIRECT is scale invariant in the number of agents. We also provide a theoretical discussion of our approximation quality and present extensive empirical results on synthetic and real-world graphs demonstrating the scalability of combining DIRECT with our non-pairwise formulation.

Personalized public transport mobility service: a journey ranking approach for route guidance