Estimation and prediction of road traffic flow using particle filter for real-time traffic control

Real-data testing results of a real-time state estimator and predictor are presented with particular focus on the feature of enabling of detector fault alarms and also its relation to queue-length based traffic control. A parameter and state estimator/predictor is developed by using particle filter. The simulation testing results are quite satisfactory and promising for further work on developing a hybrid model of traffic flow that captures the transition between low and high intensity. By using this hybrid model, it may be more feasible to achieve the significant feature of automatic adaptation to changing system condition

Parameter estimation for stochastic hybrid model applied to urban traffic flow estimation

This study proposes a novel data-based approach for estimating the parameters of a stochastic hybrid model describing the traffic flow in an urban traffic network with signalized intersections. The model represents the evolution of the traffic flow rate, measuring the number of vehicles passing a given location per time unit. This traffic flow rate is described using a mode-dependent first-order autoregressive (AR) stochastic process. The parameters of the AR process take different values depending on the mode of traffic operation – free flowing, congested or faulty – making this a hybrid stochastic process. Mode switching occurs according to a first-order Markov chain. This study proposes an expectation-maximization (EM) technique for estimating the transition matrix of this Markovian mode process and the parameters of the AR models for each mode. The technique is applied to actual traffic flow data from the city of Jakarta, Indonesia. The model thus obtained is validated by using the smoothed inference algorithms and an online particle filter. The authors also develop an EM parameter estimation that, in combination with a time-window shift technique, can be useful and practical for periodically updating the parameters of hybrid model leading to an adaptive traffic flow state estimator

Mind and Data Science

Mind and Data Scienc

Networks (of Everything)

Networks (of Everything

Perturbation analysis and sample-path optimization: stochastic flow models of urban traffic networks case

Coordination of traffic streams in an urban network, controllable by switching traffic lights, requires a global macroscopic model of the evolution of the flows of vehicle. We propose the use fluid petri nets as modeling tools. For the design of on-line controllers for traffic lights we study the network-wide effects of different local perturbations of the traffic light switching times via fast simulation. The infinitesimal perturbation analysis can under certain conditions lead to optimal closed loop performanc

Development of linear parameter varying control system for autonomous underwater vehicle

The development and application of Linear Parameter Varying (LPV) control system for robust longitudinal control system on an Autonomous Underwater Vehicle (AUV) are presented. The LPV system is represented as Linear Fractional Transformation (LFT) on its parameter set. The LPV control system combines LPV theory based upon Linear Matrix Inequalities (LMIs) and - synthesis to form a robust LPV control system. The LPV control design is applied for a pitch control of the AUV to fulfill control design criteria on frequency and time domain. The final closed-loop system is tested for robust stability throughout the operational envelope

Hybrid Petri net model of a traffic intersection in an urban network

Control in urban traffic networks constitutes an important and challenging research topic nowadays. In the literature, a lot of work can be found devoted to improving the performance of the traffic flow in such systems, by means of controlling the red-to-green switching times of traffic signals. Different techniques have been proposed and commercially implemented, ranging from heuristic methods to model-based optimization. However, given the complexity of the dynamics and the scale of urban traffic networks, there is still a lot of scope for improvement. In this work, a new hybrid model for the traffic behavior at an intersection is introduced. It captures important aspects of the flow dynamics in urban networks. It is shown how this model can be used in order to obtain control strategies that improve the flow of traffic at intersections, leading to the future possibility of controlling several connected intersections in a distributed way

Expectation-maximization based parameter identification for HMM of urban traffic flow

This paper concerns on modeling of traffic flow as a hybrid approach that combines continuous and discrete dynamics in a system. The model is chosen as simple as possible such as a Hidden Markov Model (HMM). Traffic flow can be classified into two-states and switching between two states controlled by first-order Markov chain with a certain probability. The model is characterized by several Gaussian parameters and estimated by using Expectation-Maximization (EM) technique. Actual traffic flow data on City of Jakarta and Bandung is used to model through EM estimation parameter and to validate the results by using particle filter. The results confirm that the proposed model gives satisfactory results which capture the variation of traffic flow. This work is easily extended to Jump Markov Model as a more general model especially relating to the development of traffic control design based upon queue length

Development of Torque and Drag Calculation Software for Oil Well Planning–Part 1: 2D Aadnoy Method

With the increasing number of drilled ultra-extended reach wells and complex geometry wells, the drilling limitation caused by excessive torque and drag forces must be further investigated. The wellbore friction being a main limiting factor in extended reach well needs to be studied with the new developed models. The torque and drag software implement two methods: (1) 2D and 3D analytical model developed by Aadnøy (Aadnoy & Andersen, 1998; Aadnoy & Andersen, 2001; Aadnoy & Djurhuus, 2008; Aadnoy, et al., 2010; Aadnoy, 2010) and (2) Miska and Mitchel, for 2D wellbore (Mitchell, et al., 2011). This paper presents the theory and implementation of 2D Aadnoy method. Quite diverse wellbore trajectory and depth has been chosen for a better evaluation and comparison of the model with the measured data. In order to investigate the potential and limitation of the model, torque and drag analysis during the different operations such as tripping in, tripping out, rotating off bottom, combined up/down were investigated