A distributed optimum signal scheme for dynamic traffic control

Advanced Traffic Management Systems (ATMS) are those components of Intelligent Vehicle Highway Systems (IVHS) that integrate traffic detection, communication, and control functions to be responsive to dynamic traffic conditions and increase the efficiency of existing traffic networks. ATMS provide the management foundation that will enable and integrate other IVHS components such as Commercial Vehicle Operations, Advanced Traveler Information Systems, Advanced Vehicle Control Systems, and Advanced Public Transportation Systems. This paper defines Advanced Traffic Management Systems. It also describes the functions that may take place within an ATMS-class Traffic Management Center (TMC), a scenario that a future TMC operator may encounter, and some of the human factors issues that must be addressed in the design of an ATMS-class TMC.

Intelligent Vehicle-Highway Systems (IVHS) is a major U.S. Department of Transportation initiative to improve the safety and efficiency of our nation's highways. IVHS includes five related components: Advanced Traffic Management Systems (ATMS); Advanced Traveler Information Systems (ATIS); Commercial Vehicle Operations (CVO); Advanced Vehicle Control Systems (AVCS); and, Advanced Public Transportation Systems (APTS). Although the Federal Highway Administration has initially chosen to address each of these components separately, a number of issues are shared by all components. One critical common element deals with the capabilities of the humans in the system. Appropriate guidelines that consider the needs and capabilities of operators, maintainers, and users will be critical for efficient functioning of each system. Efforts are underway to define and resolve critical human factors issues related to IVHS components. This symposium addresses four of the five more highway related IVHS components. For each of these components, presenters will define the key engineering characteristics, hypothetical scenarios that focus on human-system interfaces, and examples of human factors issues that must be considered in the design of IVHS systems.

A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model

With the development of our modern cities, growing traffic problems adversely affect people’s traveling convenience more and more, which has become one of the most crucial factors considered in urban planning and design in recent years. Urban traffic congestion is a severe problem that significantly reduces the quality of life in particularly metropolitan areas. However, frequently constructing new roads is not realistic and untenable in social and economic aspects. In the effort to deal with this intractable problem, so-called intelligent transportation systems (ITS) technologies are successfully implemented widely throughout the world nowadays. ITS with two important components advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS) aim to relieve the increasing congestion and decrease travel time through providing information to the drivers by means of radio broadcasts or dynamic route guidance systems.

Distributed Computing and Simulation in a Traffic Research Test Bed

This paper develops disturbance rejection control schemes using a control separation based LQ design for output tracking of linear multivariable possibly nonminimum-phase systems with unmatched disturbances. A control separation based LQ control framework is constructed for rejection of unmatched disturbances and tracking of a reference signal. A three-component control signal is generated from such a LQ design with a new cost function, for desired system stability, disturbance rejection and output tracking. Using dynamic programming, finite-time and infinite-time control schemes are developed in details. A comparison with a traditional LQ control design is studied to show the advantages of the new control separation based LQ control method. Simulation results are presented to verify the effectiveness of the proposed LQ control based disturbance rejection method.

The primary focus of this study is to investigate the control strategies of a hybrid system used in hydraulic excavators. First, the structure and evaluation target of hybrid hydraulic excavators are analyzed. Then the dynamic system model including batteries, motor and engine is built as the simulation environment to obtain control results. A so-called multi-work-point dynamic control strategy, which has both closed-loop speed PI (proportion integral) control and direct torque control, is proposed and studied in the simulation model. Simulation results indicate that the hybrid system with this strategy can meet the power demand and achieve better system stability and higher fuel efficiency.

ABSTRACTPneumatic artificial muscles (PAMs) are a class of pneumatic drives that have received considerable attention for applications related to bio-inspired robotics. Nevertheless, servo control of PAMs is challenging due to the compressibility and nonlinear flow characteristics of air, hysteresis behaviour as well as uncertainties present. In this paper, positioning of an antagonistically paired PAM with mass flow rate of compressed air regulated by a 5/3-way proportional directional valve and driven by a dynamical adaptive backstepping-sliding mode control (DAB-SMC) scheme is investigated. Implemented for the first time on a PAM-driven actuating system, derivation of this model-based nonlinear control scheme is presented first followed by experimental evaluation. Positioning performance is studied using a sinusoidal trajectory with tracking frequencies of 0.05, 0.1, 0.2 and 0.5 Hz, and a multiple-step polynomial input having step sizes of 0.7°, 1.4°, 2.9° and 5.7°. Over various operating conditions, average root mean square error value of 0.16° and steady-state error value of 0.04° are achieved for position tracking and regulating, respectively. The adaptive LuGre friction observer embedded in the control scheme effectively compensates hysteresis behaviour of the PAMs and helps to improve the performance. The proposed DAB-SMC scheme outperforms the classical SMC scheme by 33% in accuracy. The control scheme has also demonstrated a robust performance towards the uncertainties including loading. In addition, a slight performance compromise in both tracking and regulating tasks was observed, when the 5/3-way proportional valve is replaced by cost-effective 2/2-way pulse width modulation controlled on–off valves.

There is a need for advanced traffic management systems (ATMS) on a local and network level to effectively utilize today`s transportation system. An automated operating system, with round the clock incident detection, would help to initiate the required action on time in case of an accident and would minimize the effect of the incident. One of the key elements of ATMS is the vehicle detection unit. The most commonly used inductive loop detectors have a relatively short life span and frequently malfunction. Any maintenance of loop detectors requires road closures. Also, safety of the crew is an issue. An overview of currently available technologies for traffic control and monitoring is presented, with special emphasis on systems that can be installed and maintained without significantly interfering with the traffic. The paper presents the available technologies, provides comparative information about them, and discusses the technical and conceptual problems typically arising during the deployment. The paper describes video detection systems that currently seem to be the most attractive alternative to the traditional intrusive methods. The new perspectives for traffic monitoring and management not available so far are drawn. The typical architecture of these systems is described, and a few popular video systems aremore » presented in some detail. The most critical deployment issues, such as the choice of the equipment, are described, the location of the sensors is discussed, and data transmission problems are considered.« less

Nowadays, traffic congestion is increasingly aggravating in almost every urban area and existing traffic light controllers cannot satisfy the rising demands efficiently to handle the traffic pressure. An intelligent transportation system (ITS) aims to provide major innovations for improving the performance of a public transport system including the traffic light control systems. In this paper, we propose a fog-based distributed architecture for a dynamic traffic light control system for ITS. In the proposed architecture, the local gateway collects real-time local traffic data using a wireless sensor network at each intersection. It also gets the neighboring traffic data from the distributed fog nodes. Using this local and global traffic data, the proposed Efficient Dynamic Traffic Light Control algorithm for Multiple (EDTLCM) intersections calculates the optimal green light sequence and duration (that maximizes the benefits), considering three objectives i) reducing the average waiting time ii) minimizing the fuel consumption and iii) maximizing the throughput. The simulation result shows that the proposed EDTLCM minimizes the waiting time, reduces fuel consumption and improves system throughput compared to the other dynamic strategies.

In this paper, a reinforcement learning (RL) based dynamic inverse attitude control scheme is proposed for near-space vehicle (NSV). Firstly, the conventional dynamic inverse control is employed to ensure the basic capability of NSV attitude tracking. Subsequently, RL is employed to tackle the system uncertainties. Actor-critic RL method is adopted to generate a compensation control signal in order to track attitude command better. Finally, simulation results illustrate that the proposed RL based dynamic inverse control scheme can obtain a better performance compared with the conventional dynamic inverse control scheme.

A secure effective key management scheme for dynamic access control in a large leaf class hierarchy

A novel on-line dynamic traffic control strategy for an urban expressway-arterial corridor system is proposed. The corridor system comprises an expressway mainline, on and off-ramps, and surface streets (arterial roads). The ramps are not subjected to any ramp metering control. Assuming that the real-time vehicle arrival and departure can be obtained from the loop detectors on the expressway, the proposed traffic control strategy aims to maintain a predetermined level of service on a congested expressway section by disseminating the congestion information to drivers through various advanced traveler information system means. This strategy consists of three components: expressway mainline control (EMC), on-ramp control (OnC), and off-ramp control (OffC) mechanisms. The EMC mechanism is the major component that diverts vehicles from the expressway to the surface streets, and the OnC and OffC procedures protect the ramps and surface streets from excessive flow. This is because diverting the vehicles without taking into consideration the surface street capacity may cause the overall performance of the corridor system to deteriorate. A simulation-based modeling methodology is developed to evaluate the on-line dynamic traffic control strategy. The evaluation is carried out by performing sensitivity analysis tests of the control parameters used in the strategy. A case study based on a corridor system in Singapore has shown that the traffic control strategy is effective.

Enormous advance has proven throughout the years in the area of traffic surveillance by the growth of intelligent traffic video surveillance system. In the current work, through the traffic videos, the traffic video surveillance automatically keyed out the vehicles like ambulance and trucks, which in turn assisted us in directing the vehicles at the time of emergency. Nevertheless, it doesn’t provide us a vital solution for the regulating the traffic. Moreover, this idea just identifies the vehicles, but it couldn’t notice the accidents expeditiously. Therefore in the proposed work, expeditious traffic video surveillance and monitoring system are presented along with dynamic traffic signal control and accident detection mechanism. Hybrid median filter has been utilized at the beginning for pre-processing of traffic videos, and to remove the noise. Hybrid support vector machine (SVM with extended Kalman filter) has been utilized to chase the vehicles. Next, the histogram of flow gradient features are drew-out to categories the vehicles. According to the traffic density and through video files, vehicles are computed, and then for emergency vehicles, the traffic signal gets switched dynamically. To realize the arrival of ambulances, the cameras have been set to catch traffic videos minimum at 500 m of the signal and deep learning neural networks has been employed. Hence dynamic signal control has been incorporated expeditiously. Likewise, multinomial logistic regression has been utilized in real-time live streaming videos, to identify the accidents correctly. The observational solution shows that the proposed intelligent traffic video surveillance system render expeditious dynamic control of traffic signals and it raises the identification of accidents correctly.

It is a rational assumption that travelers would not be completely in compliance with the guidance by advanced traveler information systems (ATIS). This paper presents a dynamic programming model for investigating the day-to-day receiving and adjusting process to the information released by ATIS. The dynamic model aims at seeking an optimal information release strategy for minimizing the overall disutility and a logit-based route choice is given on the base of adjusted travel times by travelers. The model is also an optimal control model and solved by solving a series of sequential programs. Numerical results are presented to show that there exists an optimal information release strategy that can drive the flow pattern to a system optimum.