Traffic Measurement Data Research Articles

Optimal Heterogeneous Sensor Deployment Strategy for Dynamic Origin–Destination Demand Estimation

A network sensor location problem (NSLP) model is proposed to determine the optimal heterogeneous sensor deployment strategy in terms of the number of links (counting) and node (video or image) sensors and their installation locations for dynamic origin–destination (O-D) demand estimates under a budget constraint. The proposed NSLP model also captures the impact of the time duration for which traffic data measurements are available on the optimal sensor deployment strategy; this topic had not been addressed in previous studies. A sequential sensor location algorithm that avoids matrix inversions was proposed to solve the NSLP model under the assumption of multivariate normal distribution for the prior dynamic O-D demand estimates. A network from a part of Chennai, India, was used to illustrate the performance of the proposed NSLP model and solution algorithm. The results show that the proposed algorithm can identify the sensor locations on the basis of the amount of information provided and can determine the number of each sensor type to maximize the accuracy of O-D demand estimation. The results also illustrate that the proposed NSLP model can capture the time-varying characteristics of the amount of information provided by each sensor location. The proposed method can be used to analyze the impacts of various sensor types, their number, location, and the time duration for which traffic data measurements are available for estimation of dynamic O-D demand under limited resources.

A local least square support vector machine prediction algorithm of small scale network traffic based on correlation analysis

Real-time monitoring and forecasting technology for network traffic has played an important role in network management. Effective network traffic prediction could analyze and solve problems before overload occurs, which significantly improves network availability. In this paper, after the vulnerability of traditional nonlinear prediction method in forecasting modeling is analyzed, the relevant local (RL) forecast which is based on correlation analysis and the parameter optimization method based on pattern search (PS) is introduced. Using the correlation analysis, the optimal training subset is chosen from time-and distance-correlated training samples. On this basis, the prediction model is established by LSSVM. Finally network traffic dataset collected from wired campus networks is studied for our experiments. And the results show that the relevant local LSSVM prediction method whose training set and parameters have been automatically optimized can effectively predict the small scale traffic measurement data, and RL-LSSVM traffic forecasting algorithm exhibits significantly good prediction accuracy for the data set compared with previous algorithm.

Ensemble of Differential Equations using Pareto Optimal for Traffic Forecasting

The formal and empirical proof is that the ensemble of the learning models performs better than the single one. In order to construct the ensemble of the system of ordinary differential equations (ODEs), the two problems (diversity and accuracy of ODEs) are considered. In the paper, we estimate experimentally the model ensemble using multi-objective optimization. This paper presents a pareto optimal approach for identifying a family of the additive tree models which are used to reconstruct and identify the system of ordinary differential equations to predict the small-time scale traffic measurements data. We employ the tree-structure based evolution algorithm and particle swarm optimization (PSO) to evolve the architecture and the parameters of the additive tree model. The small-scale traffic measurements data is used to test ODE ensemble, and experimental results reveal that the proposed method is feasible and efficient for forecasting the time series. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.2878 Full Text: PDF

Ensemble of Flexible Neural Tree and Ordinary Differential Equations for Small-time Scale Network Traffic Prediction

Accurate models play important roles in capturing the salient characteristics of the network traffic, analyzing and simulating for the network dynamic, and improving the predictive ability for system dynamics. In this study, the ensemble of the flexible neural tree (FNT) and system models expressed by the ordinary differential equations (ODEs) is proposed to further improve the accuracy of time series forecasting. Firstly, the additive tree model is introduced to represent more precisely ODEs for the network dynamics. Secondly, the structures and parameters of FNT and the additive tree model are optimized based on the Genetic Programming (GP) and the Particle Swarm Optimization algorithm (PSO). Finally, the expected level of performance is verified by using the proposed method, which provides a reliable forecast model for small-time scale network traffic. Experimental results reveal that the proposed method is able to estimate the small-time scale network traffic measurement data with decent accuracy.

Parameter Estimation for Traffic Noise Models Using a Harmony Search Algorithm

A technique has been developed for predicting road traffic noise for environmental assessment, taking into account traffic volume as well as road surface conditions. The ASJ model (ASJ Prediction Model for Road Traffic Noise, 1999), which is based on the sound power level of the noise emitted by the interaction between the road surface and tires, employs regression models for two road surface types: dense-graded asphalt (DGA) and permeable asphalt (PA). However, these models are not applicable to other types of road surfaces. Accordingly, this paper introduces a parameter estimation procedure for ASJ-based noise prediction models, utilizing a harmony search (HS) algorithm. Traffic noise measurement data for four different vehicle types were used in the algorithm to determine the regression parameters for several road surface types. The parameters of the traffic noise prediction models were evaluated using another measurement set, and good agreement was observed between the predicted and measured sound power levels.

Nonlinear prediction of small scale network traffic based on local relevance vector machine regression model

Based on the nonlinear time series local prediction method and the relevance vector machine regression model, the local relevance vector machine prediction method is proposed and applied to predict the small scale traffic measurement data, and the BIC-based neighbor point selection method is used to choose the number of nearest-neighbor points for the local relevance vector machine regression model. We also compare the performance of the local relevance vector machine regression model with the feed-forward neural network optimized by particle swarm optimization for the same problem. Experimental results show that the local relevance vector machine prediction method whose neighboring points have been optimized can effectively predict the small scale traffic measurement data, can reproduce the statistical features of real small scale traffic measurements, and the prediction accuracy of the local relevance vector machine regression model is superior to that of the feedforward neural network optimized by PSO and the local linear prediction method.

Estimating health related costs and savings from balcony acoustic design for road traffic noise

A multi-faceted study is conducted with the objective of estimating the potential fiscal savings in annoyance and sleep disturbance related health costs due to providing improved building acoustic design standards. This study uses balcony acoustic treatments in response to road traffic noise as an example. The study area is the State of Queensland in Australia, where regional road traffic noise mapping data is used in conjunction with standard dose–response curves to estimate the population exposure levels. The background and the importance of using the selected road traffic noise indicators are discussed. In order to achieve the objective, correlations between the mapping indicator (LA10(18hour)) and the dose response curve indicators (Lden and Lnight) are established via analysis on a large database of road traffic noise measurement data. The existing noise exposure of the study area is used to estimate the fiscal reductions in health related costs through the application of simple estimations of costs per person per year per degree of annoyance or sleep disturbance. The results demonstrate that balcony acoustic treatments may provide a significant benefit towards reducing the health related costs of road traffic noise in a community.

Small-time scale network traffic prediction based on flexible neural tree

In this paper, the flexible neural tree (FNT) model is employed to predict the small-time scale traffic measurements data. Based on the pre-defined instruction/operator sets, the FNT model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using the Genetic Programming (GP) and the parameters are optimized by the Particle Swarm Optimization algorithm (PSO). The experimental results indicate that the proposed method is efficient for forecasting small-time scale traffic measurements and can reproduce the statistical features of real traffic measurements. We also compare the performance of the FNT model with the feed-forward neural network optimized by PSO for the same problem.

Active traffic management on road networks: a macroscopic approach

Active traffic management (ATM) is the ability to dynamically manage recurrent and non-recurrent congestion based on prevailing traffic conditions in order to maximize the effectiveness and efficiency of road networks. It is a continuous process of (i) obtaining and analysing traffic measurement data, (ii) operations planning, i.e. simulating various scenarios and control strategies, (iii) implementing the most promising control strategies in the field, and (iv) maintaining a real-time decision support system that filters current traffic measurements to predict the traffic state in the near future, and to suggest the best available control strategy for the predicted situation. ATM relies on a fast and trusted traffic simulator for the rapid quantitative assessment of a large number of control strategies for the road network under various scenarios, in a matter of minutes. The open-source macrosimulation tool Aurora ROAD NETWORK MODELER is a good candidate for this purpose. The paper describes the underlying dynamical traffic model and what it takes to prepare the model for simulation; covers the traffic performance measures and evaluation of scenarios as part of operations planning; introduces the framework within which the control strategies are modelled and evaluated; and presents the algorithm for real-time traffic state estimation and short-term prediction.

Time-series forecasting using a system of ordinary differential equations

This paper presents a hybrid evolutionary method for identifying a system of ordinary differential equations (ODEs) to predict the small-time scale traffic measurements data. We used the tree-structure based evolutionary algorithm to evolve the architecture and a particle swarm optimization (PSO) algorithm to fine tune the parameters of the additive tree models for the system of ordinary differential equations. We also illustrate some experimental comparisons with genetic programming, gene expression programming and a feedforward neural network optimized using PSO algorithm. Experimental results reveal that the proposed method is feasible and efficient for forecasting the small-scale traffic measurements data.

Small-time scale network traffic prediction based on a local support vector machine regression model

In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.

Network Administrator Assistance System Based on Fuzzy C-means Analysis

In this research we design a network administrator assistance system based on traffic measurement and fuzzy c-means (FCM) clustering analysis method. Network traffic measurement is an essential tool for monitoring and controlling communication network. It can provide valuable information about network traffic-load patterns and performances. The proposed system utilizes the FCM method to analyze users' network behaviors and traffic-load patterns based on traffic measurement data of IP network. Analysis results can be used as assistance for administrator to determine efficient controlling and configuring parameters of network management systems. The system is applied in Dali University campus network, and it is effective in practice.

Real-Time Freeway Traffic State Estimation Based on Extended Kalman Filter: A Case Study

This paper presents a case study of real-time traffic state estimation. The adopted general approach to the design of universal traffic state estimators for freeway stretches is based on stochastic macroscopic traffic flow modeling and extended Kalman filtering, which are outlined in the paper. The reported investigations were conducted by use of eight-hour traffic measurement data collected from a freeway stretch of 4.1 km close to Munich, Germany. Some key issues are carefully investigated, including the tracking capability of the designed traffic state estimator, significance of the online model parameter estimation, sensitivity of the estimator to the initial values of the estimated model parameters as well as to the related noise standard deviation values, and the capability of the estimator to handle biased flow measurements. The achieved results are quite satisfactory.

Travel Time Estimation with Correlation Analysis of Single Loop Detector Data

In this paper, the average travel time over a link is considered as a random variable following an identical probability distribution as the arrival process. A new estimation method of the average travel time uses a cross-correlation analysis of traffic flow measurement data. This method requires only traffic flow information, which is available from the measurements of single loop detectors upstream and downstream from one link. Different from the existing maximum cross-correlation analysis method, the proposed method considers average travel time as a random variable, with its mean value estimated from all significant cross-correlation coefficients rather than from only the maximum cross-correlation coefficient. Therefore, the inherent variability of average travel time among different vehicles can be considered. Moreover, different from the existing optimization method, the proposed method uses the statistical t-test of the significant cross-correlation coefficients to determine automatically and adaptively the fitting range of the probability density function of the average travel time. Thus, it avoids using the approximated car length factor and has no need to predetermine the range of the average travel time as required by the optimization method. Details of the average travel time estimation procedures are presented, and the effectiveness of the proposed method is demonstrated through both simulation study and a case study of real traffic data.

Travel Time Estimation with Correlation Analysis of Single Loop Detector Data

In this paper, the average travel time over a link is considered as a random variable following an identical probability distribution as the arrival process. A new estimation method of the average travel time uses a cross-correlation analysis of traffic flow measurement data. This method requires only traffic flow information, which is available from the measurements of single loop detectors upstream and downstream from one link. Different from the existing maximum cross-correlation analysis method, the proposed method considers average travel time as a random variable, with its mean value estimated from all significant cross-correlation coefficients rather than from only the maximum cross-correlation coefficient. Therefore, the inherent variability of average travel time among different vehicles can be considered. Moreover, different from the existing optimization method, the proposed method uses the statistical t-test of the significant cross-correlation coefficients to determine automatically and adapt...

A real-time freeway network traffic surveillance tool

This paper presents a real-time freeway network traffic surveillance tool RENAISSANCE. Based on a stochastic macroscopic freeway network traffic flow model and the extended Kalman filter, RENAISSANCE is fed with a limited amount of real-time traffic measurements to enable a number of freeway network traffic surveillance tasks, including traffic state estimation and prediction, travel time estimation and prediction, queue tail/head/length estimation and prediction (queue tracking), and incident alarm. The paper first introduces the stochastic macroscopic freeway network traffic flow model and a real-time traffic measurement model, upon which a complete dynamic system model for freeway network traffic is established, with a special attention to the handling of some important model parameters. The addressed traffic surveillance tasks are described along with the functional architecture of RENAISSANCE. A simulation test was conducted for the tool with respect to a hypothetical freeway network example, while the traffic state estimator of RENAISSANCE was also tested with real traffic measurement data collected from a Bavarian freeway.

Background to fatigue load models for Eurocode 1: Part 2 Traffic Loads

AbstractThe paper deals with background studies on definition and calibration of fatigue traffic load models for road bridges in Eurocode 1: Part 2 Traffic Loads on Bridges. Starting from real traffic data measurements, the derivation of the load models is illustrated step by step, emphasizing the peculiarities of the calibration methods adopted in prenormative research. The basic assumptions as well as the main features, the accuracy and the philosophy of each load model are discussed, and important questions pertaining to vehicle interactions, not fully clarified in the Eurocode, are tackled and solved in a general way, in the framework of queueing theory.

Real Data Screening: Problem Statement, Methods and Field Results

Abstract The scope of this paper is the presentation of a general approach to the data cleaning problem and its solution in the traffic domain. The developed algorithms can be applied to urban and freeway traffic data collection processes. More precisely, two main algorithms have been developed, calibrated, evaluated, and applied to check and correct motorway and urban traffic data measurements in the Ile de France network.