The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Lek Achenie - One of the best experts on this subject based on the ideXlab platform.
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data reconciliation based Traffic Count analysis system
Transportation Research Record, 1998Co-Authors: M Zhao, Norman W. Garrick, Lek AchenieAbstract:Traffic volume data, especially average annual daily Traffic (AADT), are important in transportation engineering. They are required in managing and maintaining existing facilities and in planning and designing new facilities. Many state highway agencies use the ramp Counting procedure described in FHWA's Traffic Monitoring Guide to estimate AADTs for freeways. The procedure involves Counting all entrance and exit ramps between two established mainline Counters (anchor points) and then reconciling the Count data to estimate mainline AADT. The reconciling of Count data includes three steps. First, AADTs for the ramps and the anchor points are estimated from the Count data. Then AADT for each unCounted mainline link is calculated by addition or subtraction of ramp AADT to or from mainline AADT, starting from one anchor point. Finally, adjustments of the AADT are performed to achieve a match at the second anchor point if necessary. The process can be time-consuming and labor-intensive if it is done manually. ...
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DATA RECONCILIATION BASED Traffic Count ANALYSIS SYSTEM: USER'S GUIDE
1997Co-Authors: M Zhao, Norman W. Garrick, Lek AchenieAbstract:The Traffic Count Analysis System (TCAS) is a software package written in the FORTRAN computer programming language. It is developed to analyze Traffic Count data for freeways, and is designed to perform the following functions: (1) Calculate average annual daily Traffic (AADT) values for ramps and certain mainline sections using hourly Traffic Count data; (2) Estimate AADTs for unCounted mainline sections; and (3) Adjust the ramp AADTs and the estimated mainline AADTs so that they are balanced at all intersections. The TCAS can also be used to update the station location data file. The station location data file contains the information required to calculate the AADTs and to form the incidence matrix (which is a numerical representation of the freeway network). Updating of the station location data file is required whenever changes occur in the configuration of the freeway. The minimum amount of input data required for the TCAS includes: (1) Hourly Traffic Counts; (2) Expansion factors; and (3) Station location data. This report is a user's guide for the TCAS. It is organized in the following chapters: (1) Introducing the Traffic Count Analysis System; (2) Preparing Input Data Files; (3) Using the Traffic Count Analysis System; (4) Displaying the Analysis Results; and (5) Updating the Station Location File.
Bingnan Jiang - One of the best experts on this subject based on the ideXlab platform.
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Vehicle Speed Prediction by Two-Level Data Driven Models in Vehicular Networks
IEEE Transactions on Intelligent Transportation Systems, 2017Co-Authors: Bingnan JiangAbstract:Vehicle speed prediction provides important information for many intelligent vehicular and transportation applications. Accurate on-road vehicle speed prediction is challenging, because an individual vehicle speed is affected by many factors, e.g., the Traffic condition, vehicle type, and driver's behavior, in either deterministic or stochastic way. This paper proposes a novel data-driven vehicle speed prediction method in the context of vehicular networks, in which the real-time Traffic information is accessible and utilized for vehicle speed prediction. It first predicts the average Traffic speeds of road segments by using neural network models based on historical Traffic data. Hidden Markov models (HMMs) are then utilized to present the statistical relationship between individual vehicle speeds and the Traffic speed. Prediction for individual vehicle speeds is realized by applying the forward-backward algorithm on HMMs. To evaluate the prediction performance, simulations are set up in the SUMO microscopic Traffic simulator with the application of a real Luxembourg motorway network and Traffic Count data. The vehicle speed prediction result shows that our proposed method outperforms other ones in terms of prediction accuracy.
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Traffic and vehicle speed prediction with neural network and Hidden Markov model in vehicular networks
2015 IEEE Intelligent Vehicles Symposium (IV), 2015Co-Authors: Bingnan JiangAbstract:Accurate on-road vehicle speed prediction is important for many intelligent vehicular and transportation applications. It is also challenging because the individual vehicle speed is affected by many factors, e.g., Traffic speed, vehicle type, and driver's behavior, in either deterministic or stochastic ways. This paper proposes a novel vehicle speed prediction method in the context of vehicular networks, where the real-time Traffic information is accessible. Traffic speeds of following road segments are first predicted by Neural Networks (NNs) based on historical Traffic data. Hidden Markov models (HMMs) are trained by the Baum-Welch algorithm with historical Traffic and vehicle data to present the statistical relationship between vehicle speed and Traffic speed. The forward-backward algorithm is applied on HMMs to extract vehicle's speed on each road segment along the driving route. Simulation is set up on the SUMO microscopic Traffic simulator with the application of a real Luxembourg highway network and Traffic Count data. The vehicle speed prediction result shows that our proposed method outperforms other ones in terms of prediction accuracy.
M Zhao - One of the best experts on this subject based on the ideXlab platform.
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data reconciliation based Traffic Count analysis system
Transportation Research Record, 1998Co-Authors: M Zhao, Norman W. Garrick, Lek AchenieAbstract:Traffic volume data, especially average annual daily Traffic (AADT), are important in transportation engineering. They are required in managing and maintaining existing facilities and in planning and designing new facilities. Many state highway agencies use the ramp Counting procedure described in FHWA's Traffic Monitoring Guide to estimate AADTs for freeways. The procedure involves Counting all entrance and exit ramps between two established mainline Counters (anchor points) and then reconciling the Count data to estimate mainline AADT. The reconciling of Count data includes three steps. First, AADTs for the ramps and the anchor points are estimated from the Count data. Then AADT for each unCounted mainline link is calculated by addition or subtraction of ramp AADT to or from mainline AADT, starting from one anchor point. Finally, adjustments of the AADT are performed to achieve a match at the second anchor point if necessary. The process can be time-consuming and labor-intensive if it is done manually. ...
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DATA RECONCILIATION BASED Traffic Count ANALYSIS SYSTEM: USER'S GUIDE
1997Co-Authors: M Zhao, Norman W. Garrick, Lek AchenieAbstract:The Traffic Count Analysis System (TCAS) is a software package written in the FORTRAN computer programming language. It is developed to analyze Traffic Count data for freeways, and is designed to perform the following functions: (1) Calculate average annual daily Traffic (AADT) values for ramps and certain mainline sections using hourly Traffic Count data; (2) Estimate AADTs for unCounted mainline sections; and (3) Adjust the ramp AADTs and the estimated mainline AADTs so that they are balanced at all intersections. The TCAS can also be used to update the station location data file. The station location data file contains the information required to calculate the AADTs and to form the incidence matrix (which is a numerical representation of the freeway network). Updating of the station location data file is required whenever changes occur in the configuration of the freeway. The minimum amount of input data required for the TCAS includes: (1) Hourly Traffic Counts; (2) Expansion factors; and (3) Station location data. This report is a user's guide for the TCAS. It is organized in the following chapters: (1) Introducing the Traffic Count Analysis System; (2) Preparing Input Data Files; (3) Using the Traffic Count Analysis System; (4) Displaying the Analysis Results; and (5) Updating the Station Location File.
Loukas Dimitriou - One of the best experts on this subject based on the ideXlab platform.
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fuzzy rule based system approach to combining Traffic Count forecasts
Transportation Research Record, 2010Co-Authors: Antony Stathopoulos, Matthew G Karlaftis, Loukas DimitriouAbstract:Current advances in artificial intelligence are providing new opportunities for utilizing the enormous amount of data available in contemporary urban road surveillance systems. Several approaches, methodologies, and techniques have been presented for analyzing and forecasting Traffic Counts because such information has been identified as vital for the deployment of advanced transportation management and information systems. In this paper, a meta-analysis framework is presented for improving forecasted information of Traffic Counts, based on an adaptive data processing scheme. In particular, a framework for combining Traffic Count forecasts within a Mamdani-type fuzzy adaptive optimal control scheme is presented and analyzed. The proposed methodology treats the uncertainty pertaining to such circumstances by augmenting qualitative information of future Traffic flow states (and values) with a knowledge base and a heuristic optimization routine that provides dynamic training capabilities, resulting in an eff...
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Fuzzy Rule-Based System Approach to Combining Traffic Count Forecasts
Transportation Research Record: Journal of the Transportation Research Board, 2010Co-Authors: Antony Stathopoulos, Matthew G Karlaftis, Loukas DimitriouAbstract:Current advances in artificial intelligence are providing new opportunities for utilizing the enormous amount of data available in contemporary urban road surveillance systems. Several approaches, methodologies, and techniques have been presented for analyzing and forecasting Traffic Counts because such information has been identified as vital for the deployment of advanced transportation management and information systems. In this paper, a meta-analysis framework is presented for improving forecasted information of Traffic Counts, based on an adaptive data processing scheme. In particular, a framework for combining Traffic Count forecasts within a Mamdani-type fuzzy adaptive optimal control scheme is presented and analyzed. The proposed methodology treats the uncertainty pertaining to such circumstances by augmenting qualitative information of future Traffic flow states (and values) with a knowledge base and a heuristic optimization routine that provides dynamic training capabilities, resulting in an efficient real-time forecasting mechanism. Results from the application of the proposed framework on data acquired from realistic signalized urban network data (of Athens, Greece) and for a diversity of locations exhibit its potential
Laurence R Rilett - One of the best experts on this subject based on the ideXlab platform.
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real time od estimation using automatic vehicle identification and Traffic Count data
Computer-aided Civil and Infrastructure Engineering, 2002Co-Authors: Michael P Dixon, Laurence R RilettAbstract:Origin-destination (OD) matrices are a key input to many advanced Traffic management operations. In this study, two constrained OD estimators, based on generalized least squares and Kalman filtering, were developed and tested in order to examine the possibility of estimating OD matrices in real-time. A one-at-a-time processing method was introduced to provide an efficient organized framework for incorporating observations from multiple data sources in real-time. The estimators were tested under different conditions based on the type of prior OD information available, the type of assignment available, and the type of link volume model used. The performance of the Kalman filter estimator was also compared to that of the generalized least squares estimator to provide insight regarding their performance characteristics relative to one another for given scenarios. Automatic vehicle identification (AVI) tag Counts were used so that observed and estimated OD parameters could be compared. AVI data was incorporated primarily in three ways: as prior observed OD information; the inclusion of a deterministic link volume component that makes use of OD data extracted from the latest time interval from which all trips have been completed; and through the use of link choice proportions estimated based on link travel time data. Results show that using prior observed OD data along with link Counts improves estimator accuracy relative to OD estimation based exclusively on link Counts. The findings also show that the incorporation of constraints creates estimators that are less sensitive to limitations such as deterministic modeling errors, unreliable OD data, and assignment error. Under these limitations, the constrained Kalman filter is more robust than constrained generalized least squares when incorporating prior OD information.
