The Experts below are selected from a list of 24915 Experts worldwide ranked by ideXlab platform
Giuseppe Guido - One of the best experts on this subject based on the ideXlab platform.
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motorway Traffic Parameter estimation from mobile phone counts
European Journal of Operational Research, 2006Co-Authors: Vittorio Astarita, Robert L Bertini, Sergio Delia, Giuseppe GuidoAbstract:Abstract In this paper a new method for real time estimation of vehicular flows and densities on motorways is proposed. This method is based on fusing Traffic counts with mobile phone counts. The procedure used for the estimation of Traffic flow Parameters is based on the hypothesis that “instrumented” vehicles can be counted on specific motorway sections and Traffic flow can be measured on entrance and exit ramps. The motorway is subdivided into cells, assuming that mobile phones entering and exiting every cell can be counted during the observation period. An estimate of “instrumented” vehicle concentration is obtained and propagated on the network in time and space. This allows one to estimate Traffic flow Parameters by sampling “instrumented” Traffic flow Parameters using a “concentration” (the ratio of the densities of instrumented vehicles to the density of overall Traffic) propagation mechanism.
Guangyi Shi - One of the best experts on this subject based on the ideXlab platform.
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a robust Traffic Parameter extraction method using texture and entropy
IEEE Intelligent Vehicles Symposium, 2009Co-Authors: Hang Shi, Yuexian Zou, Yiyan Wang, Guangyi ShiAbstract:This paper presents a robust Traffic Parameters extraction (RTPE) method for intelligent Traffic system. Firstly a texture-based algorithm is introduced to solve the moving shadow problem, which occurs in Traffic lane commonly. Secondly, we propose a robust exponential entropy-based and data-dependent threshold vehicle detection algorithm, named RVD-EXEN algorithm to extract vehicle's feature from raw visual information for vehicle detection. On this basis, we calculate some basic Traffic Parameters such as Traffic flow, time occupancy ratio and space mean speed. The experiments show that proposed RTPE method has the flexibility to shadow situation, robustness to noise and efficiency of computation.
Vittorio Astarita - One of the best experts on this subject based on the ideXlab platform.
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motorway Traffic Parameter estimation from mobile phone counts
European Journal of Operational Research, 2006Co-Authors: Vittorio Astarita, Robert L Bertini, Sergio Delia, Giuseppe GuidoAbstract:Abstract In this paper a new method for real time estimation of vehicular flows and densities on motorways is proposed. This method is based on fusing Traffic counts with mobile phone counts. The procedure used for the estimation of Traffic flow Parameters is based on the hypothesis that “instrumented” vehicles can be counted on specific motorway sections and Traffic flow can be measured on entrance and exit ramps. The motorway is subdivided into cells, assuming that mobile phones entering and exiting every cell can be counted during the observation period. An estimate of “instrumented” vehicle concentration is obtained and propagated on the network in time and space. This allows one to estimate Traffic flow Parameters by sampling “instrumented” Traffic flow Parameters using a “concentration” (the ratio of the densities of instrumented vehicles to the density of overall Traffic) propagation mechanism.
Gurcan Comert - One of the best experts on this subject based on the ideXlab platform.
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short term freeway Traffic Parameter prediction
Expert Systems With Applications, 2016Co-Authors: Anton Bezuglov, Gurcan ComertAbstract:Three Grey System theory models for short term Traffic speed prediction studied.The grey models demonstrated better accuracy than other tested nonlinear models.The Verhulst model with Fourier error correction demonstrates the best accuracy.The simpler derivations can allow the algorithms to be placed on portable devices.Well-defined mathematics of models can allow alteration for multidimensional data. Intelligent transportation systems applications require accurate and robust prediction of Traffic Parameters such as speed, travel time, and flow. However, Traffic exhibits sudden shifts due to various factors such as weather, accidents, driving characteristics, and demand surges, which adversely affect the performance of the prediction models. This paper studies possible applications and accuracy levels of three Grey System theory models for short-term Traffic speed and travel time predictions: first order single variable Grey model (GM(1,1)), GM(1,1) with Fourier error corrections (EFGM), and the Grey Verhulst model with Fourier error corrections (EFGVM). Grey models are tested on datasets from California and Virginia. They are compared to nonlinear time series models. Grey models are found to be simple, adaptive, able to deal better with abrupt Parameter changes, and not requiring many data points for prediction updates. Based on the sample data used, Grey models consistently demonstrate lower prediction errors over all the time series improving the accuracy on average about 50% in Root Mean Squared Errors and Mean Absolute Percent Errors.
Hang Shi - One of the best experts on this subject based on the ideXlab platform.
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a robust Traffic Parameter extraction method using texture and entropy
IEEE Intelligent Vehicles Symposium, 2009Co-Authors: Hang Shi, Yuexian Zou, Yiyan Wang, Guangyi ShiAbstract:This paper presents a robust Traffic Parameters extraction (RTPE) method for intelligent Traffic system. Firstly a texture-based algorithm is introduced to solve the moving shadow problem, which occurs in Traffic lane commonly. Secondly, we propose a robust exponential entropy-based and data-dependent threshold vehicle detection algorithm, named RVD-EXEN algorithm to extract vehicle's feature from raw visual information for vehicle detection. On this basis, we calculate some basic Traffic Parameters such as Traffic flow, time occupancy ratio and space mean speed. The experiments show that proposed RTPE method has the flexibility to shadow situation, robustness to noise and efficiency of computation.