The Experts below are selected from a list of 34539 Experts worldwide ranked by ideXlab platform
Mashrur Chowdhury - One of the best experts on this subject based on the ideXlab platform.
-
real time highway Traffic Condition assessment framework using vehicle infrastructure integration vii with artificial intelligence ai
IEEE Transactions on Intelligent Transportation Systems, 2009Co-Authors: Mashrur Chowdhury, Adel W Sadek, Mansoureh JeihaniAbstract:This paper presents a framework for real-time highway Traffic Condition assessment using vehicle kinetic information, which is likely to be made available from vehicle-infrastructure integration (VII) systems, in which vehicle and infrastructure agents communicate to improve mobility and safety. In the proposed VII framework, the vehicle onboard equipment and roadside units (RSUs) collaboratively work, supported by an artificial intelligence (AI) paradigm, to determine the occurrence and characteristics of an incident. Two AI paradigms are examined: 1) support vector machines (SVMs) and 2) artificial neural networks (ANNs). Each RSU then assesses the Traffic Condition based on the information from multiple vehicles traveling on its supervised highway segment. As a case study, this paper developed a model of the VII-SVM framework and evaluated its performance in a microscopic Traffic simulation environment for a highway network in Spartanburg, SC. The performance of the VII-SVM was compared with the performance of the corresponding VII-ANN framework, and both frameworks were found to be capable of classifying the travel experience using the kinetic data generated by each vehicle. The performance of the VII-SVM framework, in terms of its detection rate, false-alarm rate, and detection times, was also found to be superior to a baseline California-type incident-detection algorithm. Moreover, the framework provided additional information, including an estimate of the incident location and the likely number of lanes blocked, which will be helpful for implementing an appropriate response strategy. The proposed VII-AI framework thus provides a reliable alternative to traditional Traffic sensors in assessing Traffic Conditions.
-
A real-time Traffic Condition assessment and prediction framework using vehicle-infrastructure integration (vii) with computational intelligence
2008Co-Authors: Mashrur ChowdhuryAbstract:This research developed a real-time Traffic Condition assessment and prediction framework using Vehicle-Infrastructure Integration (VII) with computational intelligence to improve the existing Traffic surveillance system. Due to the prohibited expenses and complexity involved for the field experiment of such a system, this study adopted state-of-the-art simulation tools as an efficient alternative. This work developed an integrated Traffic and communication simulation platform to facilitate the design and evaluation of a wide range of online Traffic surveillance and management system in both Traffic and communication domain. Using the integrated simulator, the author evaluated the performance of different combination of communication medium and architecture. This evaluation led to the development of a hybrid VII framework exemplified by hierarchical architecture, which is expected to eliminate single point failures, enhance scalability and easy integration of control functions for Traffic Condition assessment and prediction. In the proposed VII framework, the vehicle on-board equipments and roadside units (RSUs) work collaboratively, based on an intelligent paradigm known as “Support Vector Machine (SVM),” to determine the occurrence and characteristics of an incident with the kinetics data generated by vehicles. In addition to incident detection, this research also integrated the computational intelligence paradigm called “Support Vector Regression (SVR)” within the hybrid VII framework for improving the travel time prediction capabilities, and supporting on-line leaning functions to improve its performance over time. Two simulation models that fully implemented the functionalities of real-time Traffic surveillance were developed on calibrated and validated simulation network for study sites in Greenville and Spartanburg, South Carolina. The simulation models' encouraging performance on Traffic Condition assessment and prediction justifies further research on field experiment of such a system to address various research issues in the areas covered by this work, such as availability and accuracy of vehicle kinetic and maneuver data, reliability of wireless communication, maintenance of RSUs and wireless repeaters. The impact of this research will provide a reliable alternative to traditional Traffic sensors to assess and predict the Condition of the transportation system. The integrated simulation methodology and open source software will provide a tool for design and evaluation of any real-time Traffic surveillance and management systems. Additionally, the developed VII simulation models will be made available for use by future researchers and designers of other similar VII systems. Future implementation of the research in the private and public sector will result in new VII related equipment in vehicles, greater control of Traffic loading, faster incident detection, improved safety, mitigated congestion, and reduced emissions and fuel consumption.
Mansoureh Jeihani - One of the best experts on this subject based on the ideXlab platform.
-
real time highway Traffic Condition assessment framework using vehicle infrastructure integration vii with artificial intelligence ai
IEEE Transactions on Intelligent Transportation Systems, 2009Co-Authors: Mashrur Chowdhury, Adel W Sadek, Mansoureh JeihaniAbstract:This paper presents a framework for real-time highway Traffic Condition assessment using vehicle kinetic information, which is likely to be made available from vehicle-infrastructure integration (VII) systems, in which vehicle and infrastructure agents communicate to improve mobility and safety. In the proposed VII framework, the vehicle onboard equipment and roadside units (RSUs) collaboratively work, supported by an artificial intelligence (AI) paradigm, to determine the occurrence and characteristics of an incident. Two AI paradigms are examined: 1) support vector machines (SVMs) and 2) artificial neural networks (ANNs). Each RSU then assesses the Traffic Condition based on the information from multiple vehicles traveling on its supervised highway segment. As a case study, this paper developed a model of the VII-SVM framework and evaluated its performance in a microscopic Traffic simulation environment for a highway network in Spartanburg, SC. The performance of the VII-SVM was compared with the performance of the corresponding VII-ANN framework, and both frameworks were found to be capable of classifying the travel experience using the kinetic data generated by each vehicle. The performance of the VII-SVM framework, in terms of its detection rate, false-alarm rate, and detection times, was also found to be superior to a baseline California-type incident-detection algorithm. Moreover, the framework provided additional information, including an estimate of the incident location and the likely number of lanes blocked, which will be helpful for implementing an appropriate response strategy. The proposed VII-AI framework thus provides a reliable alternative to traditional Traffic sensors in assessing Traffic Conditions.
Guiyan Jiang - One of the best experts on this subject based on the ideXlab platform.
-
FCM Algorithm for Identifying Urban Road Traffic Condition with Loop Sensor Data
2007 International Conference on Mechatronics and Automation, 2007Co-Authors: Haifeng Guo, Guiyan JiangAbstract:An improved fuzzy c-means algorithm (FCM) for detecting urban road Traffic Conditions based on loop sensor data is presented. Two characteristic indices, occupancy rate and average occupancy rate per vehicle, are extracted from sensor data and FCM algorithm is designed to identify the Traffic Condition. In addition, the principle of determining the fuzziness index for valid cluster is presented in this paper. Taking one single intersection for instance, the presented algorithm is demonstrated by combining an external program with VISSIM emulation software. Results show that the algorithm can improve the identified effects of the urban road Traffic Conditions in real-time, the identified result is better when the detecting interval is synchronized with the signal cycle time.
Bindiya N. Patel - One of the best experts on this subject based on the ideXlab platform.
-
Estimation of Dynamic Equivalency Factor Under Heterogeneous Traffic Condition on Urban Arterial Road—A Case Study of Porbandar City
Transportation Research, 2019Co-Authors: Yash R. Dasani, Monicaba Vala, Bindiya N. PatelAbstract:This paper presents the concept of “Dynamic Equivalency Factor” (DEF) for the urban arterial roads under heterogeneous Traffic Condition, and it reflects that the PCU (Passenger Car Unit) is not a static factor as assumed. The parameters considered for the estimation of DEF are (1) average speed of the vehicle, (2) Traffic composition, (3) time headway, and (4) roadway width. The Traffic data was collected from three urban roads of Porbandar City, and it was collected at the mid-block section as of the following roads: (1) M.G Road, (2) S.V.P Road and (3) Chhaya Road. The mid-block section was kept of 30 M and Traffic data was collected. The following road selected varies in road widths as two lanes divided and two lanes undivided and having different Traffic composition. The sections were such selected that it was free from parked vehicles, bus stop, effects of the intersection, curvatures, etc. The DEF was obtained by the following methods (1) multiple regression method, (2) headway method, (3) Chandra’s method and (4) homogenization coefficient method. The Traffic data was collected using videography technique for morning 8 a.m.–8 p.m. from which the morning peak hours 8:30–11:30 and evening peak hours 5–8 was concluded for the roads by the study of Traffic volume. The peak hour Traffic was used to calculate the DEF values and efforts were made to suggest the best realizable DEF value.
Fan Zhang - One of the best experts on this subject based on the ideXlab platform.
-
Traffic Condition matrix estimation via weighted spatio temporal compressive sensing for unevenly distributed and unreliable gps data
International Conference on Intelligent Transportation Systems, 2014Co-Authors: Chen Tian, Fan ZhangAbstract:Traffic Condition monitoring is important to nowadays metropolitan. A recent trend is to exploit the prevalence of Global Positioning System (GPS) embedded in public vehicles. The collected data forms a two dimensional Traffic Condition matrix (TCM), i.e., time slot and road segment. The problem is that the TCM directly obtained from the probed data is incomplete. Traffic estimation can complete the TCM by filling the missing entries. The authors find that in practice it is challenging to reliably estimate a TCM. First, the distribution of probed data is uneven among road segments. Second, most entries of probed data are unreliable since they are the average of only a few reports. The authors approach is Weighted Spatio-Temporal Compressive Sensing. Demonstrated by extensive large scale computational experiments, the estimation error of the authors approach reduces to just half of the baseline approach.
-
ITSC - Traffic Condition Matrix Estimation via Weighted Spatio-Temporal Compressive Sensing for Unevenly-distributed and Unreliable GPS Data
17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2014Co-Authors: Chen Tian, Fan ZhangAbstract:Traffic Condition monitoring is important to nowadays metropolitan. A recent trend is to exploit the prevalence of Global Positioning System (GPS) embedded in public vehicles. The collected data forms a two dimensional Traffic Condition matrix (TCM), i.e., time slot and road segment. The problem is that the TCM directly obtained from the probed data is incomplete. Traffic estimation can complete the TCM by filling the missing entries. The authors find that in practice it is challenging to reliably estimate a TCM. First, the distribution of probed data is uneven among road segments. Second, most entries of probed data are unreliable since they are the average of only a few reports. The authors approach is Weighted Spatio-Temporal Compressive Sensing. Demonstrated by extensive large scale computational experiments, the estimation error of the authors approach reduces to just half of the baseline approach.
