Download or read book Improved Vehicle Length Measurement and Classification from Freeway Dual loop Detectors in Congested Traffic written by Lan Wu and published by . This book was released on 2014 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: Classified vehicle counts are a critical measure for forecasting the health of the roadway infrastructure and for planning future improvements to the transportation network. Balancing the cost of data collection with the fidelity of the measurements, length-based vehicle classification is one of the most common techniques used to collect classified vehicle counts. Typically the length-based vehicle classification process uses a pair of detectors to measure effective vehicle length. The calculation is simple and seems well defined. In particular, most conventional calculations assume that acceleration can be ignored. Unfortunately, at low speeds this assumption is invalid and performance degrades in congestion. As a result of this fact, many operating agencies are reluctant to deploy classification stations on roadways where traffic is frequently congested.

Download or read book Length based Vehicle Classification Using Dual loop Data Under Congested Traffic Conditions written by Qingyi Ai and published by . This book was released on 2013 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: The accurate measurement of vehicle classification is a highly valued factor in traffic operation and management, validations of travel demand models, freight studies, and even emission impact analysis of traffic operation. Inductive loops are increasingly used specifically for traffic monitoring at highway traffic data collection sites. Many studies have proven that the vehicle speed can be estimated accurately by using dual-loop data under free traffic condition, and then vehicle lengths can be estimated accurately. The capability of measuring vehicle lengths makes dual-loop detectors a potential real-time data source for vehicle classification. However, the existing dual-loop length-based vehicle classification model was developed with an assumption that the difference of a vehicle's speed on the first and the second single loop is not significant. Under congested traffic flows, vehicles' speeds change frequently and even fiercely, and the assumption cannot be met any more. The outputs of the existing models have a high error rate under non-free traffic conditions (such as synchronized and stop-and-go congestion states). The errors may be contributed by the complex characteristics of traffic flows under congestion; but quantification of such contributing factors remains unclear. In this study, the dual-loop data and vehicle classification models were evaluated with concurred video ground-truth data. The mechanism of the length-based vehicle classification and relevant traffic flow characteristics were tried to be revealed. In order to obtain the ground-truth vehicle event data, the software VEVID (Vehicle Video-Capture Data Collector) was used to extract high-resolution vehicle trajectory data from the videotapes. This vehicle trajectory data was used to identify the errors and reasons of the vehicle classifications resulted from the existing dual-loop model. Meanwhile, a probe vehicle equipped with a Global Positioning System (GPS) data logger was used to set up reference points for VEVID and to collect traffic profile data under varied traffic flow states for developing the new model under stop-and-go traffic flow. The research has proven inability of the existing vehicle classification model in producing satisfactory estimates of vehicle lengths under congestion, i.e., synchronized or stop-and-go traffic states. The Vehicle Classification under Synchronized Traffic Model (VC-Sync model) was developed to estimate vehicle lengths against the synchronized traffic flow and the Vehicle Classification under Stop-and-Go Model (VC-Stog model) was developed to estimate vehicle lengths against the stop-and-go traffic flow. Compare to the existing models, under the congested traffic flows, the newly developed models have improved the accuracy of vehicle length estimation significantly. The contribution of this research is reflected in the following aspects: 1) An innovative VEVID-based approach is developed for evaluating the concurred dual-loop data and resulted vehicle classification and relevant traffic flow characteristics against video-based ground-truth vehicle event trajectory data, which is difficult to conduct with traditional approaches; 2) Innovative vehicle classification models for both synchronized traffic and stop-and-go traffic states are developed through such an evaluation process; 3) The algorithms for processing the dual-loop vehicle event raw data have been improved by considering the influence of traffic flow characteristics;. 4) A GPS-based approach is developed for setting up the reference points in field in conjunction with application of VEVID, which is proven a safety and efficient approach compared to traditional manual approaches. And the GPS-based travel profile data is greatly helpful in developing the new models.

Download or read book Vehicle Classification Under Congestion Using Dual Loop Data written by Sudhir Reddy Itekyala and published by . This book was released on 2010 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: The growing congestion problem on Interstates has been identified as a serious problem for accurate data collection from automatic sensors like Inductive loop detectors (ILD). Traffic speed and vehicle classification data are typically collected by dual-loop detectors on freeways. During congestion, measurement of vehicle lengths which is based on detector ON and OFF timestamps (raw loop event data) often lead to misclassification of vehicle data. Accurate detection of raw event data and modified classification algorithm are increasingly important for higher data accuracy needs for agencies such as Advanced Traffic Management Systems (ATMS) and Advanced Traffic Information Systems (ATIS). Vehicle classification algorithm works on the assumption of constant vehicle speed in the detection area. This assumption is violated during congestion which induces errors in to vehicle length estimates leading to more inaccurate vehicle classification data. This paper unlike in preceding works presents a model which is simple enough to be implemented using existing loop detector hardware. This new model assumes vehicle travels with constant acceleration over loop detection area and thus named as --Constant Acceleration based Vehicle Classification model (CAVC)". This model first identifies traffic flow state and later uses Kinematic equations for estimating vehicle length values. Data is collected by videotaping dual loop station and also simultaneously collecting raw loop event data. Ground truth vehicle data is then extracted using Vehicle Video-Capture Data Collector (VEVID) [Wei et al. 2005] from video data. This improved model (CAVC model) is then validated using ground truth classification data and also compared with the results from existing vehicle classification model for different traffic flow states (under specific scenarios).

Download or read book Length Based Vehicle Classification on Freeways from Single Loop Detectors written by Benjamin André Coifman and published by . This book was released on 2009 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Vehicle Classification from Single Loop Detectors written by Benjamin André Coifman and published by . This book was released on 2007 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Length Based Vehicle Classification from Single Loop Detector Data written by Seoungbum Kim and published by . This book was released on 2008 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Over the years many vehicle classification schemes have been developed to sort passing vehicles into several classes according to their length, number of axles, axle spacing, number of units or some other combination of vehicle features. Vehicle classification is important for infrastructure management, traffic modeling, and quantifying emissions along highways. Weigh-in-motion (WIM), axle counting, and length from dual loop detectors are commonly used for vehicle classification on freeways.

Download or read book Improved Dual loop Detection System for Collecting Real time Truck Data written by Nancy L. Nihan and published by . This book was released on 2005 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Evaluation of the Performance of Loop Detectors and Freeway Performance Measurement from Loop Detectors written by Ho Lee and published by . This book was released on 2007 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: A Freeway Management System (FMS) acquires data from the roadway and processes these data to identify and respond to problems, notifying operators and motorists of those problems. If some aspects of the data collection are unreliable, then the response decisions and the information given may well be faulty. Hence, accurate traffic data acquisition is essential for effective traffic surveillance and subsequent management applications. Loop detectors, the most commonly used vehicle detectors for freeway traffic surveillance, are not always calibrated correctly, so it is necessary to identify potentially inaccurate detectors. This thesis presents an evaluation of the performance of the loop detectors on 1-71 in Columbus, Ohio. The evaluation includes the percentage of vehicles actuating only one loop in a dual loop detector, and detector mapping error tracked by the relationship of speed and occupancy from a dual loop detector. In addition, loop correction factors for both single and dual loop detectors are calculated to improve the accuracy of speed estimates and measurements. The analysis employs both statistical trends gathered from the detectors and concurrent velocities collected from probe vehicles as they pass over the detectors. As shown herein, loop detector's sensitivity can change over time which impacts speed and occupancy from that loop. So, the trend of daily median speed for off-peak time periods is used to determine the change in sensitivity of loop detectors over long time periods. This trend is then used to illustrate the fact that the correction factors can abruptly change. In the course of this work performance measurements of the freeway using the corrected loop detector data are developed, namely average daily traffic and delay. The weekday median for both of the daily measures is calculated at each week to track trends over years. The weekly average daily traffic and delay present an overview of the freeway system usage and performance. Also, summary plots and summary difference plots are developed to show how traffic condition evolves over time and space for identifying traffic condition and recurring congestion. Although presented in the context of the Columbus system, the tools should be generalizable to most freeway surveillance systems.

Download or read book Evaluation of Dual loop Data Accuracy Using Video Ground Truth Data written by Nancy L. Nihan and published by . This book was released on 2002 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Improving Dual loop Truck and Speed Data written by Nancy L. Nihan and published by . This book was released on 2006 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Loop and Length based Vehicle Classification written by Erik D. Minge and published by . This book was released on 2012 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: While most vehicle classification currently conducted in the United States is axle-based, some applications could be supplemented or replaced by length-based data. Common length-based methods are more widespread and can be less expensive, including loop detectors and several types of non-loop sensors (both sidefire and in-road sensors). Loop detectors are the most frequently deployed detection system and most dual-loop installations have the capability of reporting vehicle lengths. This report analyzes various length-based vehicle classification schemes using geographically diverse data sets. This report also conducted field and laboratory tests of loop and non-loop sensors for their performance in determining vehicle length and vehicle speed. The study recommends a four bin length scheme with a fifth bin to be considered in areas with significant numbers of long combination vehicles. The field and laboratory testing found that across a variety of detection technologies, the sensors generally reported comparable length and speed data.

Download or read book Deliver a Set of Tools for Resolving Bad Inductive Loops and Correcting Bad Data written by Xiao-Yun Lu and published by . This book was released on 2012 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project prototyped and demonstrated procedures to find and mitigate loop detector errors, and to derive more valuable data from loops. Specifically, methods were developed to find and isolate out loop data which is "bad" or invalid, so that mitigation means, or "fixes" can be implemented. Methods of extracting very accurate speed (+/- 3mph) and vehicle length data (+/- 1meter) from single loop stations were demonstrated to be much more accurate than current Caltrans practice. The validity of these methods were statistically proven using hundreds of thousands of vehicles. Additionally, more accurate and reliable methods of detecting the onset of both recurring or "incident" based congestion were demonstrated. These methods require access to the unprocessed loop detector card data. This unprocessed data can be acquired from the Log170 program, third party loop readers like the Infotek Wizard, or DRI's ubiquitous "C1 reader". DRI intends to implement many of these methodologies in the C1 reader client software, Videosync.

Download or read book Improving Truck and Speed Data Using Paired Video and Single loop Sensors written by and published by . This book was released on 2006 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Automatic Speed and Vehicle Class Detection for Intelligent Transportation Systems written by Neha Sharma and published by . This book was released on 2012 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traffic congestion is one of the most prevalent transport problems in large cities like Auckland. Building new roadways is often considered the most effective way to mitigate traffic congestion. However the most efficient and cost effective way to combat congestion is the use of Intelligent Transportation System (ITS) applications. Intelligent Transportation Systems (ITS) are advanced applications which integrate information and technology with the available transport infrastructure to provide a better, safe and efficient transportation network [10]. Sydney Coordinated Adaptive Traffic Systems (SCATS) is an ITS application deployed across New Zealand. It detects real time traffic data to dynamically change traffic signal timing to make best use of the road infrastructure. SCATS Ramp Metering System (SRMS) is another traffic management tool that controls motorway traffic during congestion. These ITS applications require real time data from their employed vehicle detector to function. Inductive loop detectors (ILD) are employed by SCATS to gather traffic data. There are more than 8000 inductive loops placed on SCATS controlled intersection in Auckland and over 4000 dual inductive loops on Auckland motorway. This thesis proposes a speed detection algorithm that uses these already deployed SCATS inductive loop detectors to measure vehicle speed. A vehicle classification algorithm is also presented that can distinguish between three vehicle classes. Speed estimation plays a crucial role in traffic management as it is an important indicator of traffic condition. The speed estimation algorithm presented can predict vehicle speed from a SCATS inductive loop detector with an accuracy of ±5.89 km/hr. Unlike other speed algorithms currently being used across New Zealand (NZ), the proposed speed model does not work on any assumptions and remains accurate for all traffic conditions. Widely deployed dual inductive loops across NZ are currently used to classify vehicles into four categories by measuring vehicle length. The proposed classification algorithm works on one inductive loop detector to produce a recognition rate of 100%. The algorithm can accurately predict vehicle class of a passenger car, a van and a sports-utility vehicle (SUV).

Download or read book Increasing Accuracy of Vehicle Speed Measurement in Congested Traffic Over Dual loop Sensors written by Benjamin André Coifman and published by . This book was released on 2014 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Refining Inductive Loop Signature Technology for Statewide Vehicle Classification Counts written by Chen-Fu Liao and published by . This book was released on 2021 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transportation agencies in the U.S. use devices such as loop detectors, automatic traffic recorders (ATR), or weigh-in- motion (WIM) sensors to monitor the performance of traffic network for planning, forecasting, and traffic operations. With a limited number of ATR and WIM sensors deployed throughout the state roadways, temporary double tubes are often deployed to get axle-based vehicle classification counts. An inductive loop signature technology previously developed by a Small Business Innovation Research (SBIR) program sponsored by the US Department of Transportation is used to classify vehicles using existing loops. This technology has the potential to save time and money while providing the state, counties or cities more data especially in the metro area where loop detectors have already been installed. This research leveraged the outcomes from previous development to validate the classification accuracy with video data. A loop signature system was initially installed at a traffic station in Jordan, MN, to evaluate its performance. The system was later moved to another location on US-52 near Coates, MN, to validate its classification accuracy with more heavy- vehicle traffic. Individual vehicle records were manually verified and validated with ground-truth video data using both the 13 and 7-bin classification schemes from the Federal Highway Administration (FHWA) and the Highway Performance Monitoring System (HPMS). The combined results from both test sites indicated that the loop signature technology had an overall classification accuracy of 93% and 96% using the FHWA and HPMS schemes, respectively. The classification performance can be further improved by including additional vehicle signatures from the state to the classification library.

Download or read book Computational Science and Its Applications ICCSA 2005 written by Osvaldo Gervasi and published by Springer. This book was released on 2005-05-02 with total page 1380 pages. Available in PDF, EPUB and Kindle. Book excerpt: The four volume set assembled following The 2005 International Conference on Computational Science and its Applications, ICCSA 2005, held in Suntec International Convention and Exhibition Centre, Singapore, from 9 May 2005 till 12 May 2005, represents the ?ne collection of 540 refereed papers selected from nearly 2,700 submissions. Computational Science has ?rmly established itself as a vital part of many scienti?c investigations, a?ecting researchers and practitioners in areas ranging from applications such as aerospace and automotive, to emerging technologies such as bioinformatics and nanotechnologies, to core disciplines such as ma- ematics, physics, and chemistry. Due to the shear size of many challenges in computational science, the use of supercomputing, parallel processing, and - phisticated algorithms is inevitable and becomes a part of fundamental t- oretical research as well as endeavors in emerging ?elds. Together, these far reaching scienti?c areas contribute to shape this Conference in the realms of state-of-the-art computational science research and applications, encompassing the facilitating theoretical foundations and the innovative applications of such results in other areas.