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 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 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 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 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 Mining Vehicle Classification from Archived Loop Detector Data written by Bo Huang and published by . This book was released on 2014 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vehicle classification data are used in many transportation applications, including: pavement design, environmental impact studies, traffic control, and traffic safety. Ohio has over 200 permanent count stations, supplemented by many more short-term count locations. Due to the high costs involved, the density of monitoring stations is still very low given the lane miles that are covered. This study leveraged the deployed detectors in the Columbus Metropolitan Freeway Management System (CMFMS) to collect and analyze classification data from critical freeways where the Ohio Department of Transportation has not been able to collect much classification data in the past due to site limitations. The CMFMS was deployed in an unconventional manner because it included an extensive fiber optic network, frontloading most of the communications costs, and rather than aggregating the data in the field, the detector stations sent all of the individual per-vehicle actuations (i.e., PVR data) to the traffic management center (TMC). The PVR data include the turn-on and turn-off time for every actuation at each detector at the given station. Our group has collected and archived all of the PVR data from the CMFMS for roughly a decade. The PVR data allows this study to reprocess the original actuations retroactively. As described in this thesis, the research undertook extensive diagnostics and cleaning to extract the vehicle classification data from detectors originally deployed for traffic operations. The work yielded length based vehicle classification data from roughly 40 bi-directional miles of urban freeways in Columbus, Ohio over a continuous monitoring period of up to 10 years. The facilities span I-70, I-71, I-270, I-670, and SR-315, including the heavily congested inner-belt. Prior to this study, these facilities previously had either gone completely without vehicle classification or were only subject to infrequent, short-term counts.

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 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 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 In situ Vehicle Classification Using an ILD and a Magnetoresistive Sensor Array written by Stanley G. Burns and published by . This book was released on 2009 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report provides a summary of results from a multi-year study that includes both the use of inductive loop detectors (ILDs) and magnetoresistive sensors for in-situ vehicle classification. There were strengths and weaknesses noted in both type of sensor systems. Although the magnetoresistive array provides the best vehicle profile resolution, the standard inductive loop detector provides a significant cost, hardware and software complexity, and reliability advantage. The ILD installed base far exceeds the number of magnetoresistive sensors. Several electrical and computer engineering students participated in the study and their contributions are included in the individual chapter headings. Under my direction, these students also presented project work and Research Day conferences at MN/DOT District 1 Headquarters.

Download or read book Integrate RTMC Vehicle Classification Into the Current Detector Volume Data written by Taek Mu Kwon and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Collection of vehicle classification data is considered an essential part of traffic monitoring programs. The objective of this project is to integrate the raw classification data generated by the Minnesota Department of Transportation (MnDOT) Regional Transportation Management Center (RTMC) into the existing volume data managed by the Traffic Forecasting and Analysis (TFA) Section under the Office of Transportation System Management (OTSM). RTMC manages a large number of traffic sensors in the Twin Cities’ freeway network and continuously collects a huge amount of traffic data. Recently, it added Wavetronix radar sensors, from which length-based classification and speed data are generated in addition to typical volume and occupancy data generated by loop detectors. This project integrates this classification data into the existing TFA volume data, which could save cost and time for TFA in the future by using existing classification data. The project team also integrated the RTMC speed data for the locations where it was available. The final deliverable of this project was a software tool called detHealth_app, from which users can retrieve classification and speed data in addition to volume/occupancy data in multiple formats including Federal Highway Administration (FHWA) format. The detHealth_app program was thoroughly tested and has been successfully used by MnDOT TFA.

Download or read book Evaluation of Vehicle Classification Equipment written by and published by . This book was released on 1982 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Evaluation of Vehicle Classification Equipment written by Richard W. Lyles and published by . This book was released on 1982 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Global Trends in Information Systems and Software Applications written by P. Venkata Krishna and published by Springer. This book was released on 2012-08-01 with total page 842 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 2-Volume-Set, CCIS 0269-CCIS 0270, constitutes the refereed proceedings of the International Conference on Global Trends in Computing and Communication (CCIS 0269) and the International Conference on Global Trends in Information Systems and Software Applications (CCIS 0270), ObCom 2011, held in Vellore, India, in December 2011. The 173 full papers presented together with a keynote paper and invited papers were carefully reviewed and selected from 842 submissions. The conference addresses issues associated with computing, communication and information. Its aim is to increase exponentially the participants' awareness of the current and future direction in the domains and to create a platform between researchers, leading industry developers and end users to interrelate.

Download or read book Heavy Vehicle Classification Analysis Using Length based Vehicle Count and Speed Data written by Eren Yuksel and published by . This book was released on 2018 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is an increasing demand for application of Intelligent Transportation Systems (ITS) in order to make highways safer and sustainable. Collecting and analyzing traffic stream data are the most important parameters in transportation engineering in enhancing our understanding of traffic congestion and mobility. Classification of the vehicles using traffic data is one of the most essential parameters for traffic management. Of particular interest are heavy vehicles which impact traffic mobility due to their lack of maneuverability and slower speeds. The impact of heavy vehicles on the traffic stream results in congestion and reduction of road efficiency. In this paper, length-based vehicle count and speed data were analyzed and interpreted using one week's data from Interstate 5 (I-5) in the Portland, Oregon (OR) region of the United States (US). I-5 was chosen due to its prominent role in promoting North-South freight movement between Canada and Mexico and its vicinity to the Port of Portland. The objective of this analysis was to find better visualization techniques for the length-based traffic count and speed data. In total, 13,901,793 out of 56,146,138 20-second records were analyzed. The vehicles were classified into two categories. Those that were 20 feet or less were considered as passenger vehicles and those above 20 feet were considered as heavy vehicles. The data consisted of approximately 25% heavy vehicles. Results showed the merit of applying more disaggregate data (5-min polar, and radar plots) for better visualization as against hourly, and 15-min plots in order to capture sudden changes in average speed, heavy vehicle volume, and heavy vehicle percentage.