Author: Wisconsin. Department of Transportation. Division of PlanningPublisher:
ISBN:
Category : Motor vehicles
Languages : en
Pages : 34
Book Description
Vehicle Classification Survey Data Collection Methods and Analysis
Author: Wisconsin. Department of Transportation. Division of PlanningPublisher:
ISBN:
Category : Motor vehicles
Languages : en
Pages : 34
Book Description
Sampling Techniques for the Collection of Vehicle Classification Data
Author: Jerry G. PigmanPublisher:
ISBN:
Category : Motor vehicles
Languages : en
Pages : 42
Book Description
Vehicle Classification Sampling Methodology Evaluation
Author: Wisconsin. Department of Transportation. Division of Planning & BudgetPublisher:
ISBN:
Category : Traffic surveys
Languages : en
Pages : 52
Book Description
Validation of Urban Vehicle Classification Sampling Methodology
Author: Publisher:
ISBN:
Category : Sampling (Statistics)
Languages : en
Pages : 104
Book Description
The Mobility Analysis Section of the CDOT Division of Transportation Development (DTD) developed this study to determine whether the cluster count method developed by CDOT is statistically reliable for estimating vehicle classification on urban roadways with average daily traffic volumes exceeding 15,000 vehicles per day. Specifically, CDOT needed to assess whether or not the percentages of vehicles in the 13 FHWA vehicle classifications estimated by the cluster count method differ significantly from expected percentages obtained by 24-hour counts. Since vehicle classification is expensive to perform by manual observation over long periods of time, a statistically reliable method of estimating vehicle type percentages on urban roadways using a less time-consuming method is desirable. The study team utilized the chi-square statistical test to evaluate the similarity between vehicle classifications collected using the cluster count method and 24-hour vehicle counts collected using other data collection methods. Vehicle classification data were collected at 12 sites around Denver, Colorado that represented different roadway classes. The statistical tests between the data collected using the cluster count method and the 24-hour counts revealed that the current cluster count method varied beyond an acceptable statistical similarity to the 24-hour counts. Upon reaching this conclusion, the study panel simulated various changes to the short duration count methodology in an effort to identify the greatest improvement in statistical accuracy. As a result of this study, the recommended short duration vehicle classification methodology requires vehicle counts to be performed for 15 minutes every hour for a 24-hour period. This method exhibits strong statistical similarity to the 24-hour classification counts for all roadway classes and study sites included in this analysis. This collection method is statistically accurate, easy for field personnel to understand and collect, and is about onethird of the cost of a manual 24-hour count. The Mobility Analysis Section of DTD has developed a guidebook on the recommended short duration count methodology that will be available to CDOT staff, data collectors, consultants, and other public agencies. This guidebook outlines how to collect the short duration classification data, process and manage the data, and perform quality control checks.
Vehicle Classification Data User Demand
Author: Wisconsin. Department of Transportation. Division of PlanningPublisher:
ISBN:
Category : Choice of transportation
Languages : en
Pages : 38
Book Description
Traffic Data Collection and Analysis
Author: Alexander FrenchPublisher: Transportation Research Board National Research
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 68
Book Description
This synthesis will be of interest to traffic engineers, highway planners, and others concerned with the collection of traffic data for traffic engineering studies, for long-range planning, and for evaluation of traffic law enforcement. Information is presented on current practice in traffic data collection and analysis. Although types of highway traffic data collected over the past 50 years have not changed significantly, the quantities, analysis procedure, and presentations of these data have changed as a result of changing policies, operational concerns, and capabilities resulting from new technologies. This report of the Transportation Research Board describes the technology (both hardware and software) that is being used for traffic data collection, and discusses technological advances that have not yet been applied to the acquisition and presentation of traffic data.
Vehicle Classification Data Expansion
Author: Wisconsin. Department of Transportation. Division of Planning & BudgetPublisher:
ISBN:
Category : Motor vehicles
Languages : en
Pages : 50
Book Description
Data Systems and Travel Survey Methods 2010
Author: Publisher:
ISBN: 9780309142885
Category : Traffic monitoring
Languages : en
Pages : 168
Book Description
"The 17 papers in this issue are concerned with data systems and travel survey methods. Specific topics discussed include the following: evaluating highway safety hardware improvements using benefit-cost analysis; transportation asset management; road inventory data collection and integration; multipurpose location-based services; travel time estimation algorithms; travel data collection with Bluetooth sensors; traffic monitoring of motorcycles; nonintrusive sensors for vehicle classification; validating an automatic bottleneck detection tool; temporal data aggregation; axle load measurement errors; cluster analysis of traffic data; volume data correction for single-channel advance loop detectors; automated vehicle identification at weigh-in-motion inspection stations; and collecting local freight data."--pub. desc.
Length-based Vehicle Classification Using Dual-loop Data Under Congested Traffic Conditions
Author: Qingyi AiPublisher:
ISBN:
Category :
Languages : en
Pages : 93
Book Description
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.
Department of Transportation and Related Agencies Appropriations for 1999
Author: United States. Congress. House. Committee on Appropriations. Subcommittee on Department of Transportation and Related Agencies AppropriationsPublisher:
ISBN:
Category : United States
Languages : en
Pages : 2014
Book Description