Validation of Urban Vehicle Classification Sampling Methodology

Validation of Urban Vehicle Classification Sampling Methodology PDF

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Published: 2005

Total Pages: 104

ISBN-13:

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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.

Using the Traffic Monitoring Guide to Develop a Truck Weight Sampling Procedure for Use in Virginia

Using the Traffic Monitoring Guide to Develop a Truck Weight Sampling Procedure for Use in Virginia PDF

Author: Benjamin H. Cottrell

Publisher:

Published: 1992

Total Pages: 54

ISBN-13:

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The Traffic Monitoring Guide (TMG) provides a method for the development of a statistically based procedure to monitor traffic characteristics such as traffic loadings. Truck weight data in particular are a major element of the pavement management process because there is a strong relationship between pavement deterioration and truck weights. Because truck weight data collected by weigh-in-motion (WIM) systems are more representative of actual traffic loadings and are more efficient than enforcement and static weight data, the use of the TMG and WIM systems together provide improved monitoring of truck weights. The objective of this research was to develop a plan for VDOT to implement a truck weight sampling procedure using the TMG and WIM systems. Four alternatives from the TMG that were based on different schemes for multiple measurements at permanent WIM sites were evaluated. A truck weight sampling plan was developed for the preferred alternative. Truck weight sample sites, data collection procedures, cost and resources estimates, data from permanent WIM sites, and data management information are included in the plan.

Adaptive Sampling Methods for Vehicle Trajectory Data

Adaptive Sampling Methods for Vehicle Trajectory Data PDF

Author: Choudhury Nazib Wadud Siddique

Publisher:

Published: 2019

Total Pages: 94

ISBN-13:

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The ubiquitous use of smartphones and the emergence of new technologies such as ridesourcing and connected/automated vehicles provide new opportunities for mobile sensors in traffic monitoring and data collection. To make GPS based smartphones an effective and practical source of transportation data, one needs to address the multifaceted challenges related to mobile sensing. Since the mobile sensing technology requires individual sensors (often from end-users) sending location information periodically to the data collector (e.g., a server), one of such challenges is the storage and data transmission cost incurred to individual sensors/users, as well as the battery life of mobile sensors. This research aims to balance the data transmission cost and the needs to collect detailed mobile sensing data by developing a method to resample the smartphone-based GPS data at the user side. The work introduces the concept of Vehicle Flow State (VFS) to explain the implicit nature of the probe vehicle's motion. Then the work proposes a methodology which first estimates the vehicle flow state (VFS) of the sensor/vehicle from its trajectory data and then uses the estimated VFS to adjust the sampling rate of the trajectory accordingly. The primary contributions of this work are as follows. First, this work develops the concept of vehicle flow state (VFS) and developed an HMM-based method to identify the VFS of an individual vehicle. Second, two self-adaptive sampling strategies for vehicle trajectory data are presented based on the identified VFS, which reduces the overall data size and transmission cost. Finally, this work presents comprehensive testing and validation of the proposed methods with real-world trajectory data. The methods and algorithms provided in this work will be of significant value to the server-side and the user/client side of a smartphone-based vehicle trajectory data collection system. The reduced data using proposed methods show a promising result in traffic modeling applications (such as queue length estimation) and the end user's privacy protection.

Adaptive Video-based Vehicle Classification Technique for Monitoring Traffic

Adaptive Video-based Vehicle Classification Technique for Monitoring Traffic PDF

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Published: 2015

Total Pages: 4

ISBN-13:

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This report presents a methodology for extracting two vehicle features, vehicle length and number of axles in order to classify the vehicles from video, based on Federal Highway Administration's (FHWA's) recommended vehicle classification scheme. There are two stages regarding this classification. The first stage is the general classification that basically classifies vehicles into 4 categories or bins based on the vehicle length (i.e., 4-Bin length-based vehicle classification). The second stage is the axle-based group classification that classifies vehicles in more detailed classes of vehicles such as car, van, buses, based on the number of axles. The Rapid Video-based Vehicle Identification System (RVIS) model is developed based on image processing technique to enable identifying the number of vehicle axles. Also, it is capable of tackling group classification of vehicles that are defined by axles and vehicle length based on the FHWA's vehicle classification scheme and standard lengths of 13 categorized vehicles. The RVIS model is tested with sample video data obtained on a segment of I-275 in the Cincinnati area, Ohio. The evaluation result shows a better 4-Bin length-based classification than the axle-based group classification. There may be two reasons. First, when a vehicle gets misclassified in 4-Bin classification, it will definitely be misclassified in axle-based group classification. The error of the 4-Bin classification will propagate to the axle-based group classification. Second, there may be some noises in the process of finding the tires and number of tires. The project result provides solid basis for integrating the RVIS that is particularly applicable to light traffic condition and the Vehicle Video-Capture Data Collector (VEVID), a semi-automatic tool to be particularly applicable to heavy traffic conditions, into a "hybrid" system in the future. Detailed framework and operation scheme for such an integration effort is provided in the project report.

Evaluation of Methodology for Determining Truck Vehicle Miles Traveled in Illinois

Evaluation of Methodology for Determining Truck Vehicle Miles Traveled in Illinois PDF

Author: R. F. Benekohal

Publisher:

Published: 2002

Total Pages: 208

ISBN-13:

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Nationwide surveys of departments of transportation, metropolitan planning organizations, and classification vendors/producers were conducted to determine the state of practice on equipment and methodologies used to determine truck vehicle miles traveled (VMT). The current Illinois Department of Transportation (IDOT) methodology was evaluated and it was found that it overestimated truck VMT for multi-unit trucks on all eight functional classes except on the minor urban arterials. The average overestimation was 11.5% and it varied from -10% to +44%. The current method overestimated truck VMT for single-unit trucks in five and underestimated in three functional classes. The under/over estimation ranged from -6% to +35%, but the average value was close to zero. To calculate truck VMT more accurately, this study proposed two different methods based on average truck percentage (ATP) and average section length (ASL). In the ATP method, truck VMT is calculated by multiplying the ATP for a group of roadway sections by the total VMT of that group. The ATP method should be used when the ATP and the total VMT by volume groups are available. In the ASL method, the total truck volume for the sampled sections is multiplied by the ASL. The ASL method should be used when the information required for ATP is not available or not reliable. Sample size influences the accuracy of truck VMT estimation and the decision on sample size must consider the error level that is acceptable. This study looked at the likely error for different sample sizes and recommended using 8% to 16% of the number of roadway sections. The sections should be distributed among the volume groups. Recently, IDOT collects vehicle classification data for three categories at about 10,000 sections, biennially. It is recommended to evaluate the truck VMT calculation using recent data.

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