Validation of Urban Vehicle Classification Sampling Methodology

Validation of Urban Vehicle Classification Sampling Methodology PDF

Author:

Publisher:

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.

Validating the Performance of Vehicle Classification Stations

Validating the Performance of Vehicle Classification Stations PDF

Author: Benjamin André Coifman

Publisher:

Published: 2012

Total Pages:

ISBN-13:

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Vehicle classification is used in many transportation applications, e.g., infrastructure management and planning. Typical of most developed countries, every state in the US maintains a network of vehicle classification stations to explicitly sort vehicles into several classes based on observable features, e.g., length, number of axles, axle spacing, etc. Periodic performance monitoring is necessary to ensure the quality of collected data; however, such testing has been prohibitively labor intensive to do as thoroughly as needed. To address these challenges, this study examined three interrelated facets of vehicle classification performance monitoring. First, we manually evaluate the performance of vehicle classification stations on a per-vehicle basis, second we develop a portable LIDAR (light detection and ranging) based vehicle classification system that can be rapidly deployed, and third we use the LIDAR based system to automate the manual validation done in the first part using the tools from the second part. In the first part we examined over 18,000 vehicles, at several stations and found good performance overall, but performance for trucks was far worse than passenger vehicles. About a third of the errors were fixed by modifying the classification decision tree, the remaining two thirds of the errors are unavoidable because different classes have overlapping axle spacings or lengths (e.g., passenger vehicles and trucks, or commuter cars and motorcycles). All subsequent uses of the classification data must accommodate this unavoidable blurring. Next, we develop a side-fire LIDAR based classification system that does not require any calibration in the field. Finally, we develop a process to use the LIDAR system (or another temporary vehicle classification system) deployed concurrent to a permanent classification station to semi-automate the manual validation. The automated process does the bulk of the work, typically taking a user only a few minutes to validate all of the exceptions from all lanes over an hour of data. We found wide variance in performance from one station to the next. Since these errors are a function of the specific station, there would be benefit in the short term to leverage the LIDAR based system to evaluate the performance of many other classification stations to catch systematic errors that bias classification performance.

Guide to Urban Traffic Volume Counting

Guide to Urban Traffic Volume Counting PDF

Author: R. A. Ferlis

Publisher:

Published: 1981

Total Pages: 60

ISBN-13:

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This report presents methods by which urbanized areas can develop and implement integrated traffic data counting programs to serve the volume data needs of all their agencies. The procedures presented complement the techniques for measuring vehicle type and occupancy presented in the Guide for Estimating Urban Vehicle Classification and Occupancy. Methods for estimating volume at a single location, volume across a particular cordonline or cutline, vehicle-miles travelled within a corridor, and regional vehicle-miles travelled are presented. Of particular value to transportation technical staffs in urban areas, these techniques permit collection of volume data at pre-determined levels of precision, and in a cost-effective manner.

Detection, Tracking and Classification of Vehicles in Urban Environments

Detection, Tracking and Classification of Vehicles in Urban Environments PDF

Author: Zezhi Chen

Publisher:

Published: 2012

Total Pages:

ISBN-13:

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The work presented in this dissertation provides a framework for object detection,tracking and vehicle classification in urban environment. The final aim is to produce a system for traffic flow statistics analysis. Based on level set methods and a multi-phase colour model, a general variational formulation which combines Minkowski-form distance L2 and L3 of each channel and their homogenous regions in the index is defined. The active segmentation method successfully finds whole object boundaries which include different known colours, even in very complex background situations, rather than splitting an object into several regions with different colours. For video data supplied by a nominally stationary camera, an adaptive Gaussian mixture model (GMM), with a multi-dimensional Gaussian kernel spatio-temporal smoothing transform, has been used for modeling the distribution of colour image data. The algorithm improves the segmentation performance in adverse imaging conditions. A self-adaptive Gaussian mixture model, with an online dynamical learning rate and global illumination changing factor, is proposed to address the problem of sudden change in illumination. The effectiveness of a state-of-the-art classification algorithm to categorise road vehicles for an urban traffic monitoring system using a set of measurement-based feature (BMF) and a multi-shape descriptor is investigated. Manual vehicle segmentation was used to acquire a large database of labeled vehicles form a set of MBF in combination with pyramid histogram of orientation gradient (PHOG) and edge-based PHOG features. These are used to classify the objects into four main vehicle categories: car, van (van, minivan, minibus and limousine), bus (single and double decked) and motorcycle (motorcycle and bicycle). Then, an automatic system for vehicle detection, tracking and classification from roadside CCTV is presented. The system counts vehicles and separates them into the four categories mentioned above. The GMM and shadow removal method have been used to deal with sudden illumination changes and camera vibration. A Kalman filter tracks a vehicle to enable classification by majority voting over several consecutive frames, and a level set method has been used to refine the foreground blob. Finally, a framework for confidence based active learning for vehicle classification in an urban traffic environment is presented. Only a small number of low confidence samples need to be identified and annotated according to their confidence. Compared to passive learning, the number of annotated samples needed for the training dataset can be reduced significantly, yielding a high accuracy classifier with low computational complexity and high efficiency.

Effective Statistical Learning Methods for Actuaries III

Effective Statistical Learning Methods for Actuaries III PDF

Author: Michel Denuit

Publisher: Springer Nature

Published: 2019-10-31

Total Pages: 250

ISBN-13: 3030258270

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This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. It simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous yet accessible. Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. Various topics are covered from feed-forward networks to deep learning, such as Bayesian learning, boosting methods and Long Short Term Memory models. All methods are applied to claims, mortality or time-series forecasting. Requiring only a basic knowledge of statistics, this book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning. This is the third of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.

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