Validation of Urban Vehicle Classification Sampling Methodology

Validation of Urban Vehicle Classification Sampling Methodology
Author:
Publisher:
Total Pages: 104
Release: 2005
Genre: Sampling (Statistics)
ISBN:

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.

Effective Statistical Learning Methods for Actuaries III

Effective Statistical Learning Methods for Actuaries III
Author: Michel Denuit
Publisher: Springer Nature
Total Pages: 250
Release: 2019-10-31
Genre: Business & Economics
ISBN: 3030258270

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.

HRIS Abstracts

HRIS Abstracts
Author: National Research Council (U.S.). Highway Research Board
Publisher:
Total Pages: 840
Release: 1987
Genre: Highway engineering
ISBN:

Machine Learning Techniques for Smart City Applications: Trends and Solutions

Machine Learning Techniques for Smart City Applications: Trends and Solutions
Author: D. Jude Hemanth
Publisher: Springer Nature
Total Pages: 227
Release: 2022-09-19
Genre: Computers
ISBN: 303108859X

This book discusses the application of different machine learning techniques to the sub-concepts of smart cities such as smart energy, transportation, waste management, health, infrastructure, etc. The focus of this book is to come up with innovative solutions in the above-mentioned issues with the purpose of alleviating the pressing needs of human society. This book includes content with practical examples which are easy to understand for readers. It also covers a multi-disciplinary field and, consequently, it benefits a wide readership including academics, researchers, and practitioners.

Computational Science and Its Applications – ICCSA 2021

Computational Science and Its Applications – ICCSA 2021
Author: Osvaldo Gervasi
Publisher: Springer Nature
Total Pages: 734
Release: 2021-09-10
Genre: Computers
ISBN: 3030869733

The ten-volume set LNCS 12949 – 12958 constitutes the proceedings of the 21st International Conference on Computational Science and Its Applications, ICCSA 2021, which was held in Cagliari, Italy, during September 13 – 16, 2021. The event was organized in a hybrid mode due to the Covid-19 pandemic.The 466 full and 18 short papers presented in these proceedings were carefully reviewed and selected from 1588 submissions. The books cover such topics as multicore architectures, blockchain, mobile and wireless security, sensor networks, open source software, collaborative and social computing systems and tools, cryptography, applied mathematics human computer interaction, software design engineering, and others. Part IV of the set includes the papers on Urban and Regional Planning and the proceedings of the following workshops: ​International Workshop on Blockchain and Distributed Ledgers: Technologies and Applications (BDLTA 2021); International Workshop on Computational and Applied Mathematics (CAM 2021); International Workshop on Computational and Applied Statistics (CAS 2021); International Workshop on Computerized Evaluation of Economic Activities: Urban Spaces (CEEA 2021).The chapters "Automated Housing Price Valuation and Spatial Data", "Spatial Automated Valuation Model (sAVM) – From the Notion of Space to the Design of an Evaluation Tool", and "A MCDA/GIS-Based Approach for Evaluating Accessibility to Health Facilities" are published open access under a CC BY license (Creative Commons Attribution 4.0 International License).

Decision-making Strategies for Automated Driving in Urban Environments

Decision-making Strategies for Automated Driving in Urban Environments
Author: Antonio Artuñedo
Publisher: Springer Nature
Total Pages: 205
Release: 2020-04-25
Genre: Technology & Engineering
ISBN: 3030459055

This book describes an effective decision-making and planning architecture for enhancing the navigation capabilities of automated vehicles in the presence of non-detailed, open-source maps. The system involves dynamically obtaining road corridors from map information and utilizing a camera-based lane detection system to update and enhance the navigable space in order to address the issues of intrinsic uncertainty and low-fidelity. An efficient and human-like local planner then determines, within a probabilistic framework, a safe motion trajectory, ensuring the continuity of the path curvature and limiting longitudinal and lateral accelerations. LiDAR-based perception is then used to identify the driving scenario, and subsequently re-plan the trajectory, leading in some cases to adjustment of the high-level route to reach the given destination. The method has been validated through extensive theoretical and experimental analyses, which are reported here in detail.