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Record W7005068560

A performance-based engineering model for design and evaluation of on-road enforcement programs governing commercial vehicle operations

2005· dissertation· en· W7005068560 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2005
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital limb and hand anomalies
Canadian institutionsnot available
Fundersnot available
KeywordsTruckEnforcementScope (computer science)Software deploymentProduct (mathematics)Scale (ratio)Commercial vehicle
DOInot available

Abstract

fetched live from OpenAlex

This thesis develops and applies a performance-based engineering model for the design and evaluation of on-road enforcement programs governing commercial vehicle operations (CVOs).The enforcement programs involve three regulatory elements: (1) truck safety, (2) truck weights and dimensions (W&D), and (3) driver, vehicle and carrier credentials.A performance-based model relates system outputs and outcomes to the resources used in program activities (inputs).Output measures reflect the quantity of resources used (e.g., person-hours/year operating weigh scales), the scale or scope of activities performed (e.g., number of trucks checked for safe condition), and the efficiency in converting resources into a product (e.9., violation rate).Outcome measures are intended to reflect an agency's degree/level of success in meeting its goals and objectives, with particular reference to the agency's responsibilities (e.g., Canadian goal of achieving a 20 percent reduction in the number of fatalities and injuries involving commercial vehicles by 2010).CNCHRP,2o0o) 1.2 BACKGROUND AND NEED On-road enforcement of commercial vehicle regulations plays an essential part in ensuring transportation safety, minimizing damage to infrastructure, and providing a level .Increase compliance and collection of fees .Help to obtain the most out of the advanced technology investments already made by the Department (e.g., deployment of laptop computers in patrol vehicles, installation of new computers at the weigh scales, acquisition and installation of WMAVC equipment, etc.) .Help to demonstrate the value of those investments from safety and economic perspectives. OBJECTIVES AI{D SCOPEThe objectives ofthe research are:1. To gain an understanding of current policies, regulations and programs governing commercial vehicle operations.This requires a comprehensive environmental scan including a literature review and jurisdictional considerations of the commercial vehicle enforcement programs in select North American jurisdictions. To gain practical knowledge about enforcement program activity inManitoba.This involves the gathering, assembly and analysis of a variety of data sets.A comprehensive commercial vehicle compliance database is developed, including a GIS platform, spanning a three-year inspection period, 2000 to 2002.The data sets are of two types: (1) commercial vehicle compliance related information, including inspectors' monthly reports, Commercial Vehicle Safety Alliance (CVSA) reports, and motor carrier compliance information system quarterly reports; and (2) traffic and truck traffic databases generated from data obtained through permanent vehicle counting, classifying and weighing stations, weigh scale surveys, and applicable geographic information systems (GIS) 3. To gain an understanding of the truck trafftc activity in the province and additional considerations relevant to the research.The additional considerations investigated are: truck weights (overweight), spring road restrictions, infrastructure condition, heavy truck collisions, and expert intelligence regarding the need for enforcement activity.4. To identifu system vulnerability indicators warranting a high, normal, or low level of compliance monitoring from the W&D standpoint, truck safety standpoint and credentials standpoint. 5. To define performance-based measures to be used in designing and evaluating optional activity types/levels of the enforcement program.Performance measures are typically thought of as ouþuts and./or outcomes.6.To analyze relationships between program outputs and outcomes as a function of different types and intensities of on-road compliance activities þrogram inputs), and to develop a performance-based engineering model from these relationships.7. 8. 1.4 To apply the performance-based engineering model by evaluating the effects of alternative allocation of enforcement resources under BASE CASE funding.The BASE CASE resources are defined in Chapter 8 and are obtained from the 2000 to 2002 compliance information.Three alternatives are considered: (l) TKT- based allocation; (2) TKT-based allocation with 2417 operatton at West Hawk HTIS; and (3) TKT-based allocation using roving enforcement method only.To identifu considerations for future research.RESEARCH CONSIDERATIONS The following requirements were essenti¿l to the conduct of this research: 1.A knowledge of the MTGS commercial vehicle enforcement policies and programs.This was gained through my work with the Compliance and Regulatory Service Branch spanning three summer work terms, from 2000 to 2002.It involved obtaining practical knowledge of mechanical inspections of trucks, operation of permanent and portable weight measuring devices, truck permitting procedures, and enforcement of tn¡ck sizelweight and other regulations pertaining to trucks under the Highway Trafüc Act. 2. Assistance in the creation of the tn¡ck traffic database required for this research.This was made possible through my participation, as a junior Transportation Engineer (EIT), in the development and implementation of the new truck traffrc information system for the province of Manitoba, which includes extensive traffrc data collection and analysis.3. Regular professional interaction and communication with engineers, technical field staff, MTGS officials and individuals from the trucking industry (drivers and carriers).4.This research is data-intensive and requires extensive practical knowledge for database creation and an understanding of the commercial vehicle compliance information.The data sets utilized include: (1) inspectors' monthly reports, Commercial Vehicle Safety Alliance (CVSA) reports, motor carrier compliance information system quarterly reports ; (2) taffic and tnrck traffic databases generated from data obtained through permanent vehicle counting, classiSing and weighing stations, weigh scale surveys, and applicable geographic information systems (GIS); and (3) infrastructure condition dat¿ (pavement and bridge). 5. Utilization of the Manitoba Single Centerline spatial database platform (Han, 2003), GIS for transporüation, and dat¿base software used for analysis and synthesis of information (Maptitude, Access, Paradox).Understanding the structure of the spatial data is critical in analysis of commercial vehicle compliance information on a GIS platform.6.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.236
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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