Jonathan D Regehr

Safety performance of longer combination vehicles relative to other articulated trucks

This article helps improve the understanding about the safety performance of longer combination vehicles (LCVs) relative to other articulated trucks operating on rural highways, using evidence from the Canadian portion of the CANAMEX trade corridor. The analysis reveals that from a collision rate perspective, LCVs as a group have better safety performance than other articulated trucks. Turnpike doubles have the lowest collision rate of all articulated truck types (16 collisions per 100 million vehicle-kilometres of travel or VKT), followed by Rocky Mountain doubles (32 collisions per 100 million VKT). The collision rate for triple trailer combinations (62 collisions per 100 million VKT) is higher than the collision rates for tractor semitrailers (42 collisions per 100 million VKT) and legal-length tractor double trailers (44 collisions per 100 million VKT). These results are an important input for civil engineering and transport policy decisions concerning longer combination vehicle operations.

Hybrid Approach for Clustering Vehicle Classification Data to Support Regional Implementation of the Mechanistic-Empirical Pavement Design Guide

This paper develops a hybrid approach for analyzing vehicle classification data and applies the approach to a fused data set from multiple jurisdictions in the Canadian prairie region. Application of the approach results in a set of regional default truck traffic classification groups for use in the Mechanistic–Empirical Pavement Design Guide. The hybrid approach is a conglomeration of three components: statistical clustering procedures, expert judgment, and industry intelligence. By applying the hybrid approach, analysts receive the joint benefits of analytical rigor and industry-oriented pragmatism. Application of this approach results in eight truck traffic classification groups for the Canadian prairie region that exhibit distinct differences from the default distributions developed for national use in the United States. The benefits of applying the hybrid approach on fused data sets include (a) the statistical strength gained from use of additional classification data, (b) the development of truck traffic classification groups that better reflect the diversity of patterns in a region, and (c) the potential for improved ability to capture future shifts in truck traffic characteristics because of experience gained in other jurisdictions. The paper also identifies limitations to the hybrid approach that should be considered. These limitations include varying data quality between jurisdictions, the sensitivity of low-volume sites to changes in industry patterns and the ability to track these changes, and potential shortages of continuous classification sites. When its benefits and limitations are well understood, the hybrid approach can be applied to truck traffic data analyses in any jurisdiction.

Methodology to Estimate the Distance Traveled by Trucks on Rural Highway Systems

Truck travel, normally expressed in terms of truck vehicle-distance travel (VDT), is a critical data requirement for the design, construction, operation, and management of rural highway systems. The need to estimate and characterize truck travel on a system-wide basis, yet retain a compatible, site-specific mining capability, challenges the traditional approach of traffic monitoring programs. This paper develops and applies a two-phase methodology to estimate truck VDT on rural highway systems. The first phase, which leverages the approach recommended for developing truck traffic data inputs for mechanistic-empirical pavement design, processes vehicle classification data obtained from continuous, sample, and manual counts into annual average daily truck traffic (AADTT) estimates by vehicle class. The second phase attributes these site-specific AADTT estimates to highway segments comprising a rural highway network. The methodology, which is transferrable across jurisdictions, integrates standard statistical procedures with engineering judgment and trucking industry intelligence by establishing transparent decision algorithms and criteria. Application of the methodology using real data reveals relevant limitations and illustrative results that have application to a wide range of transportation engineering decisions.

Evaluating Annual Average Daily Traffic Calculation Methods with Continuous Truck Traffic Data

Traffic volume, often measured in relation to annual average daily traffic (AADT), is a fundamental output of traffic monitoring programs. At continuous count sites, unusual events or counter malfunctions periodically cause data loss, which influences AADT accuracy and precision. This paper evaluates five methods used to calculate AADT values from continuous count data, including the use of a simple average, the commonly adopted method developed by AASHTO (the AASHTO method), and methods that incorporate adjustments to the AASHTO method. The evaluation imposes data removal scenarios designed to simulate real-life causes of data loss to quantify the accuracy and precision improvements provided by these adjustments. Truck traffic data are used to reveal issues arising when volumes are low or when they exhibit unusual temporal patterns. Unlike the AASHTO method, which incorporates a weighted average and an hourly base time period, the FHWA method provides the most accurate and precise results in all data removal scenarios, according to the evaluation. Specifically, when up to 15 days of data are randomly removed, application of the FHWA method can be expected to produce errors within approximately é1.4% of the true AADT value, 95% of the time. Results also demonstrate that including a weighted average improves AADT accuracy primarily, whereas the use of hourly rather than daily count data influences precision. If possible, practitioners contemplating the adoption of the FHWA method should assess its relative advantages within their local context.

Repeatable Procedure for Determining a Representative Average Rail Profile

The use of rail profile measurements for the planning and specification of rail-grinding activities normally involves comparing the existing and desired rail profiles within a rail segment. In current practice, a somewhat subjective approach is used to select a measured profile—usually located near the midpoint of the segment—that represents the profiles throughout the rail segment. Industry-standard rail profile data were used to develop an automated procedure for calculating a representative average (mean) rail profile for a rail segment. The procedure was verified by comparing the calculated average with an expected profile. Then, it was validated by comparing the calculated average profiles of 42 in-service rail segments (10 tangents and 32 curved segments) with the Corresponding median rail profiles for each segment, chosen subjectively. Validation results indicated that the coordinates comprising the mean and median profiles differed by less than 1%, on average. Agreement was stronger for tangent rail segments than for curved rail segments, as expected. Therefore, validation demonstrated that the procedure yields results comparable with current practice while it improves the objectivity and repeatability of the decisions that support rail-grinding activities. The procedure also offers the opportunity for integration with existing software tools to help automate the specification of grinding activities that minimize metal removal and prolong rail life.

Understanding and Estimating In-Service Axle Weights of Transit Buses

Despite evidence that certain transit buses exceed axle weight limits (sometimes without any passengers on board) and mostly anecdotal indications of the concomitant pavement impacts, relatively little empirical evidence substantiates these impacts. The number of transit buses exceeding weight limits has been exacerbated by regulatory changes directed at improving emissions and accessibility. These changes have required manufacturers to include heavy auxiliary equipment, such as emissions reduction components and hydraulic systems, on transit buses and have been introduced without commensurate increases in the weight limits of transit buses. Public agencies have limited knowledge about transit bus weights and their pavement impacts. Further, estimating in-service weights of transit buses is difficult. To help improve knowledge about issues of transit bus weights, this paper describes the legal and regulatory factors surrounding transit bus weight, basic estimates of in-service bus weights, contributing factors to transit bus weights, challenges for reducing bus weights, pavement impacts of transit buses, and challenges for estimating in-service weights of transit buses. The paper also develops and applies a methodology for estimating in-service weights of transit buses by using Winnipeg, Manitoba, Canada, as a case study. Application of this methodology (a) demonstrates the need for reliable local data when in-service weights of transit buses are estimated and (b) empirically corroborates regulatory compliance and pavement-related concerns by using data collected from in-service transit buses.

Road safety performance measures and AADT uncertainty from short-term counts

OBJECTIVE The objective of this paper is to enable better risk analysis of road safety performance measures by creating the first knowledge base on uncertainty surrounding annual average daily traffic (AADT) estimates when the estimates are derived by expanding short-term counts with the individual permanent counter method. BACKGROUND Many road safety performance measures and performance models use AADT as an input. While there is an awareness that the input suffers from uncertainty, the uncertainty is not well known or accounted for. METHOD The paper samples data from a set of 69 permanent automatic traffic recorders in Manitoba, Canada, to simulate almost 2 million short-term counts over a five year period. These short-term counts are expanded to AADT estimates by transferring temporal information from a directly linked nearby permanent count control station, and the resulting AADT values are compared to a known reference AADT to compute errors. The impacts of five factors on AADT error are considered: length of short-term count, number of short-term counts, use of weekday versus weekend counts, distance from a count to its expansion control station, and the AADT at the count site. RESULTS The mean absolute transfer error for expanded AADT estimates is 6.7%, and this value varied by traffic pattern group from 5% to 10.5%. Reference percentiles of the error distribution show that almost all errors are between -20% and +30%. Error decreases substantially by using a 48-h count instead of a 24-h count, and only slightly by using two counts instead of one. Weekday counts are superior to weekend counts, especially if the count is only 24h. Mean absolute transfer error increases with distance to control station (elasticity 0.121, p=0.001), and increases with AADT (elasticity 0.857, p<0.001). IMPLICATIONS These results can support evidence-based risk analysis of road safety performance measures that use AADT as inputs. Analytical frameworks for such analysis exist but are infrequently used in road safety because the evidence base on AADT uncertainty is not well developed.

Framework for Characterizing Truck Traffic Related to Petroleum Well Development and Production in Unconventional Shale Plays

Technological developments have stimulated rapid change and growth in North Americas petroleum industry. This growth has placed significant demand on local and regional transportation infrastructure. This paper develops and applies an integrated framework for characterizing petroleum-related truck traffic to support the engineering and planning efforts needed to accommodate the growth. The framework draws from standard methodologies used for monitoring truck traffic and modeling freight transport demand and illustrates how these methodologies interrelate through their reliance on common data sources and their mutual goal of characterizing current and future truck traffic. Specific data sources relevant to the petroleum industry are identified within the context of the framework, although the framework is generic and transferable to other jurisdictions and industries with unique truck travel demands. An illustrative application of the framework for the petroleum industry reveals new insights about the industry that enable a better engineering and planning response to its transportation needs. The application examples also demonstrate how data describing exogenous industry factors, activity system variables, and transportation supply variables integrate with data collected by truck traffic monitoring programs to (a) clarify the interpretation of traditional truck traffic monitoring data, (b) provide direction in the design of a monitoring program, and (c) justify adjustments to standard monitoring procedures. However, successful data integration is limited by the difficulty in appropriately fusing quantitative and qualitative data sources, the increased reliance on industry intelligence, and the challenge of representatively observing a dynamic industry.

Issues and Options for Oversize/Overweight Permitting of Petroleum-Related Trucks in a Performance-Based Regulatory Context: The Manitoba Experience

This paper presents a case study of the Manitoba experience in permitting petroleum-related oversize/overweight (OS/OW) truck traffic. In recent years, Southwest Manitoba, along with many regions throughout North America, has experienced rapid growth and change in the petroleum industry. This growth has fuelled economic development and also caused infrastructure challenges on rural roads that are being used by unique vehicle configurations, many of which are beyond basic truck size and weight (TSW) limits. Manitobas OS/OW permitting program for these vehicles stems from the performance-based approach to TSW regulation being used in Canada since 1988 and relies on ongoing collaboration with the petroleum industry. Manitobas experiences have led to several insights, which may be options for other jurisdictions facing similar issues related to OS/OW petroleum-related trucking. These insights include: (1) purposeful collaboration with the industry and officials in neighbouring jurisdictions to understand permitting needs and barriers; (2) supplementing qualitative understanding of the industry with quantitative data; and (3) identifying opportunities to expedite permitting procedures by issuing annual permits to routinely-configured vehicles, utilizing technologies to assist with TSW enforcement, and rationalizing permit fee structures.

Options for Exposure-Based Charging for Long Multiple Trailer Truck Permits

This paper analyzes options for exposure-based charging for long multiple trailer truck permits. Long trucks—Rocky Mountain doubles, turnpike doubles, and triple trailer combinations—are granted permits because they provide increased technical productivity for hauling low-density freight. Experiences in the Canadian Prairie Region indicate that standardization of rationales used to establish permit charges is increasingly important as the network permitting long trucks expands and enables regional operation. Contributing to the differences in charging perspectives is the cube-out condition under which most long trucks operate, which challenges the justification for assessing incremental fees for their operation. An analysis of truck size and weight regulations governing the cubic trucking domain demonstrates that trucks in this domain are ideally suited for freight densities up to about 240 kg/m3 (15 lb/ft3). Four exposure-based options for establishing permit charges are presented: revenue-based charging, cost recovery, incentive provision, and privatization. Carrier costs and public revenues for three cases in the revenue-based charging option—benefit-sharing, revenue neutrality, and full benefit taxation—are analyzed and compared with the current base case conditions in Manitoba. The analysis reveals increases in turnpike double operating costs ranging from 2% for revenue neutrality to 50% for full benefit taxation. Corresponding public revenue increases for these three cases range from 30% for revenue neutrality to a 12-fold increase for full benefit taxation. The sensitivity of the results to freight density and utilization demonstrates the need for charges to reflect differences between cube-out and weigh-out conditions, and the distance traveled by long trucks.