Single-unit Trucks Research Articles

Machine-learning approach for estimating passenger car equivalent factors using crowdsourced data

Passenger car equivalent (PCE) factors are used by the Highway Capacity Manual (HCM) to convert truck volumes to equivalent passenger car volumes and are typically calculated using multi-class volumes collected from traffic sensors. However, this requires costly sensor installations that provide limited spatial coverage. Therefore, this study proposes a novel approach to estimate PCE volumes using crowdsourced and open data. A multi-class volume estimation model (TS-SAE-XGB) is proposed to estimate passenger car and truck volumes, and single unit truck ratios. These parameters are input to a PCE interpolation algorithm which estimates PCE values using HCM methods. A spatial leave-one-out cross validation was conducted to compare the proposed model against five other machine learning models when estimating PCE values. The TS-SAE-XGB model estimated PCE and heavy vehicle factors with a MAPE of 6.22% and 3.03%, respectively, providing transportation professionals a practical method of estimating freeway PCE values where sensors are unavailable.

Development of a Mechanistic-Empirical-Based Highway Cost Allocation Model for Flexible Pavements

Construction, operation, and maintenance of a pavement network requires funding, partially sourced from road user taxes. Recent studies showed that lightweight vehicles are typically taxed higher compared with heavy trucks that damage the roads the most. To facilitate the equity and fairness of the allocated costs to different vehicles in the United States (U.S.), Highway Cost Allocation Studies (HCAS) were performed using various pavement performance prediction models. Reviewed literature showed the lack of mechanistic-empirical (ME)-based HCAS models for the flexible pavement network. In this study, a national-level ME-based HCAS model was developed, and the damage shares of different vehicle classes have been estimated for 67,583 pavement sections in the U.S. Highway Performance Monitoring System (HPMS) database. The proposed HCAS model was compared with the existing Federal Highway Administration (FHWA) HCAS model (i.e., National Pavement Cost Model [NAPCOM]). The analysis of the traffic data showed that two-axle single-unit trucks (SU2) and tractor-semitrailers with two tandem and one single axle (CS5T) were the most frequent users of the pavement network. The results showed that the damage share of SU2 is dominant in minor roadways, while the damage share of the heavier vehicles in the CS5T class is dominant in major arterials and interstates. In addition, it was found that, although the geographical location and environmental condition of the pavement section affects the magnitude of the pavement distresses, the distribution of the damage shares remains almost the same. This can be attributed to the similarities in the traffic data, for example, vehicle class distribution and axle load spectra.

Characterization of Motorcycle Encroachments in the U.S

In 2020, there were 5,579 motorcyclist fatalities in the U.S., which is the highest on record. Despite accounting for only 3% of registered vehicles, motorcycles are involved in 42% of fatal guardrail impacts. Roadside safety hardware testing guidelines are outlined in the Manual for Assessing Safety Hardware (MASH) for passenger vehicles and large trucks but these procedures do not include any motorcycle impacts. Although international test procedures for roadside hardware prescribe motorcycle crash tests, it is not known if the prescribed test conditions reflect the conditions at which motorcycles depart the roadway in the U.S. A better understanding of the characteristics of motorcycles departing the roadway in the U.S. is needed before the development of motorcycle crash tests. This study used the National Cooperative Highway Research Program (NCHRP) 17-88 database to compare the encroachment and impact characteristics of motorcycles, passenger vehicles, single-unit trucks, and tractor-trailer trucks. Motorcycles were found to have a similar distribution of impact angles to passenger vehicles, with an 85th percentile of 24 degrees. The median and 85th percentile impact angle was found to be shallower for tractor-trailer trucks compared with motorcycles and passenger vehicles. Additionally, large trucks and motorcycles were found to roll over at a higher frequency than passenger vehicles. During the first event, almost 80% of motorcycles were upright. By the second event, almost 50% of motorcyclists were separated from the motorcycle. This indicates that a large percentage of riders lose contact with the motorcycle during the first event and are separated during any subsequent events. Based on these results, future motorcycle-barrier tests should consider an upright configuration and an impact angle of 24 degrees.

Rollover Investigation of Two-Axle Heavy Vehicle Based on Load Transfer Ratio with Vehicle and Road Condition: A Simulation Approach

Commercial heavy vehicle is commonly used to transport goods and people efficiently and safely. A previous study has shown a number of gross vehicle weight (GVW) and speed violations recorded in selected areas in Malaysia, and two-axle single unit truck (SUT) is the most commercial heavy vehicle type that violated the weight and speed regulation. Moreover, accidents involving heavy vehicles result in severe traffic disruption and fatalities to other road users due to heavy vehicle size and capability to carry huge amounts of goods. Thus, the objective of this paper is to investigate the correlation and effect of the vehicle and road condition on the two-axle SUT rollover during cornering on the curved road using the simulation approach. The verified two-axle SUT model is simulated using IPG-TruckMaker® with different GVW, speed, and coefficient of friction values while the cornering radius, driver behaviour and load’s center of gravity remain constant. A correlation based on performance indices is established, and it is found that the heavy vehicle speed has a strong correlation to the lateral load transfer to cause a rollover followed by GVW and coefficient of friction, respectively.

Performance Case Study of Concrete Bridge-Rail Post on Deck Overhang

Concrete post-and-beam bridge rails are a common bridge-rail type in the U.S. However, direct investigations of their performance on bridge-deck overhangs are rare, and current specifications do not clearly address this rail type or the design of decks supporting them. In this paper, the results of a performance case study of concrete bridge-rail posts on deck overhangs are presented. This study included (1) bogie testing of a MASH TL-4 concrete post on an instrumented deck, (2) development and calibration of a corresponding LS-DYNA model, and (3) use of the calibrated model to further describe system behavior, evaluate the effect of common design alternatives on performance, and corroborate the system performance from a full-scale crash test. The results of this study indicated that current design methods of the AASHTO LRFD Bridge Design Specifications (BDS) resulted in a significant underestimation of lateral capacity, partially because their neglect of inertial resistance. In the bogie test of the concrete post, over 40% of the peak lateral resistance of the post was attributed to inertial effects. Similarly, the calibrated LS-DYNA model indicated that the full-scale system could withstand a simplified single-unit truck (SUT) loading consisting of a 150 kip pulse load while only sustaining minor damage, despite having a BDS-predicted capacity of 73 kips. Additional findings included a characterization of deck demands, performance effects of design alternatives, such as edge distance and slab thickness, and the influence of using straight versus hooked transverse deck bars.

Effect of Pavement Roughness on Arterial Noise Using Different Vehicle Types

For a healthy and sustainable environment, it is necessary to reduce the negative impact of noise on neighborhoods adjacent to arterials. The development of the automotive industry, the introduction of hybrid and electric vehicles, and improvements to vehicle bodies have made tire–pavement interaction the primary source of noise at regular traffic speeds. The objective of this study was to investigate the effect of pavement surface condition on noise levels for different vehicles, including gasoline, hybrid, electric vehicles, and a single-unit truck. Both interior and exterior noises were measured along rural and urban arterials. The exterior noise was measured using the controlled pass-by method. The results of the analysis indicated that pavement roughness and traffic speed had significantly influenced the interior noise level. It was found that the relationship between interior noise and vehicle speed, and pavement roughness is linear. Compared to gasoline car, hybrid and electric cars may reduce interior noise by 7 and 11 dB(A), respectively. For rural arterials and speeds of 80 km/h, exterior noise was strongly affected by pavement roughness. Analysis of exterior noise on urban arterials at 50 and 60 km/h speeds showed that pavement roughness has a significant impact on the noise level. In contrast, the noise analysis under two-speed levels, which are too close to each other, did not help in capturing the impact of speed on exterior noise. Therefore, further studies are recommended to highlight this issue. For a sustainable and healthy environment, it is vital to keep the pavement in good condition and increase the proportion of electric and hybrid cars in the vehicle fleet.

Analysis of Wind Force on Cyclists From Passing Vehicles

Cyclists must withstand significant wind force from passing vehicles. This study analyzed the magnitude of wind force generated by different vehicle types (sports utility vehicle, pickup truck, single unit truck, and semi-trailer truck), vehicle speeds (25, 40, and 60 mph), separation distances (2 ft, 4 ft, and 6 ft), and cyclist riding positions (racing and touring). Three research methods were used: field tests in controlled and natural environments, computational fluid dynamic simulations, and scaled-model wind tunnel experiments. The analysis focused on assessing longitudinal and transverse wind forces that are directed at the cyclist in parallel and perpendicular directions, respectively. All three methods showed the expected trend—that wind force increases with an increase in vehicle muzzle bluntness, vehicle speed, and cyclist proximity. Selected measurements and various models are provided. The wind force to knock a cyclist over (i.e., the flipping moment) was calculated for each test scenario (vehicle type, speed, and separation distance) and compared with the recommended limit (17 N) found in the literature. The semi-trailer truck generated flipping moments that exceeded the limit for all scenarios except 25 mph and 6 ft separation. All the vehicles, at all the speeds tested, exceeded the limit at 2 ft separation. This novel use of simulation and wind tunnel experiments provides groundwork for future research.

Assessing highway revenue impacts of electric vehicles using a case study

Increasing electric vehicle (EV) adoption poses serious concerns about the long-term adequacy of highway revenues. This paper addresses not only the fuel tax revenue loss across all vehicle classes but also proposes alternative funding mechanisms to recover the loss. Data on average vehicle miles travelled per vehicle, fuel taxes, vehicle registrations, fuel efficiency and consumption are used. The case study location is Indiana, U.S., and analysis period is 2021–2035. The optimal annual fees to recover the loss for each battery EV class range from $241 (in 2021) to $342 (in 2035) for automobiles, $344 to $435 for light-duty trucks, $1246 to $1488 for buses, $969 to $1243 for single-unit trucks, $6192 to $7321 for combination trucks, and $26 to $35 for motorcycles. For the most likely scenario (5% EV market penetration (EVMP) for light-duty vehicles and 30% EVMP for medium-/heavy-duty vehicles), the statewide fuel tax revenue decreases by 21% from 2030 to 2035. The paper also discusses a vehicle-miles-travelled fee and a pay-as-you-charge fee. The study framework is designed to facilitate replication in other states, and the results can provide useful information for assessing the adequacy of the existing revenue models and efficacy of prospective mitigation measures.

Prediction of Short-Term Ultrafine Particle Exposures Using Real-Time Street-Level Images Paired with Air Quality Measurements.

Within-city ultrafine particle (UFP) concentrations vary sharply since they are influenced by various factors. We developed prediction models for short-term UFP exposures using street-level images collected by a camera installed on a vehicle rooftop, paired with air quality measurements conducted during a large-scale mobile monitoring campaign in Toronto, Canada. Convolutional neural network models were trained to extract traffic and built environment features from images. These features, along with regional air quality and meteorology data were used to predict short-term UFP concentration as a continuous and categorical variable. A gradient boost model for UFP as a continuous variable achieved R2 = 0.66 and RMSE = 9391.8#/cm3 (mean values for 10-fold cross-validation). The model predicting categorical UFP achieved accuracies for "Low" and "High" UFP of 77 and 70%, respectively. The presence of trucks and other traffic parameters were associated with higher UFPs, and the spatial distribution of elevated short-term UFP followed the distribution of single-unit trucks. This study demonstrates that pictures captured on urban streets, associated with regional air quality and meteorology, can adequately predict short-term UFP exposure. Capturing the spatial distribution of high-frequency short-term UFP spikes in urban areas provides crucial information for the management of near-road air pollution hot spots.

Distribution of Highway Infrastructure Cost Responsibility and Revenue Contribution Shares Among Highway Users in North Carolina: Present Conditions and Future Alternatives

As the proportion of fuel-efficient and electric vehicles is rapidly increasing, US states are challenged to meet the greater needs of the aging transportation infrastructure with less funds. The purpose of this study is to support state authorities in understanding the equity implications, feasibility, and effectiveness of many of the currently available transportation funding mechanisms. To fulfill this objective, the study analyzes data from North Carolina (NC) between 2014 and 2017. As a first step, we estimate and compare the transportation infrastructure cost responsibility and revenue contribution of NC highway users. Results indicate that lightweight vehicles contribute substantially more to the revenue compared with their share of cost responsibilities. Single-unit trucks with four or more axles and multi-unit trucks are found to underpay their cost responsibilities by 37% to 92%. We use these results in combination with previous research to assess different scenarios for funding transportation infrastructure in the future. Each alternative is evaluated for its revenue generation potential, equity-related improvements, applicability, and public acceptance. Findings suggest that a moderate increase in traditional taxes such as motor fuels tax, vehicle sales tax, or the state sales tax are practical and reasonable approaches for generating significant additional revenue in the short run without strong opposition from the public. However, all the scenarios analyzed by this study, which are based on tax and fee structures planned or implemented in the USA, are found to have minor impacts in improving equity between the cost responsibility and revenue contribution across vehicle classes.

Numerical Simulation of the Effect of Repeated Load and Temperature on the Behavior of Asphalt Layers

Roads and highways are used by different vehicle types, and the heavy vehicles among them can be considered the most critical in loading, which causes failure in the pavement structure and increases rehabilitation and maintenance costs. In this study, the composite effects for wheel loads and temperature were considered in the finite element analysis using Abaqus 6.14. The asphalt layer was modeled as an elastic material, while the base and subbase layers were modeled as an elastoplastic material following the Mohr-Coulomb model. Also, the impact of wheel loads on flexible pavement settlement and the main output of analyzing pavement structure are almost represented by the vertical stresses and the surface deformation, which are considered the critical response point. A single unit truck was tried with two thicknesses of the asphalt layer, 14 cm, and 25 cm, with two different temperatures. Since base and subbase layer thicknesses remained constant, it does not affect the displacement variation. However, it was found that the increase of asphalt layer thickness from 0.14 m to 0.25 m leads to a decrease in the vertical displacement of about 0.59% and becomes 0.77% when the temperature increases to 50℃.

Manual for Assessing Safety Hardware TL-4 Evaluation of Critical Flare Rate for Cast-in-Place Concrete Barrier

Concrete median barriers are used by Departments of Transportation (DOTs) to provide separation of traffic. Typically, the crashworthiness of these barriers is tested and evaluated in full-scale crash testing conducted according to current roadside safety device standards. Occasionally, DOTs need to flare the concrete barrier length of need (LON) around fixed objects, such as bridge piers. Flaring a concrete barrier increases the effective impact angle which consequently increases the impact severity of the vehicle creating an opportunity for vehicle instability or excessive occupant risk factors. No current recommendations are available to guide the installation of flared barriers around such fixed objects, while still maintaining barrier crashworthiness. Although the current practice is to flare the cast-in-place (CIP) concrete barrier at a maximum 20:1 rate, no full-scale crash testing has been conducted to determine the crashworthiness of the system at this condition or at a flare rate that might be considered more critical. The crash tests reported in this paper were performed according to the Manual for Assessing Safety Hardware (MASH) Tests 4–10 (with the use of a passenger car), 4–11 (a pickup truck), and 4–12 (a single unit truck) for LON, which represent the tests considered necessary to demonstrate the MASH compliance of the device. The 40-in. single slope CIP concrete barrier with a 20:1 flare rate met MASH requirements and is considered MASH compliant for Test Level (TL) 4. The system is considered suitable for implementation where there is a need to flare the concrete barrier around a fixed object.

Permitted speed decision of single-unit trucks with emergency braking maneuver on horizontal curves under rainy weather.

Under adverse weather conditions, visibility and the available pavement friction are reduced. The improper selection of speed on curved road sections leads to an unreasonable distribution of longitudinal and lateral friction, which is likely to cause rear-end collisions and lateral instability accidents. This study considers the combined braking and turning maneuvers to obtain the permitted vehicle speed under rainy conditions. First, a braking distance computation model was established by simplifying the relationship curve between brake pedal force, vehicle braking deceleration, and braking time. Different from the visibility commonly used in the meteorological field, this paper defines "driver’s sight distance based on real road scenarios" as a threshold to measure the longitudinal safety of the vehicle. Furthermore, the lateral friction and rollover margin is defined to characterize the vehicle’s lateral stability. The corresponding relationship between rainfall intensity-water film thickness-road friction is established to better predict the safe speed based on the information issued by the weather station. It should be noted that since the road friction factor of the wet pavement not only determined the safe vehicle speed but also be determined by the vehicle speed, so we adopt Ferrari’s method to solve the quartic equation about permitted vehicle speed. Finally, the braking and turning maneuvers are considered comprehensively based on the principle of friction ellipse. The results of the TruckSim simulation show that for a single-unit truck, running at the computed permitted speed, both lateral and longitudinal stability meet the requirements. The proposed permitted vehicle speed model on horizontal curves can provide driving guidance for drivers on curves under rainy weather or as a decision-making basis for road managers.

Modeling Freight Vehicle Type Choice using Machine Learning and Discrete Choice Methods

The choice of vehicle type is one of the important logistics decisions made by firms. The complex nature of the choice process is because of the involvement of multiple agents. This study employs a random forest machine learning algorithm to represent these complex interactions with limited information about shipment transportation. The data are from Commercial Travel Surveys with information about outbound shipment transportation. This study models the choice among four road transport vehicle types: pickup/cube van, single-unit truck, tractor trailer, and passenger car. The characteristics of firms and shipments are used as explanatory variables. SHAP-based variable importance is calculated to interpret the importance of each variable, and shows that employment and weight are the most important variables in determining the choice of vehicle type. The random forest model is also compared with the multinomial and mixed logit models. The model prediction results on the validation data are compared. The results show that random forest model outperforms both the multinomial and mixed logit model with an overall increase in accuracy of about 7.8% and 9.6%, respectively.

Assessment of commercial truck driver injury severity based on truck configuration along a mountainous roadway using hierarchical Bayesian random intercept approach

For the last decade, disaggregate modeling approach has been frequently practiced to analyze truck-involved crash injury severity. This included truck-involved crashes based on single and multi-vehicles, rural and urban locations, time of day variations, roadway classification, lighting, and weather conditions. However, analyzing commercial truck driver injury severity based on truck configuration is still missing. This paper aims to fill this knowledge gap by undertaking an extensive assessment of truck driver injury severity in truck-involved crashes based on various truck configurations (i.e. single-unit truck with two or more axles, single-unit truck pulling a trailer, semi-trailer/tractor, and double trailer/tractor) using ten years (2007–2016) of Wyoming crash data through hierarchical Bayesian random intercept approach. The log-likelihood ratio tests were conducted to justify that separate models by various truck configurations are warranted. The results obtained from the individual models demonstrate considerable differences among the four truck configuration models. The age, gender, and residency of the truck driver, multi-vehicles involvement, license restriction, runoff road, work zones, presence of junctions, and median type were found to have significantly different impacts on the driver injury severity. These differences in both the combination and the magnitude of the impact of variables justified the importance of examining truck driver injury severity for different truck configuration types. With the incorporation of the random intercept in the modeling procedure, the analysis found a strong presence (24%–42%) of intra-crash correlation (effects of the common crash-specific unobserved factors) in driver injury severity within the same crash. Finally, based on the findings of this study, several potential countermeasures are suggested.

Impact of Specialized Hauling Vehicles and Emergency Vehicles on Bridge Load Rating

The Federal Highway Administration (FHWA) mandated states to adopt specialized hauling vehicles (SHVs) and emergency vehicles (EVs) in 2013 and 2016, respectively, in the load rating of bridges. Both the AASHTO single-unit trucks (SUs) and EVs are specially configured so that they may result in high load effects and can adversely affect bridge load rating factors. This paper investigates the impacts of rating these vehicles on the states’ bridge load ratings. An extensive literature review of the states’ load rating policies is performed. To determine whether any state can possibly be exempted from the new load ratings for SUs and EVs for Interstate highway bridges, the load effects of various state legal vehicles are analyzed and compared with those of SUs and EVs. The results of the study indicate the inevitability of executing the new load rating analysis for SUs and EVs for the vast majority of states. Weigh-in-motion data are processed to screen the potential EV traffic fleeting on the highway, and the calibrated live load factors are proposed for EVs accordingly. The load effects are found to be smaller than those FHWA originally assigned, improving the rating factors. Lastly, this paper proposes a screening tool to help state agencies to convert the known rating factors to the rating factors of SUs and EVs so that the load rating work can be prioritized for the bridges that are vulnerable to SUs and EVs.

Peer Review: The Volpe/Federal Highway Administration National Vehicle Miles Traveled Forecasting Models

This paper describes the findings of an independent peer review of the modeling tools used by the Volpe National Transportation Systems Center to forecast national vehicle miles traveled (VMT) over the next 30 years. Overall, the VMT forecasting models, which use autoregressive distributed lag models for light-duty vehicle, single-unit truck, and combination truck VMT, work well to estimate travel demand. All model estimations were reviewed, and all models perform well against several validation and testing techniques. The study team was supported by an expert panel selected from academia, government, and industry with experience in econometric methods, transportation and economic data, and modeling methods. The panel reviewed model documentation as well as the report assessing the VMT forecasting models and provided insight into alternative model research. The paper is an effort to synthesize the approaches and the validation methods used. A complementary literature search was also conducted to test the validity and comparability of several estimated variable coefficients. The paper concludes by summarizing the key findings and making recommendations on future model improvements.

Performance-Based Design Framework for Concrete Barriers Subjected to Truck Collision

Abstract A validated computational model is used to investigate the response of concrete barriers subjected to impact by single-unit trucks and tractor-semitrailers with different weights and appro...

Examining Driver Injury Severity in Single-Vehicle Road Departure Crashes Involving Large Trucks

Road departure (RD) crashes are among the most severe crashes that can result in fatal or serious injuries, especially when involving large trucks. Most previous studies neglected to incorporate both roadside and median hazards into large-truck RD crash severity analysis. The objective of this study was to identify the significant factors affecting driver injury severity in single-vehicle RD crashes involving large trucks. A random-parameters ordered probit (RPOP) model was developed using extensive crash data collected on roadways in the state of Kentucky between 2015 and 2019. The RPOP model results showed that the effect of local roadways, the natural logarithm of annual average daily traffic (AADT), the presence of median concrete barriers, cable barrier-involved collisions, and dry surfaces were found to be random across the crash observations. The results also showed that older drivers, ejected drivers, and drivers trapped in their truck were more likely to sustain severe single-vehicle RD crashes. Other variables increasing the probability of driver injury severity have included rural areas, dry road surfaces, higher speed limits, single-unit truck types, principal arterials, overturning-consequences, truck fire occurrence, segments with median concrete barriers, and roadside fixed object strikes. On the other hand, wearing seatbelt, local roads and minor collectors, higher AADT, and hitting median cable barriers were associated with lower injury severities. Potential safety countermeasures from the study findings include installing median cable barriers and flattening steep roadside embankments along those roadway stretches with high history of RD large-truck-related crashes.

Computational modeling and dynamic response of highway bridge columns subjected to combined vehicle collision and air blast

Vehicle impacts into supporting bridge columns can cause significant damage and, when combined with an air blast, could result in, at best, an unsafe situation for the bridge or, at worst, complete collapse. Limited studies have been conducted that investigate the response of bridge substructure units subjected to a combined collision and blast loading. This paper summarized results from a numerical study examining the response of highway bridge columns subjected to a combined vehicle collision and air blast using LS-DYNA, the computational tool used for this research. Numerical models consisting of a circular reinforced concrete (RC) bridge column, its pile cap and pile foundation system, and a surrounding soil volume were developed and validated. Validated models were then subjected to simulated impacts from a Ford F800 Single-Unit Truck (SUT) and air blasts represented using a Multi-Material Arbitrary Lagrangian-Eulerian (MM-ALE) formulation with applied blast loads being taken from the fluid-structure interaction (FSI) algorithm in LS-DYNA. The validated computational modeling technique presented in this study provides a vital source for producing information in the combined collision and blast design field and determining the combined impact-blast behavior of bridge columns. A detailed investigation was performed to determine a critical impact and blast sequence for this research study. Furthermore, numerical simulations of SUT collision with RC column coupled with air blast were conducted considering variations of vehicle velocity, blast scaled distance, and column diameter. The demand and performance of RC bridge columns were examined with representative damage categories identified in this study to provide insights on column design to withstand the collision and blast combination.