Truck Rates Research Articles

Evaluation of advanced emergency braking systems in drowsy driving-related real-world truck collisions.

The effectiveness of advanced emergency braking systems (AEBS) in preventing drowsy driving-related truck collisions remains unclear. We aimed to evaluate the damage-mitigation effect of AEBS on drowsy driving-related collisions involving large trucks using collision rate and damage amount. Data collected by a Japanese transportation company from 1,699 collisions involving 31,107 large trucks over 7 years were analyzed post-hoc. The collision rate (number of trucks with collisions/total number of trucks) and damage amount (total amount of property damage and personal injury) were compared based on whether the collisions were caused by drowsy or non-drowsy driving and whether the trucks were equipped with AEBS or not. For all and non-drowsy driving-related collisions, the collision rate for the 12,887 trucks with AEBS (1.62 and 1.20 collisions/truck/7 years, respectively) was significantly lower than that for the 18,220 trucks without AEBS (1.94 and 1.56 collisions/truck/7 years, respectively) (p=0.04 and p=0.008, respectively). However, for drowsy driving-related collisions, the collision rate did not significantly differ between trucks with and without AEBS. The damage amount in neither type of collision (drowsy vs. non-drowsy) significantly differed between trucks with and without AEBS. Regarding the collision rate of large trucks, AEBS was effective in non-drowsy driving-related collisions, but not in collisions involving drowsy driving. The damage amount was not mitigated for trucks with and without AEBS regardless of the collision type. The limited effect of AEBS for damage-mitigation suggests the need for combined use with other safety-support systems that intervene in driving operations.

Platform-based stable truck matching problem with trailer-swapping mode

This study investigates the platform-based stable truck-matching problem with trailer-swapping mode (STMP-TSM). The TSM is a novel collaborative transportation approach in which trucks participate in trailer swapping, significantly decreasing the rate of empty trucks and reducing transportation costs. In the STMP-TSM, a platform delivers a trailer-swapping scheme that satisfies all participating trucks. Correspondingly, an integer linear programming model is developed to maximize the total truck utility of the STMP-TSM. A specific preference list based on a chain-data structure is meticulously constructed to obtain a stable matching scheme. The preference list enables more generalized truck matching. In addition, a series of acceleration strategies is proposed to expedite the generation of a preference list while effectively reducing its length. An iterative preference-list-trim heuristic algorithm is designed, which strategically trims chains in the preference list to solve the STMP-TSM efficiently. As a benchmark, an integer linear programming model is developed based on the preference list to solve the truck-matching problem using the TSM. Finally, a series of numerical experiments are conducted to evaluate the performance of the proposed algorithm, assess the practicality of the TSM, and analyze the influences of the key parameters.

Labor shortages and agricultural trucking rates

AbstractIn the United States, truck rates for perishable food, the per‐mile rate charged for trucking services to move perishable food from farms to stores, rose substantially in the post‐COVID‐19 pandemic era. We argue that rising truck rates is a signal of a broader shortage of truckers, but the connection between labor shortages, rising truck rates, and a lack of trucking services has yet to be established empirically. In this paper, we develop an empirical examination based on an equilibrium job search, matching, and bargaining framework in which we estimate the role of labor shortages in accelerating driver‐wage growth, and truck rates for agricultural products. We estimate the model by combining US Bureau of Census Current Population Survey data on truck driver wages with USDA‐Agricultural Marketing Service Service data on truck rates to establish the linkage between trucker supply and the demand for trucking services. We find that the COVID‐19 pandemic was responsible for a rise in for‐hire trucker wages of some , a rise in average truck rates of nearly and that the gap between trucker‐job openings and successful matches explains a significant, but small, rise in truck rates.

Prediction of driving stress on high-altitude expressway using driving environment features: A naturalistic driving study in Tibet

Objective Owing to the harsh environment in high-altitude areas, drivers experience significant driving stress. Compared with urban roads or expressways in low-altitude areas, the driving environment in high-altitude areas has distinct features, including mountainous environments and a higher proportion of trucks and buses. This study aims to investigate the feasibility of predicting stress levels through elements in the driving environment. Methods Naturalistic driving tests were conducted on an expressway in Tibet. Driving stress was assessed using heart rate variability (HRV)-based indicators and classified using K-means clustering. A DeepLabv3 model was built to conduct semantic segmentation and extract environment elements from the driving scenarios recorded through a camera next to the driver’s eyes. A decision tree and 4 other ensemble learning models based on decision trees were built to predict driving stress levels using the environment elements. Results Fifty-six indicators were extracted from the driving environment. Results of the prediction models demonstrate that extreme gradient boosting has the best overall performance with the F1 score (harmonic mean of the precision and recall) and G-mean (geometric mean of sensitivity and specificity) reaching 0.855 and 0.890, respectively. Indicators based on the variation rate of trucks and buses have high feature importance and exhibit positive effects on driving stress. Indicators reflecting the proportion of mountain, road, and sky features negatively affect the expected levels of driving stress. Additionally, the mountain feature demonstrates multidimensional effects, because driving stress is positively affected by indicators of the variation rate for mountain elements. Conclusions This study validates the prediction of driving stress using environment elements in the driver’s field of view and extends its application to high-altitude expressways with distinct environmental characteristics. This method provides a real-time, less intrusive, and safer method of driving stress assessment and prediction and also enhances the understanding of the environmental determinants of driving stress. The results hold promising applications, including the development of a driving state assessment and warning module as well as the identification of high-risk road sections and implementation of control measures.

Evaluating the long-term operational performance of a large-scale inland terminal: A discrete event simulation-based modeling approach.

Inland terminals, or dry ports, have played an important role in multimodal transportation networks as transportation hubs that provide connections between seaports and hinterland economies. While important, evaluating the operational performance of a dry port is especially challenging since it depends not only on internal factors, such as the variety and number of container handling equipment (CHE) deployed, but also on other external factors, including changes in transportation policies and container demands experienced by a dry port. To properly evaluate the holistic performance of a dry port while considering all the aforementioned factors, a discrete event simulation (DES) framework is herein developed and applied to the Ladkrabang Inland Container Depot (LICD)-one of the largest dry ports in Southeast Asia-under various operational settings. Despite complicated internal operations, the devised DES framework has shown itself useful in the analyses of LICD, due largely to its flexibility that allows users to include sophisticated operational rules into models. According to our computational results, the current LICD operation is markedly ineffective as the usage rates of all CHE types are relatively low and varied across gate operators-especially the yard truck whose values range between 2.46% and 11.15% on yearly average. We also find that, by redesigning the LICD and its internal operations, the LICD's performance could be substantially enhanced-even with fewer numbers of CHE. Regarding the four CHE types, the reach stacker seems to limit LICD's capability, as its utilization tends to first reach the maximum allowable rate of 75%, while the rubber tyred gantry crane could help boost the usage rate of yard trucks, which, in turn, results in reduced container dwelling times. Nonetheless, the modified LICD could accommodate up to 140% of the current container demand before it experiences operational difficulties induced by the saturation of container flow from rail transportation.

Smart charging strategies for heavy electric vehicles

With emission standards tightening around the world and the popularity of electric cars rising, the electrification of heavy-duty vehicles (e.g., diesel trucks) appears to be a natural progression. For electric truck (ET) charging, a return-to-base strategy with chargers located where trucks begin and end each day, such as trucks used in cargo, freight, and delivery entities, is becoming increasingly viable and attractive. This return-to-base strategy; however, has impacts on the peak demand of a facility which can contribute to an increase in demand charge and requires electrical asset upgrades in certain circumstances. This challenge is different and unique compared to the light-duty passenger vehicles charging challenge (i.e., low distributed charging load and longer charging periods). To address this ET charging challenge, this paper proposes a smart charging system (SCS) that manages the charging process of ETs at their commercial facilities, with the objective of minimizing the peak demand of the aggregate load profile at the facility. Our simulation studies show the effectiveness of the proposed SCS in reducing the peak demand of the facility. The proposed solution can achieve a monthly saving in demand charge of up to 54% compared to uncontrolled charging schemes. • A smart charging system to address the challenges of ET charging at their facilities. • Addresses ET operators' concerns over the increase of demand charges of ET charging. • Accommodates the V2V and V2G technologies in the smart charging system of ETs. • Increases the adoption rate of electric trucks into the different commercial fleets. • The proposed solution can achieve a monthly saving in demand charge of up to 54%.

Research on scheduling optimization of internal trucks for inter-terminal transportation

Based on the operation mode of inter-terminal transportation in a container port, an optimization method of internal trucks scheduling was proposed to cooperate among multiple terminals in a port. In order to minimize the remaining workload of each terminal, a mixed integer programming model for internal trucks scheduling optimization was established by considering the transportation time and energy consumption of internal trucks. The improved genetic algorithm was used to solve the model, and an example was given to verify the validity of the model. The results show that the scheduling optimization model based on inter-terminal transportation mode can effectively reduce the total operating cost of each terminal and improve the utilization rate of internal trucks.

Influence Mechanism of a Bridge Wind Barrier on the Stability of a Van-Body Truck under Crosswind

Understanding the crosswind stability of cars under strong wind loads and research on wind resistance methods is important for improving the safety performance of wind-induced driving on bridges. Taking van-body trucks as the research object, numerical calculation methods and wind tunnel test methods are used to conduct the wind-induced driving safety analyses of van trucks on a cross-sea bridge. The influence of the structural parameters of the barrier-type wind barrier on the aerodynamic characteristics and straight-line driving stability of the trucks on the bridge is studied and analyzed quantitatively. The results show that the decrease in the porosity of the wind barrier can effectively reduce the average wind speed of the bridge deck, and increasing the height of the wind barrier can effectively reduce the wind speed and increase the occlusion height of the bridge deck. The lateral acceleration, yaw rate, and lateral displacement of trucks decrease with the decrease in the porosity of the wind barrier and decrease with the increase in the height of the wind barrier. The research conclusions can not only provide data support for wind-induced driving safety analysis and the wind-resistant design of bridges but also provide a new method to balance the requirements of bridge wind-induced driving safety and bridge wind-induced structure safety.

COVID-19, Truck Rates and Trucking Shortages

COVID-19, Truck Rates and Trucking Shortages

Modeling cooperative driving strategies of automated vehicles considering trucks’ behavior

Modeling impact of trucks on the cooperative driving strategies of automated vehicles would be a challenge. It needs to figure out the behaviors of CACC cars and CACC trucks during the formation and disengagement of CACC strings when the manually driven vehicles, i.e., cars and trucks, are mixed in the traffic flow. Also it requires depicting the behaviors of manually driven cars and trucks under the influence of CACC cooperative strategy that are intended to enhance the CACC strings. Furthermore it needs to investigate the effect of CACC trucks and manually driven trucks on the characteristics of traffic flow respectively. To deal with these problems, we propose a four-lane cellular automaton traffic modeling framework to simulate the interaction between automated vehicles and manually driven vehicles. Numerical results show that trucks do have negative effect on CACC strings’ incorporating, and reduce the traffic capacity. Besides the CACC strings always maintain small scale due to the complicated composition of vehicles. According to the different mixing rate of CACC trucks and manually driven trucks, the managed lane strategy for trucks is proposed aiming to improve the traffic capacity. It indicates that this strategy exhibits different effects depending on the penetrations of CACC trucks, manually driven trucks and CACC cars. However the restriction strategy for trucks is recommended when CACC trucks’ penetration is larger than manually driven trucks’ penetration.

Effectiveness of front crash prevention systems in reducing large truck real-world crash rates

Objective: Forward collision warning (FCW) and automatic emergency braking (AEB) have the potential to prevent or mitigate many large truck crashes. While these systems are known to be effective in passenger vehicles, less is known about their effectiveness in large trucks. The objective of this study was to estimate the effectiveness of these systems in reducing real-world crash rates of large trucks. Methods: Data on Class 8 trucks operating on limited-access highways during 2017–2019 were obtained from SmartDrive Systems. Detailed data on exposure measures and crash circumstances were extracted from video footage by both automated means and manual coding. Crash rates were compared by front crash prevention technology (FCW, AEB, neither), both for all police-reportable crashes overall and for relevant crash types. Results: FCW was associated with a statistically significant 22% reduction in the rate of police-reportable crashes per vehicle miles traveled, and a significant 44% reduction in the rear-end crash rate of large trucks. AEB also was associated with significant reductions—12% overall and 41% for rear-end crashes. Warnings were issued in 31% of rear-end crashes for FCW-equipped trucks. AEB intervened in 43% of rear-end crashes; about two thirds of these interventions involved autobrake activations. On average, speed was reduced by over half between the time of the intervention and impact for both systems. Observed reductions in same-direction sideswipe and roadway departure crashes per mile traveled were smaller in magnitude than those of rear-end crashes; these were consistent with other crash avoidance technologies suspected to be bundled with FCW/AEB in some cases, and very few front crash prevention interventions occurred in these types of crashes. Conclusions: FCW and AEB are effective countermeasures for crashes in which large trucks rear-end other vehicles. Large truck safety is expected to improve as new trucks are increasingly equipped with these systems. FCW has the advantage that some of these systems can be retrofitted to existing trucks, so benefits can be realized sooner and with less investment.

Technical Project of an Electrified Highway in China

This study introduces the system research and technical demonstration of electrified highway and electric freight vehicle technology worldwide. In combination with China's highway freight traffic, overload of some vehicles, and the actual electrified technological situation of different traction locomotives in China, a Chinese electrified highway technical solution about dual or three source (petrol-electric hybrid)-trolley electric truck based on the overhead contact line power supply is proposed. This solution includes the electrification topological structure, highly efficient drive technology, energy intelligent management technology of online charging, intelligent control technology of pantograph, and high voltage power supply technology meeting the charging demand of heavy duty trucks on the electrified highway. These technologies have solved the key technical problems, such as large flow rate of electric trucks, small actual carrying capacity of the power supply network, and easy trip of wayside substations in China. A small-scale road test has been carried out in Suzhou, China. The study results have important guiding significance for the further research and developmental test and demonstration of electrified highway technology in China.

Modeling and Prediction of the Volatility of the Freight Rate in the Roadway Freight Market of China

The trucking sector is an essential part of the logistic system in China, carrying more than 80% of its goods. The complexity of the trucking market leads to tremendous uncertainty in the market volatility. Hence, in this highly competitive and vital market, trend forecasting is extremely difficult owing to the volatility of the freight rate. Consequently, there is interest in accurately forecasting the freight volatility for truck transportation. In this study, to represent the degree of variation of a freight rate series in the trucking sector over time, we first introduce truck rate volatility (TRV). This investigation utilizes the generalized autoregressive conditional heteroskedasticity (GARCH) family of methods to estimate the dynamic time-varying TRV using the real trucking industry transaction data obtained from an online freight exchange (OFEX) platform. It explores the ability of forecasting with and without reestimation at each step of the conventional GARCH models, a neural network exponential GARCH (NN-EGARCH) model, and a traditional forecasting technique, the autoregressive integrated moving average (ARIMA) approach. The empirical results from the southwest China trucking data indicate that the asymmetric GARCH-type models capture the characteristics of the TRV better than those with Gaussian distributions and that the leverage effects are observed in the TRV. Furthermore, the NN-EGARCH performs better in in-sample forecasting than other methods, whereas ARIMA performs similarly in out-of-sample TRV forecasting with reestimation. However, the Diebold–Mariano test indicates the better forecasting ability of ARIMA than the NN-EGARCH in the out-of-sample periods. The findings of this study can benefit truckers and shippers to capture the tendency change of the market to conduct their business plan, increase their look-to-buy rate, and avoid market risk.

Operating conditions calculation during three-dimensional process charts construction of the trucks reliability

The article shows that the existing methods of determining the trucks reliability do not take into account the level of load effect during the operation. Depending on climatic conditions, road surface categories, carrying capacity use conditions and technical service quality, the cumulative value of the load impact will vary and affect the reliability index. The purpose of the work is to identify the relationship between the reliability indices and the cumulative value of the impact, as well as a graphical interpretation of these dependencies using three-dimensional reliability process charts. In the paper the methods determining the accumulation rate of loading effects on vehicles and their impact on the probability of failure-free operation and the failure rate of trucks are used. The sequence of collecting statistical data on failures and operating conditions is given. The theoretical bases determining the operating reliability indices dependence on the loading action level are also discussed. The sequence of three-dimensional reliability process charts construction is presented. The obtained results give the possibility of updating the modes of maintenance for various makes and models of commercial vehicles taking into account the operating conditions.

An estimation of the future adoption rate of autonomous trucks by freight organizations

This paper presents a model to estimate the future adoption of connected autonomous trucks (CATs) by freight transportation organizations. An accurate estimation of the market penetration rate of CATs is necessary to adequately prepare the infrastructure and legislation needed to support the technology. Building upon the theory of Diffusion of Innovations, we develop Bass models for various freight transportation innovations, including improved tractor and trailer aerodynamics, and anti-idling technologies for trucks. The proposed model accounts for heterogeneity between organizations by using a modified Bass model to vary parameters within a designated range for each of the potentially adopting organizations. The results of the paper are Bass models for existing freight organization innovation adoption and estimates of multiple scenarios of CAT adoption over time by freight organizations within the case study region of Shelby County, Tennessee and provide a foundation for organizational innovation adoption research. Our analyses suggest that the market penetration rate of CATs within 25 years varies from nearly universal adoption (i.e., more than 95%) to 20% or less depending on the rate at which autonomous technology improves over time, changes in public opinion on autonomous technology, and the addition of external influencing factors such as price and marketing.

Practical Road-Resistance Functions for Expressway Work Zones in Occupied Lane Conditions

In order to create a practical road-resistance function for work zones under different lane occupation conditions, the expected speed of vehicles was calibrated in the work zone simulation model based on measured data, and simulation models were constructed for the closed half lane and the closed inside lane under different rates of trucks. Based on the statistical theory, the influence of significance of traffic volume and truck ratios for road resistance was analyzed, and a suitable truck ratio was found for the work zone. By using the optimal nonlinear fitting theory, the practical road-resistance function for work zones under different lane occupation conditions was constructed. The results showed that the road resistance is significantly affected by the traffic volume and rate of trucks. Under the same truck ratio, the road resistance linearly increases slowly when the traffic volume is less than the critical traffic volume and rapid increases irregularly when it is greater than the critical traffic volume. Under the same traffic load, the road resistance of the work zone increases with the increase in the rate of trucks, and the difference is not obvious when the traffic volume is less than the critical traffic volume, and increases gradually when it is greater than the critical traffic volume. Through the goodness of fit test and the homogeneity of variance test, the road-resistance function constructed in this paper has high goodness of fit. The practical road-resistance functions constructed in this study could be used to guide the diversion of the rebuilt/expanded highway to ensure traffic safety. Further, the study provides a theoretical basis for the construction of intelligent highway work zones.

Modification of the Highway Capacity Manual two‐lane highway analysis procedure for Spanish conditions

SummaryThe US Highway Capacity Manual (HCM) methodology is used in Spain to evaluate traffic operation and quality of service. The effect of passing manoeuvre on two‐lane highway operational performance is considered through adjustment factors to average travel speeds and percent time spent following. The procedure is largely based on simulations in TWOPAS and passing behaviours observed during US calibrations in the 1970s. It is not clear whether US driving behaviour and vehicles' performance are comparable with Spanish conditions. The objective of this research is to adapt the HCM 2010 methodology to Spanish driver behaviour, for base conditions (i.e. no passing restrictions). To do so, TWOPAS was calibrated and validated based on current Spanish passing field data. The calibration used a genetic algorithm. The case study included an ideal two‐lane highway with varying directional traffic flow rate, directional split and percentage of trucks. The updated methodology for base conditions is simpler than the current HCM 2010 and does not rely on interpolation from tables. Copyright © 2016 John Wiley & Sons, Ltd.

The World Is Not Yet Flat: Transport Costs Matter!

The paper provides evidence of the effects of changes in transport costs on the geographic concentration of industries. The analysis uses micro-level commodity flow data and micro-geographic plant-level data to construct industry-specific ad valorem trucking rates and continuous measures of geographic concentration. The findings show that, controlling for international trade exposure and input-output links, increasing trucking rates are significantly associated with declining geographic concentration. The effect is large: changes in trucking rates explain around 20 percent of the observed decline in geographic concentration of Canadian manufacturing industries between 1992 and 2008.

Transportation Rate Analysis

This research effort developed transportation rates ($/net ton) from origin to destination—including transfer and handling fees—for a non–statistically significant sample of 200 waterborne movements and their alternative least-cost all-overland routings (i.e., rail, truck, or both). The sample consisted of originating, terminating, internal, and through movements on three waterways: Gulf Intracoastal Waterway–East (100 movements), Arkansas River (50 movements), and Red River (50 movements) in the United States. Water line-haul rates obtained from two major barge operators and handling rates obtained from terminal operators were compared with the corresponding rates obtained from the barge costing model (BCM). It was found that the BCM underestimated waterborne charges and that its definitions of cost components were inconsistent with real-world barge operations. Terminal operators interviewed were not aware of any—or could not identify any—specific off-river origins or destinations, so none was associated with any of the movements. Line-haul rail rates were obtained from the Surface Transportation Board's Carload Waybill Samples 2009 and 2010 and Uniform Railroad Costing System. Truck rates were obtained from national and interstate motor carriers. In most cases (171 of 200 movements) the least-cost, most practical land-only line-haul alternative to barge was found to be rail. Overall, this research concluded that barge shipment was the least-cost transportation alternative for every commodity group on the Gulf Intracoastal Waterway–East and Red River and for the majority of commodity groups on the Arkansas River, with annual savings of $2.85 billion. Additional field data collection efforts based on larger, statistically significant samples are needed to implement improvements in the BCM.

Measuring Spatial and Temporal Market Structure in a Transportation Sector: For-hire Grain Trucking on the Alberta-Saskatchewan Border in Canada

While the trucking industry across North America is now fully de-regulated, truck markets and movements are diverse enough that the level of competition in truck transportation almost certainly varies across space, commodities, and even time. Most studies of market power in transportation do not measure or account for spatial or temporal variation in levels of competition, and in addition, it is not clear to what degree such variation affects shippers. For example, there is anecdotal evidence that trucking of certain commodities in Western Canada is characterized by considerable market power that only manifests at certain times of the year. In this light, we examine both spatial aspects as well as the dynamics of rates in the medium-to-long-haul grain trucking sector in West-Central Alberta and East-Central Saskatchewan on the Canadian prairies. We attempt to characterize market power over both space and time within this regional trucking sector. This is done using a unique and detailed data set of trucking rates charged to shippers (farmers) for grain transportation to a common destination (Lloydminster, AB) from the numerous dispersed farms in the region. To frame the unique spatial aspects of this issue, we begin by using geographic information systems (GIS) software to build freight rate contours for this trucking market through space. A set of suppositions regarding the possible shape of these contours as they relate to transportation market structure is also developed. Subsequently, a subset of the data is used to conduct an econometric estimation of short-run freight rate dynamics. These latter estimates reveal evidence of less than competitive transportation markets through time and space. Ultimately, we find that market power in trucking is not persistent within this market, but we do observe uncompetitive pricing behavior at certain times of the year. Given that trucking is deregulated, the latter finding is somewhat unexpected. We suspect that this set of conditions very likely affects trucking rates across more markets and regions than the one examined here.