Application of Connected Truck Data to Evaluate Spatiotemporal Impact of Rest Area Closures on Ramp Parking

Ensuring adequate access to truck parking is critical to the safe and efficient movement of freight traffic. There are strict federal guidelines for commercial truck driver rest periods. Rest areas and private truck stops are the only places for the trucks to stop legally and safely. In locations without sufficient parking areas, trucks often park on interstate ramps, which create safety risks for other interstate motorists. Historically, agencies have employed costly and time intensive manual counting methods, camera surveillance, and driver surveys to assess truck parking. Connected truck data, available in near real-time, offers an efficient alternative to practitioners to assess truck parking patterns and identify areas where there may be insufficient safe parking spaces. This paper presents a case study of interstate I-70 in east central Indiana and documents the observed spatiotemporal impacts of a rest area closure on truck parking on nearby interstate ramps. Results showed that there was a 28% increase in parking on ramps during the rest area closure. Analysis also found that ramps closest to the rest area were most impacted by the closure, seeing a rise in truck parking sessions as high as 2.7 times. Parking duration on the ramps during rest area closure also increased drastically. Although it was expected that this would result in increased parking by trucks on adjacent ramps, this before, during, after scenario provided an ideal scenario to evaluate the robustness of these techniques to assess changing parking characteristics of long-haul commercial trucks. The data analytics and visualization tools presented in this study are scalable nationwide and will aid stakeholders in informed data-driven decision making when allocating resources towards improving the nations commercial vehicle parking infrastructure.

Excessive Drowsiness among Truck Drivers in Benin in 2023: Associated Factors and Risk of Crashes Occurrence

Introduction: Over-drowsiness is a condition with serious consequences, including road accidents. The condition, however, is often ignored both by carers as well as victims themselves. The aim of the present study was to investigate the factors associated with excessive drowsiness in Cotonou, Benin 2023, along with its influence on the occurrence of crashes among truck drivers. Methods: This was a descriptive and analytical cross-sectional study, held from March 13 to April 10, 2023, focusing on large truck drivers over 18 years of age, selected by convenience from parking lots in and around the city of Cotonou. Data collected using questionnaires on socio-demographic and behavioral factors, sleeping habits and working conditions were processed using Stata 15.0 software. Excessive drowsiness was defined by a score above 10 on the Epworth scale. Associated factors were found by multiple logistic regression, at a threshold of 0.05. Results: Altogether 304 drivers, all male and aged 35.98 ± 8.42 years, were surveyed. The prevalence of excessive drowsiness was 29.2%. The associated factors identified were not practicing sport OR = 2.27, CI95% = [1.33;3.86], p = 0,002;sleep duration per working day OR = 1.82;CI95% = [1.06;3.11], p = 0,027 and average distance travelled per day OR = 3.40;CI95% = [1.53;7.56], p = 0,003. Excessive drowsiness was associated with a 1.73-fold increased risk of road accidents (CI95% [1.04;2.87];p = 0.03). Conclusion: Communicating excessive drowsiness and its associated factors to all the stakeholders in the haulage chain is essential to help prevent road accidents.

A comparative study on kinetics and dynamics of two dump truck lifting mechanisms using MATLAB simscape

In this paper,two lifting mechanism models with opposing placements,which use the same hydraulic hoist model and have the same angle of 50°,have been developed.The mechanical and hydraulic simulation models are established using MATLAB Simscape to analyze their kinetics and dynamics in the lifting and holding stages.The simulation findings are compared to the analytical calculation results in the steady state,and both methods show good agreement.In the early lifting stage,Model 1 produces greater force and discharges goods in the container faster than Model 2.Meanwhile,Model 2 reaches a higher force and ejects goods from the container cleaner than its counterpart at the end lifting stage.The established simulation models can consider the effects of dynamic loads due to inertial moments and forces generated during the system operation.It is crucial in studying,designing,and optimizing the structure of hydraulic-mechanical systems.

Intermodal Competition: Cargo Airships versus Long-Haul Trucking for Perishable Commodities

Intermodal competition changes with changes in technology, economics, and environmental concerns. Trucks and airships are generally considered not to be competitors, but this depends on the distance of haul. The tonne-kilometer cost of trucking rises much more quickly with distance than it does the cost of a cargo airship. At some distance, the two modes are direct substitutes. The costs of the Mexico-Canada refrigerated truck supply chain are compared with the costs of a 100t-lift, electrically-powered airship. The flight characteristics of the Hindenburg Zeppelin are used as a model for a modern cargo airship. The supply chain cost of trucking tomatoes is used to test the theorical proposition. The cost difference works out to about US10￠/kg (5￠/lb) advantage for trucking Mexican tomatoes to Canada. However, this cost disadvantage of the airship could be made up by their vibrationless ride, better air circulation and one-day service versus four days by truck. This alternative form of transportation could have a positive impact on worldwide north-south distribution of food. Airships can overcome trade barriers and distance to open new markets for perishable food exports. In addition, they would reduce the carbon emissions of transport. Canada imports 160,000 refrigerated truckloads of fruits and vegetables by from the southern US and Mexico. With an average driving distance of 3,000 km, these trucks emit 606,000 MT of CO2 annually. Airships powered by hydrogen fuel cells would have zero-carbon emissions. Markets are not yet incorporating the environmental advantage of airships in any freight comparison, but inevitably this will be important.

Quantifying the Impact of In-Cab Alerts on Truck Speed Reductions in Ohio

In-cab alerts warn commercial vehicle drivers of upcoming roadway incidents, slowdowns and work zone construction activities. This paper reports on a study evaluating the driver response to in-cab alerts in Ohio. Driver response was evaluated by measuring the statistical trends of vehicle speeds after the in-cab alerts were received. Vehicle speeds pre and post in-cab alert were collected over a 47 day period in the fall of 2023 for trucks traveling on interstate roadways in Ohio. Results show that approximately 22% of drivers receiving Dangerous Slowdown alerts had reduced their speeds by at least 5 mph 30 seconds after receiving such an alert. Segmenting this analysis by speed found that of vehicles traveling at or above 70 mph at the time of alerting, 26% reduced speeds by at least 5 mph. These speed reductions suggest drivers taking actional measures after receiving alerts. Future studies will involve further analysis on the impact of the types of alerts shown, roadway characteristics and overall traffic conditions on truck speeds passing through work zones.

Seroprevalence Survey of HIV and Hepatitis B Virus and Behavioral Characteristics among Heavy Truck Drivers along Port Sudan-Khartoum Highways

The prevalence of human immunodeficiency virus (AIDS) and hepatitis B virus among heavy truck drivers and their assistants has been well documented globally in correlation with their behavioral characteristics. The present study aimed to screen for human immunodeficiency virus (HIV), hepatitis B virus (HBV), and behavioral characteristics among heavy truck drivers in Port Sudan. A cross-sectional study was conducted on 274 heavy truck drivers and their assistants who used the highway Port Sudan-Khartoum in Port Sudan city during 2019-2021. Data on behavioral characteristics and substance use habits were collected using a structured questionnaire, and an ELISA test was used to screen for HIV and HBV infections in the study participants. The chi-square test, odds ratio, and confidence intervals were used to find the association between behavioral characteristics and seropositive HIV/HBV. Of the 274 enrolled participants, the seroprevalence rates of HIV were 2.7% and HBV was 23.7%. Ninety-four (34.3%) of them had a history of high-risk sexual behavior outside of marriage;only two (0.7%) used condoms;14.2% of participants reported alcohol use;and 1.1% reported drug use. Univariate analysis revealed that having a sex history outside of marriage with ≥1 sex partner and never using a condom with a spouse or casual partner were significant risk factors for HIV and HBV among drivers. Fortunately, we found that most of the drivers reported low alcohol and drug use. Concerning this study, the seroprevalence of HIV and HBV is highly associated with a history of having sex outside of marriage and sexual behavior among truck drivers and assistances. Additional studies are needed to further investigate other STIs and behavioral characteristics associated with factors in truck drivers/assistance in different truck stop regions in Sudan.

Real-Time Co-optimization of Gear Shifting and Engine Torque for Predictive Cruise Control of Heavy-Duty Trucks

Fuel consumption is one of the main concerns for heavy-duty trucks.Predictive cruise control(PCC)provides an intriguing opportunity to reduce fuel consumption by using the upcoming road information.In this study,a real-time implementable PCC,which simultaneously optimizes engine torque and gear shifting,is proposed for heavy-duty trucks.To minimize fuel consumption,the problem of the PCC is formulated as a nonlinear model predictive control(MPC),in which the upcoming road elevation information is used.Finding the solution of the nonlinear MPC is time consuming;thus,a real-time implementable solver is developed based on Pontryagin’s maximum principle and indirect shooting method.Dynamic programming(DP)algorithm,as a global optimization algorithm,is used as a performance benchmark for the proposed solver.Simulation,hardware-in-the-loop and real-truck experiments are conducted to verify the performance of the proposed controller.The results demonstrate that the MPC-based solution performs nearly as well as the DP-based solution,with less than 1%deviation for testing roads.Moreover,the proposed co-optimization controller is implementable in a real-truck,and the proposed MPC-based PCC algorithm achieves a fuel-saving rate of 7.9%without compromising the truck’s travel time.

A human-sensitive frequency band vibration isolator for heavy-duty truck seats

In this study,a human-sensitive frequency band vibration isolator(HFBVI)with quasi-zero stiffness(QZS)characteristics for heavy-duty truck seats is designed to improve the comfort of heavy-duty truck drivers on uneven roads.First,the analytical expressions for the force and displacement of the HFBVI are derived with the Lagrange equation and d'Alembert's principle,and are validated through the prototype restoring force testing.Second,the harmonic balance method(HBM)is used to obtain the dynamic responses under harmonic excitation,and further the influence of pre-stretching on the dynamic characteristics and transmissibility is discussed.Finally,the experimental prototype of the HFBVI is fabricated,and vibration experiments are conducted under harmonic excitation to verify the vibration isolation performance(VIP)of the proposed vibration isolator.The experimental results indicate that the HFBVI can effectively suppress the frequency band(4-8 Hz)to which the human body is sensitive to vertical vibration.In addition,under real random road spectrum excitation,the HFBVI can achieve low-frequency vibration isolation close to 2 Hz,providing new prospects for ensuring the health of heavy-duty truck drivers.

A Predictive Energy Management Strategies for Mining Dump Trucks

The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).

Reducing Electrical Consumption in Stationary Long-Haul Trucks

On average, long-haul trucks in the U.S. use approximately 667 million gallons of fuel each year just for idling. This idling primarily facilitates climate control operations during driver rest periods. To mitigate this, our study explored ways to diminish the electrical consumption of climate control systems in class 8 trucks through innovative load reduction technologies. We utilized the CoolCalc software, developed by the National Renewable Energy Laboratory (NREL), which integrates heat transfer principles with extensive weather data from across the U.S. to mimic the environmental conditions trucks face year-round. The analysis of the CoolCalc simulations was performed using MATLAB. We assessed the impact of various technologies, including white paint, advanced curtains, and Thinsulate insulation on reducing electrical demand compared to standard conditions. Our findings indicate that trucks operating in the eastern U.S. could see electrical load reductions of up to 40%, while those in the western regions could achieve reductions as high as 55%. Such significant decreases in energy consumption mean that a 10 kWh battery system could sufficiently manage the HVAC needs of these trucks throughout the year without idling. Given that many long-haul trucks are equipped with battery systems of around 800 Ah (9.6 kWh), implementing these advanced technologies could substantially curtail the necessity for idling to power air conditioning systems.

基于TruckSim矿用汽车转向工况侧倾运动特性分析

车辆发生转向时,由于离心力的作用而产生侧倾运动,空载高速转向时该部分作用将更为明显,进而影响车辆平稳运输。根据悬挂系统的特性差异,对所研究矿用汽车前后悬挂的侧倾中心和侧倾轴进行分析;基于以上分析,对转向时的侧倾运动进行分析,获取关键参数的变化模型;根据理论分析结果,基于Matlab/Simulink建立车辆转向工况表征侧倾运动的分析模型;根据悬挂特性和转向特性,分别建立分析模型,与车辆多体动力学模型联合,基于TruckSim建立整车转向特征分析模型;分析在固定转向盘稳态回转试验时,车辆的行驶轨迹;对空载高速转向工况下,车辆内外轮转角特征曲线,悬挂刚度、位移、侧倾刚度等参数变化进行分析;对比分析考虑转向侧倾与否的转角特征及偏差与理论分析之间的差异,以验证分析的可靠性。结果可知:车辆内外侧悬挂缸出现伸缩跳动,此时出现了附加转向效应叠加在车轮上;车轮需要较大的侧偏角,满足侧向力的需要;此时,整车的转向角关系不单单满足Ackerman定理,还增加了轮胎的侧偏角;未考虑转向侧倾时,转角偏差值随着转角的增加而不断增大,最大偏差达到了0.4223°,而考虑时,转角偏差的目标值为0.3618°。因此表明,所建立的分析模型具有较好的分析结果,能够更精确的反应车辆转向过程中,侧倾运动各部分参数的变化,为此类研究提供参考。

A Comparative Study on the Truck Frame Stiffness with Solid and Beam Element FEA Models

Truck frames should be designed and fabricated with enough rigidity to avoid excessive deflections. Finite element analysis (FEA) plays an important role in all stages of frame designs. While being accurate, 3D solid element FEA models are built upon frame configuration details which are not feasible in the preliminary design stage, partially because of limited available design data of frames and heavy computation costs. This research develops 1D beam element FEA models for simulating frame structures. In this paper, the CAD model of a truck frame is first created. The solid element FEA analysis, which is adopted as the baseline in this study, is subsequently conducted for the stiffness of the frame, Next, beam element FEA analysis is performed for validating the feasibility of the beam element FEA model by comparing the results from the solid and beam element FEA models. It is found that the beam element FEA model can predict the frame stiffness with acceptable accuracy and reduce the computation cost significantly.

Cycle Time Analysis of Open Pit Mining Dump Trucks

This study demonstrates a practical cycle time analysis of dump truck haulage system of “Ukhaa Khudag” open-pit coal mine located in Umnugobi Province, Mongolia. It examines the possibility of minimizing the cycle time of the haulage system as well as factors impacting the speed of the dump truck. The current study divides the open pit mine road for the dump trucks into five sections which are bench road, ramp, surface road, dump road uphill, and dump road. Meanwhile, it investigates the influence of the length, the grade, and the rolling resistance of the road section on the cycle time. The data is analyzed using mathematical regression methods via Microsoft Excel program. For each of the five road sections, we compare the statistical calculations of three regression models: linear, quadratic and exponential;thus, a total of thirty regression models are obtained in this research. Accordingly, the cycle time for each road section is predicted by the most accountable model. The loaded and empty direction of the movement is measured and calculated for each road section, and it appears that the difference between the calculated mean value and the actual cycle time of the models is 0.82 seconds with a relative error of 2.51 percent.

重卡发展技术路线及电气化进程探讨

近年来,中国重卡产销量进入震荡下行和深度调整阶段,动力构成优化速度加快。在“双碳”目标和汽车电动化两大国家战略背景下,中国重卡正在经历从燃油车向电动、氢重卡的逐渐转型。各技术路线的经济性直接影响其发展速度和发展规模,不同技术路线的重卡在实际使用过程中各有优势,政策层面对各技术路线的发展导向和支持力度也明显不同。未来中国重卡动力构成优化速度将明显加快,预计短期内LNG重卡依然是替代柴油重卡的主力,电动重卡销量将明显提升,氢燃料电池重卡继续保持小规模增长,并有望于“十五五”期间取得明显的技术突破。建议沿着油-气(LNG为主)-电-氢的道路稳步推进重卡的电气化进程。

Design and Optimization of New Smoke Exhaust Pipe for Mining Trucks

In order to improve the maintenance efficiency,extend the use time,ensure that the exhaust emission meets the standard,forthe 830E truck heating bucket exhaust pipe design defects,the current single smoke exhaust system is transformed into atime period,convertible smoke exhaust system.After the transformation,it can not only realize the side row to prevent direct corrosion of the box bucket in summer,but also realize the heating of the box bucket at low temperature in winter to prevent snow and ice and frozen blocks from sticking to the box bucket and the materials transported.After the transformation can save a lot of manpower,material resources,financial resources,improve the service life.

基于车联网大数据的重型货车载重估计方法

针对当前货车载重计算方法普遍存在的成本高昂及泛化性能不明确的问题,提出一种创新的重型货车载重估计方法,方法融合了车辆行驶动力学理论与机器学习算法,通过有监督学习,利用高速通行大数据对模型进行训练与验证.首先采用聚类分析,确定车辆空、半、满载判断阈值,为后续的计算提供了重要依据.随后,利用随机森林算法训练分类模型,用以判断车辆在一段行驶过程中的基本载重情况.在此基础上,进一步在车辆行驶数据中筛选出稳定行驶的小片段,根据车辆系统动力学理论,对这些小片段车重进行计算.最后,根据载重状态的判断结果,对小片段车重结果进行筛选与计算,得到最终车辆载重计算结果.研究表明,在高速通行大数据的验证下,该方法对于空载及满载状态下趟次车重计算结果的整体平均绝对百分比误差(mean absolute percent error,MAPE)均可控制在10%以内,展现了较高的准确性.相比于现有技术,由于该方法无需安装额外传感器,对数据采集、存储、运算设备的要求也相对较低,因此在成本方面具有显著优势.在交通监管、物流运输、基于大数据的产品开发方面具有快速广泛推广的潜力.

网联自动驾驶货车编队规划与控制研究综述

物流运输联盟内开展货车编队运行,有望成为未来物流运输降低运营成本的新形态。本文以网联自动驾驶货车编队规划与控制为研究对象,通过文献检索来分析网联自动驾驶货车编队的关键技术与研究现状。从市场应用角度,分析目前实现货车编队市场应用面临的挑战,以及相关利益方之间的成本分配问题。从技术方法视角,围绕网联自动驾驶编队规划与运作控制问题,解读相关技术内涵与实现方法。通过评述网联自动驾驶编队技术研究现状,总结该领域未来研究方向,旨在为研究者掌握该方向发展脉络提供参考。

车辆与无人机协同配送路径规划问题研究进展

随着民用无人机技术的不断发展,无人机速度快、通行能力强等优势更加凸显。且无人机已经被一些企业应用到了物流配送当中。最近,一种车辆与无人机协同进行配送的模式在物流运输领域得到了广泛关注,国内外的学者针对车辆与无人机协同配送路径规划问题进行了大量的研究。首先,本文从优化目标以及约束两个方面对车辆与无人机协同配送路径规划模型进行了梳理。然后,对现有的车辆与无人机协同配送路径规划算法进行了分类总结。最后,探讨了车辆与无人机协同配送路径规划问题相关的研究热点与方向。

地下矿道无人车可行驶区域检测算法

地下矿道可行驶区域检测是地下矿山自动驾驶系统的关键感知技术,然而地下矿道光照强度低、工况复杂的特点给该任务带来极大挑战。鉴于此,本文提出一种地下矿道可行驶区域检测算法。首先,为解决地下矿道细节退化导致图像特征难以提取的问题,提出一种双分支特征提取骨干网络;然后,针对地下矿道可行驶区域检测不完整问题,提出一种自适应多尺度空间空洞池化金字塔特征增强模块;最后,为解决地下矿道边界提取不准确的问题,设计一种双分支通道注意力机制融合模块。在自制矿道可行驶区域数据集上进行实验,相较于Deeplabv3+、UNet、DDRNet-23、PIDNet,本文算法取得最佳效果,在MIoU分数上分别提升2.07、2.39、1.87、1.92个百分点,在mAcc分数上分别提升1.78、2.45、1.84、1.86。本文算法已成功应用于地下无人驾驶矿车,验证了其在真实矿道场景下的有效性。

基于双曲嵌入的露天矿区暗光环境下道路多目标检测模型

露天矿环境特殊,道路场景复杂多变,在光照不足时会导致矿区道路多目标识别不清、定位不准,进而影响检测效果,给矿区无人矿用卡车的安全行驶带来严重安全隐患。目前的道路障碍物检测模型不能有效解决矿区暗光环境对模型检测效果的影响,同时对矿区小目标障碍物的识别也有较大误差,不适用于矿区特殊环境下障碍物的检测与识别。针对上述问题,提出了一种基于双曲嵌入的露天矿区暗光环境下多目标检测模型。首先,在模型的图像预处理阶段引入卷积神经网路Retinex-Net对暗图像进行增强,提高图像清晰度;然后,针对数据集中特征过多而无重点偏好的问题,在加强特征提取部分添加全局注意力机制,聚集3个维度上更关键的特征信息;最后,在检测模型预测阶段引入双曲全连接层,以减少特征丢失,并防止过拟合现象。实验结果表明:(1)基于双曲嵌入的露天矿区暗光环境下道路多目标检测模型不仅对露天矿区暗光环境下的大尺度目标具有较高的分类与定位精度,对矿用卡车及较远距离的小尺度目标即行人也可准确检测与定位,能够满足无人矿用卡车在矿区特殊环境下驾驶的安全需求。(2)模型的检测准确率达98.6%,检测速度为51.52帧/s,较SSD、YOLOv4、YOLOv5、YOLOx、YOLOv7分别提高20.31%,18.51%,10.53%,8.39%,13.24%,对于矿区道路上的行人、矿用卡车及挖机的检测精度达97%以上。