Impact of In-Cab Alerts on Connected Truck Speed Reductions in Indiana

The influence of risky driving behavior on road traffic accidents (RTAs) is a relationship that requires draconian measures to curtail the rising surge of road traffic accidents among commercial drivers. Any attempt to ignore this will result in continuous loss of lives and properties, thus weakening the global economy, especially in developing countries. The risky driving behaviors of commercial drivers (truck and taxi drivers) in Nigeria require a panacea due to their contribution to RTAs. The study examines the moderating effect of positive driving behavior on commercial truck and taxi drivers’ risky driving behavior and accident involvement relationship. A total of 1823 commercial vehicle drivers (943 taxi drivers and 880 truck drivers) completed the driver behavior questionnaire (DBQ), while the structural equation modeling (SEM) method was used for the analysis. The results indicated a significant moderating effect of positive driving behavior on the risky driving behavior and accident involvement relationship for both commercial truck and taxi drivers in Nigeria. Specifically, the truck drivers had a positive moderating effect, resulting in a decrease in RTAs with an increase in positive driving behavior. In contrast, the taxi drivers had a negative moderating effect. The results suggest that increasing positive driving behavior among truck drivers will enhance their safety, while taxi drivers will need more assessment to identify other risky behaviors that could expose them to more RTAs despite the positive driving behavior. This study will aid decision makers, transport trainers, and driver employers in knowing the importance of enforcing and promoting positive driving behaviors among drivers and include it in driving policy and driver training curricula towards RTA reduction.

The role of cardiovascular CT in occupational health assessment for coronary heart disease: An expert consensus document from the Society of Cardiovascular Computed Tomography (SCCT): Endorsed by the American College of Cardiology (ACC) and the American College of Radiology (ACR)

P29 A Review of the Prevalence of Metabolic Syndrome Components in Commercial Motor Vehicle Truck Drivers

Background:There is no legal requirement for Iranian military truck drivers to undergo regular visual checkups as compared to commercial truck drivers.Objectives:This study aimed to evaluate the impact of drivers’ visual checkups by comparing the visual function of Iranian military and commercial truck drivers.Patients and Methods:In this comparative cross-sectional study, two hundred military and 200 commercial truck drivers were recruited and their Visual Acuity (VA), Visual Field (VF), color vision and Contrast Sensitivity (CS) were assessed and compared using the Snellen chart, confrontation screening method, D15 test and Pelli-Robson letter chart, respectively. A questionnaire regarding driving exposure and history of motor-vehicle crashes (MVCs) was also filled by drivers. Results were analyzed using an independent samples t-test, one-way ANOVA (assessing difference in number of MVCs across different age groups), chi-square test and Pearson correlation at statistical significance level of P < 0.05.Results:Mean age was 41.6 ± 9.2 for the military truck drivers and 43.4 ± 10.9 for commercial truck drivers (P > 0.05). No significant difference between military and commercial drivers was found in terms of driving experience, number of MVCs, binocular VA, frequency of color vision defects and CS scores. In contrast, the last ocular examination was significantly earlier in military drivers than commercial drivers (P < 0.001). In addition, 4% of military drivers did not meet the national standards to drive as opposed to 2% of commercial drivers. There was a significant but weak correlation between binocular VA and age (r = 0.175, P < 0.001). However, CS showed a significantly moderate correlation with age (r = -0.488, P < 0.001).Conclusions:The absence of legal requirement for regular eye examination in military drivers caused the incompetent drivers to be missed in contrast to commercial drivers. The need for scientific revision of VA standard for Iranian drivers is also discussed. The CS measurement in visual checkups of older drivers deserves to be investigated more thoroughly.

Truckers with OSA, Should They Be Driving?

As air transportation demand continues to rise, congestion in air route networks has seriously compromised the safe and efficient operation of air traffic. Few studies have examined the spatiotemporal characteristics of congestion propagation under different time lag conditions. To address this gap, this study proposes a cross-segment congestion-propagation causal time-lag analysis framework. First, to account for the interdependency across segments in air route networks, we construct a point–line congestion state assessment model and introduce the FCM-WBO algorithm for precise congestion state identification. Next, the Multi-Channel Attention DSNG-BiLSTM model is designed to estimate the causal weights of congestion propagation between segments. Finally, based on these causal weights, two indicators—CPP and CPF—are derived to analyze the spatiotemporal characteristics of congestion propagation under various time lag levels. The results indicate that our method achieves over 90% accuracy in estimating causal weights. Moreover, the propagation features differ significantly in their spatiotemporal distributions under different time lags. Spatially, congestion sources tend to spread as time lag increases. We also identify segments that are likely to become overloaded, which serve as the primary receivers of congestion. Temporally, analysis of time-lag features reveals that because of higher traffic flow during peak periods, congestion propagates 36.92% more slowly than during the early-morning hours. By analyzing congestion propagation at multiple time lags, controllers can identify potential congestion sources in advance. They can then implement targeted interventions during critical periods, thereby alleviating congestion in real time and improving route-network efficiency and safety.

The paper presents space occupancy (So~) as a measure of state of traffic congestion in real time. To accurately measure state of traffic, space occupancy (Sor is calculated as the percentage length of highway occupied by vehicles, taking into account length of every vehicle. The paper presents the concepts of Soc and describes the usefulness of utilizing Sor compared to density. The relationship between So~ and density is also determined and a traffic simulation model CELLSIM is used to evaluate the accuracy of each parameter. The parameters were studied using different length of vehicles and mix of traffic. So~ more accurately determines the magnitude of congestion and may be used to set accurate thresholds of traffic congestion. The concepts described can be applied to incident detection in real time control of network traffic flow in Intelligent Transportation Systems.

This paper explores the relationships between travel time index and planning time index, two proven indices used to measure real-time congestion and travel time reliability, and their impacts on truck drivers’ route choices through training of a predictive model based on a machine learning algorithm—eXtreme Gradient Boost (XGBoost). Moreover, this study adopts an interpretable machine learning framework called SHapley Additive ExPlanation (SHAP) in the predictive model to reveal the insights usually hidden inside the machine learning “black box.” The predictive model is trained through a truck trajectory dataset provided by the Maryland Department of Transportation State Highway Administration and INRIX. The classical logistic regression model is adopted as the baseline model. The results show the XGBoost model can better handle nonlinearity and provide more reliable predictions. Through the SHAP framework, the results indicate that mobility and reliability indices and total trip time nonlinearly influence route choices. Truck drivers are more sensitive to real-time congestion information and reliability information when the differences in mobility and reliability indices on candidate routes reach certain thresholds. Moreover, the interaction study on trip time and mobility index shows that truck drivers are more sensitive to real-time congestion information if the candidate routes’ travel time is a larger portion of the total trip time.

This synthesis will be useful to federal and state agencies, commercial truck and bus operators, and others interested in improving commercial vehicle safety. The synthesis provides a state of the practice of commercial driver health and wellness programs. It provides a review of literature on truck and motorcoach driver health issues, highlighting the chief health risks facing commercial drivers; presents an analytical review of literature associating crash causation with functional impairments affecting abilities of commercial motor vehicle drivers to drive safely; describes elements of employee health and wellness programs that could apply to commercial drivers; provides the results of a survey of trucking and motorcoach companies who have already implemented employee health and wellness programs and documents the components that are presently being offered to their drivers; and offers several case studies of successful employee health and wellness programs in the truck and motorbus industries, focusing on the elements that appear to work effectively.

In order to study the differences in the driving behavior of truck drivers, a classified management of truck drivers is implemented. Obtain the vehicle driving data of 51 commercial truck drivers in natural driving conditions through the on-board OBD device, and preprocess the original data, including detecting abnormal values, time processing, filling missing values, deleting parking data, etc. On the basis of standardizing the data of commercial cargo vehicles, the index is reduced by factor analysis to obtain the speed control behavior clusters of target vehicle drivers. By extracting variable speed factors and acceleration factors and clustering them according to factor scores, three types of driver's light, medium and heavy driving behaviors are obtained. Based on the K-means cluster analysis of the data, the AdaBoost algorithm is used to establish a classification model for the safety tendency of commercial truck drivers, and the truck drivers are divided into radical drivers and conservative drivers. First, the factor analysis method is used to extract the indicators of the two directions of speeding and acceleration, and then the K-means algorithm is used to classify from two perspectives, and finally the driver's different driving conditions can be analyzed. In addition, through further screening of all driving behavior indicators through K-means clustering, the adaboost algorithm is finally used to verify and analyze the clustering results to determine driver styles with different tendencies. Data verification classification results show that the average accuracy of the driving tendency classification model of commercial truck drivers based on the AdaBoost algorithm can reach 95.74%, which can effectively distinguish radical truck drivers from conservative truck drivers.KeywordsDriving behaviorOn-board OBD dataK-means clusteringAdaBoost algorithm

Objective: To explore the relation of commercial truck drivers' body mass indexex (BMI) to fatigued driving episodes and involvement in safety-critical events. Methods: One hundred and three professional truck drivers participated in a long-term naturalistic (on-road) driving study whereby vehicle motion data as well as video of the driver and driving environment were gathered continuously. This data set was analyzed to identify safety-critical events as well as fatigued driving episodes using two independent measures of fatigue. Odds ratio analyses were then performed to explore the relative risk of driving while fatigued and involvement in safety-critical events based on driver's BMI classification (obese versus non-obese). Results: Results indicated that of the 103 participating truck drivers, 53.4 percent were obese based on BMI. Odds ratio calculations revealed that obese individuals were between 1.22 (CI = 1.03–1.45) and 1.69 times (CI = 1.32–2.18) more likely than non-obese individuals to be rated as fatigued based on the two measures of fatigue. Other analyses showed that obese individuals were at 1.37 times (CI = 1.19–1.59) greater risk for involvement in a safety-critical event than non-obese individuals. Finally, one of the fatigue measures showed that obese individuals were 1.99 times (CI = 1.02–3.88) more likely than non-obese individuals to be fatigued while involved in an at-fault safety-critical incident. Conclusion: The results of this study support other research in the field of health and well-being that indicate a link between obesity and fatigue, which is a major safety issue surrounding commercial motor vehicle operations given the long hours these drivers spend on the road.

Commercial motor vehicle drivers are at an increased risk for obstructive sleep apnea (OSA). The Federal Motor Carrier Safety Administration (FMCSA) Medical Review Board has recommended that commercial motor vehicle drivers undergo testing for OSA if they have a positive Berlin Questionnaire or a BMI ≥ 30 kg/m(2). We developed an online screening tool based on the Berlin Questionnaire for anonymous use by commercial drivers to assess their risk of OSA prior to their required FMCSA physicals. We based the survey on the Berlin Sleep Questionnaire. The survey was hosted on the Truckers for a Cause Chapter of Alert Well and Keeping Energetic of the American Sleep Apnea Association (TFAC-AWAKE) organization website, and was promoted through the TFAC's XM radio, word of mouth, and trucking industry press contacts. A total of 595 individuals completed the survey. Of these, 55.9% were positive on the Berlin, 78.3% had either hypertension or obesity, 69.6% were obese, 47.6% had a BMI > 33 kg/m(2), and 20.5% reported falling asleep at stoplights. Some commercial drivers willingly assess their OSA risk anonymously online, and a majority of those who do so are obese, have positive Berlin screening questionnaires, and would be required to undergo polysomnography if recommendations made to the FMCSA became regulation. In contrast to reported behavior during actual Commercial Driver Medical Examinations physicals, some commercial drivers will report OSA symptoms if it is "safe" to do so. Sleep health professionals need expedient, non-punitive methods to keep commercial motor vehicle drivers healthy and driving and to raise drivers' awareness of the dangers of drowsy driving and unhealthy lifestyles.

Mobile phone use among truck drivers: The application and extension of the theory of planned behavior

We consider the traffic congestion problem in Gwalior city. To monitor traffic level, previously it required the installation of dedicated measuring device on the road, on vehicles or the tracking of mobile for monitoring the traffic. Moreover, earlier efforts seem to be related with traffic flow patterns of the developed countries. After monitoring, we couldn't sync up with all devices to find traffic congestion in real time. To find least congested path, we have introduced a system that detects traffic congestion in real time and provide a least congestion free path to the commuter. In this paper, we concentrate specifically on Smartphone to gather information and recommend the least congested route to user's destination. System requires Global Positioning Services (GPS), Google Map services and Server to determine the congestion of every node in map.

We consider the traffic congestion problem in Gwalior city. To monitor traffic level, previously it required the installation of dedicated measuring device on the road, on vehicles or the tracking of mobile for monitoring the traffic. Moreover, earlier efforts seem to be related with traffic flow patterns of the developed countries. After monitoring, we couldn't sync up with all devices to find traffic congestion in real time. To find least congested path, we have introduced a system that detects traffic congestion in real time and provide a least congestion free path to the commuter. In this paper, we concentrate specifically on Smartphone to gather information and recommend the least congested route to user's destination. System requires Global Positioning Services (GPS), Google Map services and Server to determine the congestion of every node in map.