Beyond the surface: Exploring the temporally stable factors influencing injury severities in large-truck crashes using mixed logit models.

An analysis of crash data spanning four years (January 1, 2015, to December 31, 2018) from the State of Washington is conducted to investigate factors influencing injury severity outcomes in large truck-involved crashes. The study utilizes a mixed logit model that accounts for unobserved heterogeneity to capture the variation influenced by other variables. Transferability and temporal stability across the years are assessed using the likelihood ratio test. A wide range of attributes, including driver characteristics, vehicle features, crash-related attributes, roadway conditions, environmental factors, and temporal elements, are considered. Despite a significant temporal instability warranted by the likelihood ratio test across the years, twenty-one parameters consistently exhibit stable effects on injury severity over the years of which thirteen are new. The identified stable parameters included over speeding, following too closely, falling asleep, missing/ faulty airbags, head-on collisions, crashes involving two or more than three vehicles, rear-end collisions, lane width, low-light conditions, sag curves, New Jersey barriers, snowy weather, and morning hours. The temporally stable factors affecting injury severities in large truck crashes are crucial in developing the needed to address these crashes. The findings of this study offer valuable insights for researchers, stakeholders in the trucking industry, and policymakers, empowering them to develop targeted policies that not only improve traffic safety but also alleviate associated economic losses.

Electromagnetic Exposure Levels of Electric Vehicle Drive Motors to Passenger Wearing Cardiac Pacemakers.

The number of individuals wearing cardiac pacemakers is gradually increasing as the population ages and cardiovascular disease becomes highly prevalent. The safety of pacemaker wearers is of significant concern because they must ensure that the device properly functions in various life scenarios. Electric vehicles have become one of the most frequently used travel tools due to the gradual promotion of low-carbon travel policies in various countries. The electromagnetic environment inside the vehicle is highly complex during driving due to the integration of numerous high-power electrical devices inside the vehicle. In order to ensure the safety of this group, the paper takes passengers wearing cardiac pacemakers as the object and the electric vehicle drive motors as the exposure source. Calculation models, with the vehicle body, human body, heart, and cardiac pacemaker, are built. The induced electric field, specific absorption rate, and temperature changes in the passenger's body and heart are calculated by using the finite element method. Results show that the maximum value of the induced electric field of the passenger occurs at the ankle of the body, which is 60.3 mV/m. The value of the induced electric field of the heart is greater than that of the human trunk, and the maximum value (283 mV/m) is around the pacemaker electrode. The maximum specific absorption rate of the human body is 1.08 × 10-6 W/kg, and that of heart positioned near the electrode is 2.76 × 10-5 W/kg. In addition, the maximum temperature increases of the human torso, heart, and pacemaker are 0.16 × 10-5 °C, 0.4 × 10-6 °C, and 0.44 × 10-6 °C within 30 min, respectively. Accordingly, the induced electric field, specific absorption rate, and temperature rise in the human body and heart are less than the safety limits specified in the ICNIRP. The electric field intensity at the pacemaker electrode and the temperature rise of the pacemaker meet the requirements of the medical device standards of ICNIRP and ISO 14708-2. Consequently, the electromagnetic radiation from the motor operation in the electric vehicle does not pose a safety risk to the health of passengers wearing cardiac pacemakers in this paper. This study also contributes to advancing research on the electromagnetic environment of electric vehicles and provides guidance for ensuring the safe travel of individuals wearing cardiac pacemakers.

Domestic access to water in a decentralized truck-to-cistern system: a case study in the Northern Village of Kangiqsualujjuaq, Nunavik (Canada).

Municipal water supply through truck-to-cistern systems is common in northern Canada. Household satisfaction and concerns about water services likely impact user preferences and practices. This case study explores household perspectives and challenges with regard to domestic access to water in a decentralized truck-to-cistern system. A case study was conducted in the Northern Village of Kangiqsualujjuaq, Nunavik (Quebec, Canada). A paper-based questionnaire was completed by 65 households (one quarter of the population). Many households (37%) reported not drinking tap water from the truck-to-cistern system. Chlorine taste was a frequently reported concern, with those households being significantly less likely to drink water directly from the tap (p = 0.002). Similarly, households that reported a water shortage in the previous week (i.e., no water from the tap at least once) (33%) were more likely to express dissatisfaction with delivered water quantity (rs = 0.395, p = 0.004). Interestingly, 77% of households preferred using alternative drinking water sources for drinking purposes, such as public tap at the water treatment plant, natural sources or bottled water. The study underscores the importance of considering household perspectives to mitigate the risks associated with service disruptions and the use of alternative sources for drinking purposes.

Mitigating delay due to capacity drop near freeway bottlenecks: Zones of influence of connected vehicles.

We present a novel perspective on how connected vehicles can reduce total vehicular delay arising due to the capacity drop phenomenon observed at fixed freeway bottlenecks. We analytically determine spatial regions upstream of the bottleneck, called zones of influence, where a pair of connected vehicles can use an event-triggered control policy to positively influence a measurable traffic macrostate, e.g., the total vehicular delay at bottlenecks. These analytical expressions are also able to determine the boundaries (called null and event horizons) of these spatial extents, outside of which a connected vehicle cannot positively influence the traffic macrostate. These concepts can help ensure that information is disseminated to connected vehicles in only those spatial regions where it can be used to positively impact traffic macrostates. Some scenarios examined in this study indicate that communication between connected vehicles may be required over a span of several kilometers to positively impact traffic flow and mitigate delays arising due to the capacity drop phenomenon.

Investigation of structural parameters influencing vibration characteristics of heavy-duty truck diesel tanks.

The working conditions of heavy-duty trucks are very complicated as the diesel shaking and resonance problems, which causes weld tears, separators to fall off, and other failures occur. Through experiments and finite element simulation, the natural frequency and vibration mode of a given 400 L diesel tank were calculated to study the influences of structural parameters such as the fill ratio (0.1-0.9), the number of baffle plates (0, 1, 2), the spacing of the plates (240 mm, 400 mm, 560 mm) and the aperture (38 mm, 78 mm, 118 mm) on the modal parameters with the wet mode method. The results of the hammering mode test and the simulation modal analysis agree well with the maximum error is 4.8%; the natural frequency of the diesel tank will increase with fill ratio decrease; the increase of the baffle plate number (0, 1, 2) can effectively increase the first-order natural frequency of the diesel tank, but the change of the natural frequency is not obvious on the higher order; the higher plates spacing has a smaller natural frequency; increasing the aperture will highly increase the natural frequency, 188 mm has better vibration safety.

Investigating the temporal dynamics of motor vehicle collision density patterns in urban road networks - A case study of New York.

INTRODUCTION: Motor vehicle collisions are a leading source of mortality and injury on urban highways. From a temporal perspective, the determination of a road segment as being collision-prone over time can fluctuate dramatically, making it difficult for transportation agencies to propose traffic interventions. However, there has been limited research to identify and characterize collision-prone road segments with varying collision density patterns over time. METHOD: This study proposes an identification and characterization framework that profiles collision-prone roads with various collision density variations. We first employ the spatio-temporal network kernel density estimation (STNKDE) method and time-series clustering to identify road segments with different collision density patterns. Next, we characterize collision-prone road segments based on spatio-temporal information, consequences, vehicle types, and contributing factors to collisions. The proposed method is applied to two-year motor vehicle collision records for New York City. RESULTS: Seven clusters of road segments with different collision density patterns were identified. Road segments frequently determined as collision-prone were primarily found in Lower Manhattan and the center of the Bronx borough. Furthermore, collisions near road segments that exhibit greater collision densities over time result in more fatalities and injuries, many of which are caused by both human and vehicle factors. CONCLUSIONS: Collision-prone road segments with various collision density patterns over time have distinct differences in the spatio-temporal domain and the collisions that occur on them. PRACTICAL APPLICATIONS: The proposed method can help policymakers understand how collision-prone road segments change over time, and can serve as a reference for more targeted traffic treatment.

Effect of eco-driving on commercial motor vehicle driver collision risk.

INTRODUCTION: This study investigates the effect among commercial motor vehicle (CMV) drivers of the adoption of fuel-efficient driving techniques (commonly known as eco-driving) on the odds of being involved in safety-related events. METHOD: For 2,637 long-haul class 8 drivers employed by four carriers in Canada, information on driving style, total distance driven, and safety-related events like collisions, hard-braking, hard-turning, and stability control events were collected for each trip. Three carriers provided driving style-related data from the ISAAC instrument, which provides a score on a 0 to 100 scale that measures the degree to which a driver is using an appropriate amount of engine power according to driving conditions. The fourth carrier provided data on driving style characteristics, including fuel consumption, use of cruise control, and use of top gear. Depending on the carrier, information on speeding, driver age, and years of experience driving a commercial vehicle was also collected. Logit statistical models were developed to estimate the change in odds of a driver experiencing a safety-related event dependent on the measures of driving style. RESULTS: A one-unit increase in the ISAAC score was associated with a 7%, 8%, 8%, and 4% reduction in the odds of having a hard-braking event, hard left-turn event, hard right-turn event, and collision, respectively. For the carrier not employing the ISAAC system, an increase of 10% in the time spent driving in top gear with steady speed near 100 km per hour (km/h) was associated with a substantial 34% decrease in stability control events. In addition, a year increase in the driver's age, as well as a 1% increase in the amount of time spent driving using cruise control, reduced the number of hard-braking events by 9% and 3%, respectively. Conclusion/Practical Applications: The adoption of fuel-efficient driving techniques enhances the safety of CMV drivers.

A high-speed gear reshaping method for electric vehicles combining the effects of input torque and speed variation.

In this study, we introduce an optimization method for high-speed gear trimming in electric vehicles, focusing on variations in input torque and speed. This approach is designed to aid in vibration suppression in electric vehicle gears. We initially use Tooth Contact Analysis (TCA) and Loaded Tooth Contact Analysis (LTCA) to investigate meshing point localization, considering changes in gear tooth surface and deformations due to load. Based on impact mechanics theory, we then derive a formula for the maximum impact force. A 12-degree-of-freedom bending-torsion-axis coupled dynamic model for the helical gear drive in the gearbox's input stage is developed using the centralized mass method, allowing for an extensive examination of high-speed gear vibration characteristics. Through a genetic algorithm, we optimize the tooth profile and tooth flank parabolic modification coefficients, resulting in optimal vibration-suppressing tooth surfaces. Experimental results under various input torques and speeds demonstrate that the overall vibration amplitude is stable and lower than that of conventional gear shaping methods. Specifically, the root mean square of vibration acceleration along the meshing line under different conditions is 58.02 m/s2 and 20.33 m/s2, respectively. The vibration acceleration in the direction of the meshing line is 20.33 m/s2 and 20.02 m/s2 under varying torques and speeds, with 20.33 m/s2 being the lowest. Furthermore, the average magnitude of the meshing impact force is significantly reduced to 5015.2. This high-speed gear reshaping method not only enhances gear dynamics and reliability by considering changes in input torque and speed but also effectively reduces vibration in electric vehicle gear systems. The study provides valuable insights and methodologies for the design and optimization of electric vehicle gears, focusing on comprehensive improvement in dynamic performance.

Feeling unwell of passenger travel by small vehicles and associated risk factors in the North Shewa Zone, Oromiya, Ethiopia.

The current study investigated how and why sociocultural structures, situational conditions, and personal behavioural factors cause passengers to feel ill when travelling by minibuses, drawing on ideas from the social construction theory of illness. A significant objective was to investigate associated risk variables that influence passengers' feelings of illness related to the social environment, addressing their beliefs, meanings, practices, and behaviours. A survey method was used to obtain data from 384 passengers for the study. The results of logistic regression indicated that feeling ill when travelling by minibuses differed from passenger to passenger; then, they had their own set of practical and emotional challenges that had no known medical reason. Compared with male and older passengers, female and younger passengers were more likely to feel ill. Furthermore, stress and role-set effects increased passengers' experiences of feeling ill more than did passengers who had no stress prior to the trip and who had only one role. Additionally, passengers who travelled intermittently, utilized suppression techniques to lessen travel discomfort, and fastened seat belts were less likely to experience symptoms of illness. Passengers who travelled on unsafe roads and used alcohol before travel, on the other hand, were more likely to feel ill than those who travelled on safer roads and did not use alcohol before the trip. The findings suggest that passengers should be aware of predisposing conditions that result in illness, be able to rest before travelling, and use all suppressive methods to reduce or prevent illness while travelling by small buses.

Incorporating transportation costs into the single and multiple items newsvendor problems.

The efficiency and responsiveness of supply chains are vitally dependent on inventory replenishment and transportation decisions. In this paper, we study a supply chain consisting of a single retailer ordering seasonal products within the newsvendor framework. The primary objective of the paper is to investigate the retailer's decision-making process, aimed at determining the optimal replenishment quantities and selecting the appropriate mix and size of the truck fleet. Initially, we formulate a mathematical model where the retailer exclusively manages a limited fleet of its own trucks for inbound transportation of a single seasonal product. In this context, we determine a lower breakeven point for the fixed transportation cost than what has been previously proposed in the literature. Subsequently, we examine a commonly encountered transportation scenario where the retailer has the opportunity to expand its fleet size by leasing trucks from the external market. The outcomes of the numerical example indicates that the flexibility resulting from the utilization of different types of trucks can lead to reduced overall costs. We also address the practical transportation problem of efficiently shipping various seasonal products solely with the retailer's own trucks. For this complex problem, we propose an optimal solution procedure based on Lagrangian method. We show that the joint replenishment of multiple products results in cost savings and enhances utilization of the trucks' capacities.

Injury patterns in motor vehicle collision-youth pedestrian deaths.

OBJECTIVE: The objective of this study was to describe fatal pedestrian injury patterns in youth aged 15 to 24 years old and correlate them with motor vehicle collision (MVC) dynamics and pedestrian kinematics using data from medicolegal death investigations of MVCs occurring in the current Canadian motor vehicle (MV) fleet. METHODS: Based on a systematic literature review, MVC-pedestrian injuries were collated in an injury data collection form (IDCF). The IDCF was coded using the Abbreviated Injury Scale (AIS) 2015 revision. The AIS of the most frequent severe injury was noted for individual body regions. The Maximum AIS (MAIS) was used to define the most severe injury to the body overall and by body regions (MAISBR). This study focused on serious to maximal injuries (AIS 3-6) that had an increasing likelihood of causing death. The IDCF was used to extract collision and injury data from the Office of the Chief Coroner for Ontario (OCCO) database of postmortem examinations done at the Provincial Forensic Pathology Unit (PFPU) in Toronto, Canada, and other provincial facilities between 2013 and 2019. Injury data were correlated with data about the MVs and MV dynamics and pedestrian kinematics.The study was approved by the Western University Health Science Research Ethics Board (Project ID: 113440; Lawson Health Research Institute Approval No. R-19-066). RESULTS: There were 88 youth, including 54 (61.4%) males and 34 (38.6%) females. Youth pedestrians comprised 13.1% (88/670) of all autopsied pedestrians. Cars (n = 25/88, 28.4%) were the most frequent type of vehicle in single-vehicle impacts, but collectively vehicles with high hood edges (i.e., greater distance between the ground and hood edge) were in the majority. Forward projection (n = 34/88, 38.6%) was the most frequent type of pedestrian kinematics. Regardless of the type of vehicle, there was a tendency in most cases for the median MAISBR ≥ 3 to involve the head and thorax. A similar trend was seen in most of the pedestrian kinematics involving the various frontal impacts. Of the 88 cases, at least 63 (71.6%) were known to be engaged in risk-taking behaviors (e.g., activity on roadway). At least 12 deaths were nonaccidental (8 suicides and 4 homicides). Some activities may have been impairment related, because 26/63 (41.3%) pedestrians undertaking risk-taking behavior on the roadway were impaired. Toxicological analyses revealed that over half of the cases (47/88, 53.4%) tested positive for a drug that could have affected behavior. Ethanol was the most common. Thirty-one had positive blood results. CONCLUSION: A fatal dyad of head and thorax trauma was observed for pedestrians struck by cars. For those pedestrians hit by vehicles with high hood edges, which were involved in the majority of cases, a fatal triad of injuries to the head, thorax, and abdomen/retroperitoneum was observed. Most deaths occurred from frontal collisions and at speeds more than 35 km/h.

Study on the risk assessment of Pedestrian-Vehicle conflicts in channelized Right-Turn lanes based on the Hierarchical-Grey Entropy-Cloud model.

Channelized right-turn lanes (CRTLs) in urban areas have been effective in improving the efficiency of right-turning vehicles but have also presented negative impacts on pedestrian movement. Pedestrians experience confusion regarding the allocation of road space when crossing crosswalks within these areas, leading to frequent conflicts between pedestrians and motor vehicles. In this paper, considering the characteristics of pedestrian-vehicle conflicts at channelized right-turn lanes as well as the ambiguity and uncertainty of the causes, a comprehensive assignment combined with a cloud model is proposed as a risk evaluation model for pedestrian-vehicle conflicts. The study established a risk indicator system based on three aspects of the transportation system: pedestrians, motor vehicles, and the road environment. Combining the analytic hierarchy process (AHP), grey relational analysis (GRA), and entropy weighting method (EWM) to get the weights of indicator combinations, and then using the cloud model to realize quantitative and qualitative language transformation to complete the risk evaluation. This study employs specific road segments in Qingdao as a validation case for model analysis. The results indicate that the model's evaluation outcomes exhibited a significant level of agreement with the findings from field investigations during both peak and off-peak periods. It is demonstrated that the model has good performance for the safety assessment of pedestrian-vehicle conflicts at CRTL, and it also reflects the ability of the model to assess fuzzy randomness problems. It provides participation value for urban pedestrian-vehicle safety problems as well as applications in other fields.

Application of an ensemble CatBoost model over complex dataset for vehicle classification.

The classification of vehicles presents notable challenges within the domain of image processing. Traditional models suffer from inefficiency, prolonged training times for datasets, intricate feature extraction, and variable assignment complexities for classification. Conventional methods applied to categorize vehicles from extensive datasets often lead to errors, misclassifications, and unproductive outcomes. Consequently, leveraging machine learning techniques emerges as a promising solution to tackle these challenges. This study adopts a machine learning approach to alleviate image misclassifications and manage large quantities of vehicle images effectively. Specifically, a contrast enhancement technique is employed in the pre-processing stage to highlight pixel values in vehicle images. In the feature segmentation stage, Mask-R-CNN is utilized to categorize pixels into predefined classes. VGG16 is then employed to extract features from vehicle images, while an autoencoder aids in selecting features by learning non-linear input features and compressing representation features. Finally, the CatBoost (CB) algorithm is implemented for vehicle classification (VC) in diverse critical environments, such as inclement weather, twilight, and instances of vehicle blockage. Extensive experiments are conducted using different large-scale datasets with various machine learning platforms. The findings indicate that CB (presumably a specific method or algorithm) attains the highest level of performance on the large-scale dataset named UFPR-ALPR, with an accuracy rate of 98.89%.

[Source Apportionment and Health Risk Assessment of Heavy Metals in Dust Around Bus Stops in Kaifeng City Based on APCS-MLR Model].

In order to reveal the influence of urban transportation systems on the quality of urban ecological environment, this study selected surface dust from bus stops, which is strongly disturbed by transportation, as the research object. The contents of eight heavy metals (V, Cr, Co, Ni, Cu, Zn, Cd, and Pb) in the dust were determined through inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma atomic emission spectroscopy (ICP-ASE). The spatial distribution characteristics and pollution levels of the eight heavy metals in the dust were analyzed using the geo-accumulation index method. A combined qualitative (correlation analysis and principal component analysis) and quantitative (absolute principal component scores-multiple linear regression model (APCS-MLR)) method was used to explore the sources of heavy metals in surface dust near bus stops. The spatial distribution characteristics of heavy metals from different sources were elucidated using the Kriging interpolation method. The health risk assessment model proposed by the United States Environmental Protection Agency was used to evaluate the human health risks. The results showed that the average values of ω(V), ω(Cr), ω(Co), ω(Ni), ω(Cu), ω(Zn), ω(Cd), ω(Pb), and ω(As) in the bus stop surface dust were 68.36, 59.73, 5.81, 19.34, 40.10, 208.32, 1.01, and 49.46 mg·kg-1, respectively. The concentrations of heavy metals (Cd, Zn, Pb, Cu, and Cr) in the dust were all higher than the background values in the surrounding dust, exceeding them by 3.37, 2.70, 2.01, 1.95, and 1.28 times, respectively. The order of the geo-accumulation index for the eight heavy metals was Cd > Zn > Pb > Cu > Cr > V > Ni > Co, with Cd, Zn, Cu, and Pb in the dust indicating mild pollution levels and the others showing no pollution. The source analysis results showed that Cr, Co, and Ni were natural sources, whereas Cu, Zn, Pb, and Cd were traffic sources, and V was derived from a combination of industrial and natural sources. The APCS-MLR results indicated that the average contribution rates of the four sources were as follows:natural source (34.17 %), traffic source (29.84 %), industrial-natural mixed source (14.64 %), and unknown source (21.35 %). The spatial distribution map of the contribution rate of the traffic source was consistent with the trends of traffic volume and bus route density distribution. According to the health risk assessment, the cancer risk and non-cancer risk for children were both higher than those for adults. Cr was the main non-cancer factor, and Cd was the main cancer-causing factor. Natural and traffic sources contributed the most to non-cancer risk and cancer risk, respectively.

Seeing the truck, but missing the cyclist: effects of blur on duration thresholds for road hazard detection.

Drivers must respond promptly to a wide range of possible road hazards, from trucks veering into their lane to pedestrians stepping onto the road. While drivers' vision is tested at the point of licensure, visual function can degrade, and drivers may not notice how these changes impact their ability to notice and respond to events in the world in a timely fashion. To safely examine the potential consequences of visual degradation on hazard detection, we performed two experiments examining the impact of simulated optical blur on participants' viewing duration thresholds in a hazard detection task, as a proxy for eyes-on-road duration behind the wheel. Examining this question with older and younger participants, across two experiments, we found an overall increase in viewing duration thresholds under blurred conditions, such that younger and older adults were similarly impacted by blur. Critically, in both groups, we found that the increment in thresholds produced by blur was larger for non-vehicular road hazards (pedestrians, cyclists and animals) compared to vehicular road hazards (cars, trucks and buses). This work suggests that blur poses a particular problem for drivers detecting non-vehicular road users, a population considerably more vulnerable in a collision than vehicular road users. These results also highlight the importance of taking into account the type of hazard when considering the impacts of blur on road hazard detection.

Flame Retardant Exposure in Vehicles Is Influenced by Use in Seat Foam and Temperature.

Flame retardants (FRs) are added to vehicles to meet flammability standards, such as US Federal Motor Vehicle Safety Standard FMVSS 302. However, an understanding of which FRs are being used, sources in the vehicle, and implications for human exposure is lacking. US participants (n = 101) owning a vehicle of model year 2015 or newer hung a silicone passive sampler on their rearview mirror for 7 days. Fifty-one of 101 participants collected a foam sample from a vehicle seat. Organophosphate esters (OPEs) were the most frequently detected FR class in the passive samplers. Among these, tris(1-chloro-isopropyl) phosphate (TCIPP) had a 99% detection frequency and was measured at levels ranging from 0.2 to 11,600 ng/g of sampler. TCIPP was also the dominant FR detected in the vehicle seat foam. Sampler FR concentrations were significantly correlated with average ambient temperature and were 2-5 times higher in the summer compared to winter. The presence of TCIPP in foam resulted in ∼4 times higher median air sampler concentrations in winter and ∼9 times higher in summer. These results suggest that FRs used in vehicle interiors, such as in seat foam, are a source of OPE exposure, which is increased in warmer temperatures.

Evaluating the reliability of automatically generated pedestrian and bicycle crash surrogates.

Vulnerable Road Users (VRUs), such as pedestrians and bicyclists, are at a higher risk of being involved in crashes with motor vehicles, and crashes involving VRUs also are more likely to result in severe injuries or fatalities. Signalized intersections are a major safety concern for VRUs due to their complex dynamics, emphasizing the need to understand how these road users interact with motor vehicles and deploy evidence-based safety countermeasures. Given the infrequency of VRU-related crashes, identifying conflicts between VRUs and motorized vehicles as surrogate safety indicators offers an alternative approach. Automatically detecting these conflicts using a video-based system is a crucial step in developing smart infrastructure to enhance VRU safety. However, further research is required to enhance its reliability and accuracy. Building upon a study conducted by the Pennsylvania Department of Transportation (PennDOT), which utilized a video-based event monitoring system to assess VRU and motor vehicle interactions at fifteen signalized intersections in Pennsylvania, this research aims to evaluate the reliability of automatically generated surrogates in predicting confirmed conflicts without human supervision, employing advanced data-driven models such as logistic regression and tree-based algorithms. The surrogate data used for this analysis includes automatically collectable variables such as vehicular and VRU speeds, movements, post-encroachment time, in addition to manually collected variables like signal states, lighting, and weather conditions. To address data scarcity challenges, synthetic data augmentation techniques are used to balance the dataset and enhance model robustness. The findings highlight the varying importance and impact of specific surrogates in predicting true conflicts, with some surrogates proving more informative than others. Additionally, the research examines the distinctions between significant variables in identifying bicycle and pedestrian conflicts. These findings can assist transportation agencies to collect the right types of data to help prioritize infrastructure investments, such as bike lanes and crosswalks, and evaluate their effectiveness.

Exploring the challenges faced by Dutch truck drivers in the era of technological advancement.

Introduction: Despite their important role in the economy, truck drivers face several challenges, including adapting to advancing technology. The current study investigated the occupational experiences of Dutch truck drivers to detect common patterns. Methods: A questionnaire was distributed to professional drivers in order to collect data on public image, traffic safety, work pressure, transport crime, driver shortage, and sector improvements. Results: The findings based on 3,708 respondents revealed a general dissatisfaction with the image of the industry and reluctance to recommend the profession. A factor analysis of the questionnaire items identified two primary factors: 'Work Pressure', more common among national drivers, and 'Safety & Security Concerns', more common among international drivers. A ChatGPT-assisted analysis of textbox comments indicated that vehicle technology received mixed feedback, with praise for safety and fuel-efficiency improvements, but concerns about reliability and intrusiveness. Discussion: In conclusion, Dutch professional truck drivers indicate a need for industry improvements. While the work pressure for truck drivers in general may not be high relative to certain other occupational groups, truck drivers appear to face a deficit of support and respect.