Proportion Of Trucks Research Articles

Low-Carbon Route Optimization Model for Multimodal Freight Transport Considering Value and Time Attributes

As the international community increasingly focuses on climate change, optimizing low-carbon transportation routes in the multimodal freight transport system has become a pressing issue. However, due to the variability in cargo properties and the influence of various factors on transportation route decisions, formulating a low-carbon and economical multimodal freight transport plan remains a significant challenge. To address the issue, this study considered cargoes with different attributes in terms of both value and time attributes. Triangular fuzzy numbers were employed to represent the uncertain demand for cargo, with confidence levels introduced for clarification. A low-carbon route decision optimization model for multimodal freight transport was established to minimize the combined transportation carbon emission and time costs. The catastrophe adaptive genetic algorithm, based on Monte Carlo sampling, was employed to solve the model using arithmetic examples. Finally, parameter sensitivity analysis revealed that adjustments to carbon tax values and changes in the proportion of electric trucks and green electricity supply had the most significant impact on the low-carbon route decision-making plan for multimodal freight transport. For low value-added and timeliness-strong cargo, a 60% increase in carbon tax value shifted the mode of transportation from road to railway. When the carbon tax increased by more than 140%, the transportation mode shifted from railway to waterway. Additionally, when the proportion of electric trucks and green electricity supply both exceeded 80%, the transportation mode between some city nodes shifted from railway to road. When these proportions increased beyond 90%, road transportation became the predominant mode.

Heterogeneous traffic flow of expressway with Level 2 autonomous vehicles considering moving bottlenecks

With the development of autonomous driving technology, autonomous vehicles (AV) will gradually replace human-driven vehicles (HDV), but it will still be a mixed traffic flow of autonomous vehicles with different levels of automation for a long time. In addition to cars, trucks regarded as moving bottleneck are also an important part of traffic flow of expressway. In this paper, the elimination rule of moving bottleneck is introduced into the traffic flow model, and the improved hybrid traffic flow cellular automata model is proposed. Next, through numerical simulation, the existence and influence of moving bottleneck caused by heavy duty trucks are hypothesized and verified. The simulation results show that with the proportion of trucks increases, the influence range of moving bottleneck expands, resulting in the decrease of capacity and traffic congestion. Then, the impact of Level 2 autonomous vehicles (hereinafter referred to as L2 AVs) on the traffic flow of moving bottlenecks is explored. The simulation results show that with the proportion of L2 AVs increases, traffic capacity increases, and traffic congestion ratio at medium and high density decreases. Under medium density, the average velocity fluctuates greatly, while under high density, the average velocity decreases due to limitation of density. In summary, L2 AVs can alleviate influence of moving bottlenecks, increase traffic capacity, improve traffic congestion, and lift road safety. The cellular automata model of traffic flow including moving bottleneck proposed in this paper is based on the actual road situation, considering the fact that most of autonomous vehicles on the expressway currently are L2 AVs, and moving bottleneck is mainly caused by heavy trucks. This research contributes to an in-depth analysis of L2 AVs on heterogeneous traffic flows under moving bottleneck environment.

Interpretable machine learning for evaluating risk factors of freeway crash severity

Machine learning (ML) models are widely employed for crash severity modelling, yet their interpretability remains underexplored. Interpretation is crucial for comprehending ML results and aiding informed decision-making. This study aims to implement an interpretable ML to visualize the impacts of factors on crash severity using 5 years of freeways data from Iran. Methods including classification and regression trees (CART), K-nearest neighbours (KNNs), random forest (RF), artificial neural network (ANN) and support vector machines (SVM) were applied, with RF demonstrating superior accuracy, recall, F1-score and ROC. The accumulated local effects (ALE) were utilized for interpretation. Findings suggest that light traffic conditions ( volume / capacity < 0.5 ) with critical values around 0.05 or 0.38, and higher proportion of large trucks and buses, particularly at 10% and 4%, are associated with severe crashes. Additionally, speeds exceeding 90 km/h, drivers younger than 30 years, rollover crashes, collisions with fixed objects and barriers, nighttime driving and driver fatigue elevate the likelihood of severe crashes.

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.

A hybrid approach for fatigue assessment of reinforced concrete bridge deck considering realistic bridge-traffic interaction

Reinforced concrete (RC) bridge decks are directly exposed to daily traffic loads and often experience surface cracking caused by excessive stress or fatigue accumulation. The bridge deck fatigue performance over traffic loads needs to be assessed based on accurate dynamic interaction analysis of the bridge and realistic moving vehicles. Most of existing studies on fatigue assessment focus on either the bridge global response without sufficient details about the bridge deck, or refined bridge deck modeling without considering the full dynamic interaction between moving traffic and the bridge structure realistically. A hybrid fatigue assessment approach is developed by combining mode-based global bridge-traffic dynamic interaction analysis, finite-element (FE)-based refined bridge deck model, and fatigue assessment method directly based on the vehicle loads and shear strength of the bridge deck. The proposed approach is demonstrated with a typical 3-span concrete bridge under realistic traffic and road surface conditions. Based on the dynamic interaction and stress analysis results, the fatigue damage factor is further investigated with different road surface roughness levels and heavy truck proportions. It is found that the proposed analytical approach provides a useful tool to predict the bridge deck response and potential fatigue damage under realistic traffic flow.

The “Visual‐Behavior” Chain and Risk Prediction Model for Sedan Drivers Under the Influence of Container Trucks: A Case Study of Yangshan Port Freight Corridor

With the development of the Shanghai International Shipping Center, the diversity of vehicle types on the highways and arterial roads near Yangshan port is continually increasing. Within such a container port corridor, large container trucks are primarily utilized for mainline transportation. Their larger size and significant inertia would increase psychological pressure on sedan drivers, and elevate their behavior risk. To investigate the effects of container trucks on drivers’ visual characteristics and driving behavior as well as to predict driving risk, firstly, this research conducted field tests in four scenarios surrounding the port. Visual characteristics and behavior data of sedan drivers were collected. Secondly, a “Visual‐behavior” chain model was established. The relationship between drivers’ visual characteristics, driving behavior, and driving risk was illustrated from the perspective of time‐series behavior patterns. Thirdly, three driving risk prediction models were built with Autoregressive Integrated Moving Average (ARIMA), Long Short‐Term Memory (LSTM), and ARIMA‐LSTM. The results indicate that the ARIMA‐LSTM model shows the most effective prediction performance. This research provides a field‐data comparative analysis of the driving risks influenced by a high proportion of container trucks. The findings contribute to understanding the unique mixed traffic visual environment around large‐scale container ports.

Vehicle Classification using BiLSTM for Predictive Maintenance and Digital Twins

The service life of infrastructure is directly related to the traffic load. In the past, this traffic load has often been underestimated, resulting in significant damage to structures, especially steel bridges. In order to establish the paradigm of predictive maintenance in bridge engineering, prediction models for the state of damage and service life are necessary. In particular, the proportion of heavy trucks leads to a significant decrease in remaining service life. Therefore, knowledge of the composition and frequency of traffic is important. New methods from communications technology based on recurrent neural networks allow rapid identification of specific signals in monitoring data.A bidirectional Long Short-Term Memory Network is used to identify up to nine different vehicle types from the drive-by vehicle signals. The method was applied to monitoring drive-by data from a German wide span steel bridge with an orthotropic deck (Rhine bridge Duisburg-Neuenkamp). Validation was performed based on weight-in-motion data in combination with a finite element analysis. The extracted data allows a detailed analysis of the classification of vehicle types, sequence effects and clustering of vehicles. The data obtained can later be used in digital twin applications.

Analyzing Freeway Safety Influencing Factors Using the CatBoost Model and Interpretable Machine-Learning Framework, SHAP

Exploring and analyzing safety influencing factors can guide targeted traffic safety management. Traditional traffic safety models are aimed at specific data problems and making adjustments to the model structure, which lack focus on predictive ability and have limited information on the analysis of influencing factors. In recent years, machine-learning methods have opened new avenues in modeling that have higher prediction accuracy, can identify complex nonlinear relationships, and can overcome over- and under-dispersion and correlation. Machine-learning methods, however, pose the problem of limited interpretability. The interpretable machine-learning framework SHAP can be an effective solution, which can not only reflect the influence of features in each sample but also generate global interpretation. This study established gradient boosting models including the CatBoost and XGBoost models as traffic safety models, which were compared with a traditional NB regression model and a zero-inflated negative binomial regression model. SHAP was used to analyze several safety influencing factors, including geometric design features, traffic operation characteristics, time of day, and land use. Results confirmed that the CatBoost model has better prediction ability and is a more suitable traffic safety model than the traditional negative binomial regression model. Among the key findings were that ramp type is the most important factor in freeway crash frequency; curve presence has a great positive impact, while truck proportion has a great negative impact; and traffic volume is highly correlated with truck proportion. These findings can provide theoretical support for safety operation management and targeted improvement measures for freeways.

An extended non-lane-discipline-based continuum model through driver behaviors for analyzing multi-traffic flows

Non-lane-discipline environment has an essential role in driver behavior. In this environment, drivers change their driving behavior through the traffic flow, causing perturbations and complexity. To evaluate the effect of driver behavior under non-lane-based discipline on the traffic flow, the present study investigated the physical and dynamic characteristics of drivers on an urban highway in Iran for each vehicle class based on the lateral separation distance (LSD) parameter and strength influence factor. Then, an extended non-lane-discipline-based continuum model was proposed for different traffic conditions. Further, the proposed model was simulated using the adjusted upwind scheme for lane analysis. The field data results indicated that non-lane-based discipline has an important role in changing car drivers’ driving behaviors from typical and sluggish to aggressive behavior compared with lane-based discipline. Moreover, under maximum LSD, due to an increase in the lateral distance, car drivers became aggressive. In addition, there was a high stability region in traffic flow due to the positive effect of driver behavior, under non-lane-discipline environment, on the critical region of density where there was an increase in the traffic flow. However, in the free flow similar to jam density, the effect of the behavior on traffic flow was insignificant. Furthermore, driver behavior under non-lane-based heterogeneous traffic conditions could improve traffic flow. For the case of the car’s traffic flow regarding aggressive behavior under non-lane-based discipline, the capacity of lane reached 1.18–1.45 times higher in cars than that in sluggish behavior under lane-based discipline, respectively. Accordingly, the capacity of the lane was observed to be 1.50–3.41 times higher than that of trucks as the truck proportions decreased from 80% to 0% due to different LSD parameters and strength influence factors. Thus, the extended continuum model can appropriately predict traffic flow considering driver behavior under non-lane-based discipline.

Exploring the impact of truck traffic on road segment-based severe crash proportion using extensive weigh-in-motion data

Fixed proportions by severity assumption in Highway Safety Manual could be violated since the proportions of severe crashes are likely to be affected by truck traffic characteristics. Previous studies often used truck proportion as the key indicator of truck traffic. However, it considered different trucks the same regardless of their actual weight. Therefore, this paper aimed to explore the impact of truck traffic characteristics, especially actual weight, on the proportions of severe crashes on road segments while controlling for other contributing factors. Extensive Weigh-in-Motion (WIM) data from five-year (2011–2015) 88 WIM stations in New Jersey were utilized to capture detailed vehicle weight information and other truck traffic-related characteristics. Road features, traffic volume, and crash data were also collected and aggregated for road segments. To account for the bounded nature of Fatality and Injury Proportion (FIP), one-part and two-part Fractional Regression Models (FRMs) were developed, and the link functions were appropriately selected based on corresponding statistical tests. The results show that the mean of vehicle weight was significant and positively related to the FIP of nonzero-FIP road segments while controlling for other contributing factors. For the road segment with a nonzero FIP, if the mean of vehicle weight increased by 1 kip, the total crash FIP, single-vehicle crash FIP, and multiple-vehicle crash FIP for the road segment with nonzero FIP increased by 3.3%, 3.4%, 2.2% respectively. This study contributes to the literature by building a link between actual vehicle weight measured in the traffic flow and road segment crash severity.

Heavy vehicles’ non-exhaust exhibits competitive contribution to PM2.5 compared with exhaust in port and nearby areas

Heavy port transportation networks are increasingly considered as significant contributors of PM2.5 pollution compared to vessels in recent decades. In addition, evidence points to the non-exhaust emission of port traffic as the real driver. This study linked PM2.5 concentrations to varied locations and traffic fleet characteristics in port area through filter sampling. The coupled emission ratio-positive matrix factorisation (ER-PMF) method resolves source factors by avoiding direct overlap from collinear sources. In the port central and entrance areas, freight delivery activity emissions including vehicle exhaust and non-exhaust particles, as well as induced road dust resuspension, accounted for nearly half of the total contribution (42.5%–49.9%). In particular, the contribution of non-exhaust from denser traffic with high proportion of trucks was competitive and equivalent to 52.3% of that from exhaust. Backward trajectory statistical models further interpreted the notably larger-scale coverage of non-exhaust emissions in the port's central area. The distribution of PM2.5 were interpolated within the scope of the port and nearby urban areas, displaying the potential contribution of non-exhaust within 1.15 μg/m3–4.68 μg/m3, slightly higher than the urban detections reported nearby. This study may provide useful insights into the increasing percentage of non-exhaust from trucks in ports and nearby urban areas and facilitate supplementary data collection on Euro-VII type-approval limit settings.

Open-Pit Mine Truck Dispatching System Based on Dynamic Ore Blending Decisions

In the production process of open-pit mines, trucks are applied in the production process of open-pit mines for transporting ores and rocks. Most open-pit mines are equipped with dozens of trucks. It is important to plan the dispatch of trucks in the production process so that the transportation process can be the shortest in distance, the lowest in cost, and the most efficient. At present, many open-pit mining enterprises have realized the use of dispatching systems to schedule trucks to complete production tasks. However, these methods are mostly designed to deploy trucks to reduce production costs without considering the blending problem of the selected ores, and therefore it cannot meet the dual need of ore blending and dispatching. In order to solve the above technical problems and meet the actual needs of the current open-pit mine for ore blending and dispatching, this paper proposes an open-pit mine truck dispatching system based on dynamic ore blending decisions, supported by a 4G/5G wireless network, Beidou positioning, and Internet of Things technology, which can not only realize the optimized truck dispatching of open-pit mine production, but also meet the requirements of downstream concentrators for ore dressing grade. The system has been applied in the Ansteel Group QIDASHAN mine for one year. The proportion of trucks dispatched through the system reached more than 70%. The trucks’ capacity were upgraded from 3.79 to 4 million ton km per set per year, and the efficiency was improved by 5.5%. The limitations of the proposed system and method mainly include the possibility of inaccurate measurement of ore output and the lack of combination with unmanned driving.

Mitigation of emissions and energy consumption due to light-duty vehicle size increases

Analysts need to consider the shift of light-duty vehicle sales from cars to light trucks to accurately project fuel consumption, greenhouse gas emissions, and consumer spending. We find that energy consumed by on-road light-duty vehicles in the United States can vary by 10% by changing assumptions regarding the sales mix. Scenarios aligned with third-party forecasts, assuming a greater sales proportion of light trucks and fewer cars, yield petroleum consumption 3–8% higher than forecasts from the U.S. Energy Information Administration (EIA), with greenhouse gas emissions 5–7% higher and a 4–9% increase in consumer spending on vehicles and fuel. This incremental energy consumption can be offset by additional technologies for these vehicles. If most sedans were phased out in favor of larger sport utility vehicles, we find a sales share of 30% battery electric vehicles or 39% hybrid electric vehicles would equal the emissions of the EIA Reference Case.

Stability Analysis and Prediction of Traffic Flow of Trucks at Road Intersections Based on Heterogenous Optimal Velocity and Artificial Neural Network Model

The evolution of traffic-related accidents caused by long, short, and medium trucks at signalized road intersections have been underemphasized in the last few years. Far, little attention has been paid to the modelling of trucks traffic flow using an artificial neural network model and evaluating the stability analysis of trucks depending on the heterogenous optimal velocity. This research evaluates the effect of trucks on some specific traffic flow features. Over the years, it has been deduced that trucks, irrespective of their sizes, significantly impact their surrounding traffic flow due to their body sizes and operational features. In this study, we focused on modelling the traffic flow of trucks at signalized road intersections using traffic flow variables such as speed, traffic volume, traffic density, and time as our inputs and outputs. The truck traffic data was collected using up-to-date equipment such as video cameras and inductive loop detectors from the South Africa transportation network. During the ANN modelling of the truck traffic flow, we used 956 traffic datasets divided into 70% for training and 15% each for testing and validation. The ANN model results show testing regression values of R2 (0.99901). This shows that the inputs and output are well correlated and the ANN model’s superiority in predicting truck traffic flow at signalized road intersections. Based on the HEOV model results, the result of the research indicates that in the mixed traffic flow of trucks in real-life scenarios, the proportion of different trucks on the signalized road intersections rather than the proportions of types of trucks can be used in the determination of traffic flow stability of each truck. This research extends our knowledge of truck traffic flow modelling and provides a blueprint for examining the stability analysis of long, short, and medium trucks in their immediate driving environment.

How Do Transportation Influencing Factors Affect Air Pollutants from Vehicles in China? Evidence from Threshold Effect

In recent years, China has promoted a series of legal norms to reduce the environmental impact of air pollutants from vehicles. The three main vehicle emission species (carbon monoxide, hydrocarbons, nitrogen oxides) contribute significantly to air pollution. In this study, the emission factor method was used to estimate air pollutants from vehicles in 31 provinces from 2006 to 2016. The results show a trend of total vehicle carbon monoxide (CO) and hydrocarbons (HC) emissions decreasing with time; the vehicle nitrogen oxides (NOx) emission trend is divided into two stages: an upward trend between 2006 and 2012 and a downward trend after 2012. Based on a panel threshold, a regression method was used to divide the vehicle NOx and CO emissions in China into four emission zones: low emissions, medium emissions, high emissions, and extra-high emissions. Vehicle HC emissions were divided into three emission zones, which corresponded to low emissions, medium emissions, and high emissions. Overall, vehicle pollution emission efficiency and per capita GDP have a significant inhibitory effect on the three main air pollutants from vehicles (NOx, HC, CO). Both passenger and freight turnover have significant roles in promoting the three air pollutants from vehicles (NOx, HC, CO). Road density and road carrying capacity have a significant role in promoting vehicle HC and CO emissions. Increasing truck proportion inhibits vehicle CO emissions and promotes vehicle NOx emissions. The urbanization rate has a positive effect on vehicle HC and CO emissions. Moreover, there is obvious heterogeneity in different emission zones of the three air pollutants from vehicles (NOx, HC, CO).

Application of explainable machine learning for real-time safety analysis toward a connected vehicle environment

Due to the difficulty of obtaining traffic flow data and conflicts simultaneously, conflict-based analysis using macroscopic traffic features is much less studied. This research aims to analyze real-time safety by a disaggregate study and explore the benefit of the connected vehicle (CV) for real-time safety evaluation. To avoid the endogeneity problem regarding conflicts and traffic features in regression models, machine learning is employed to obtain a reliable and practical real-time safety model. The results show that the Random Forest outperforms eXtreme Gradient Boosting, Support Vector Machine and Adaptive Boosting models, achieving the best performance with the highest AUC of 0.827. For a deep understanding of conflict mechanisms, the explainable machine learning method SHAP (SHapley Additive exPlanation) is introduced to improve the model interpretability providing insights into the impacts of traffic flow features. Lane difference regarding average speed is found to have the most significant impacts on real-time safety. Speed variation, the proportion of trucks and traffic volume are associated with conflict occurrence. Further analysis highlights that the impacts of traffic features are heterogeneous and there may exist specific patterns of paired features affecting real-time safety. Encouragingly, SHAP appears to be able to complement the traditional model with random components in terms of revealing heterogeneity. The explainable machine learning can also provide a solid basis for discretizing continuous variables while previous studies perform discretization mainly based on prior knowledge and experience. The experimental result regarding CV Market Penetration Rate (CV-MPR) demonstrates that the model performance is gradually elevated with the increase of penetration rate. The initial stage of the CV market (20%, 40% CV-MPR) yields the most significant gains in real-time safety evaluation. These findings can be used beneficially in active traffic management.

What factors impact pedestrian and cyclist fatalities? A state level analysis

BackgroundPedestrian and bicyclist injuries and fatalities have increased since 2010 after a long downward trend. Trucks and SUVs, collectively called light trucks, have also increased in sales and size, which may affect pedestrians and bicyclists. Additionally, pedestrian and cyclist commuters vary by state and it has been speculated that an increase in such commuters may affect fatalities. Studying vulnerable road users can bestow clues on best practices for infrastructure and public health.MethodsState level pedestrian and cyclist fatality data was obtained from the National Highway Transportation Safety Administration for 2018. Light truck registration by state was obtained from the Office of Highway Policy Information for 2018. Commuters who walk or bike to work were obtained from the American Community Survey from 2009 to 2011, from the latest Centers for Disease Control report. We performed multiple linear regression, accounting for total motor vehicle lane miles per 100 people, also obtained from the Office of Highway Policy Information for 2018. Multiple regression analysis was performed to assess predictors for pedestrian and cyclist fatalities with the predictors variables of light truck registration, lane miles per 100 people, and proportion of commuters who are vulnerable road users. Secondary analysis included simple linear regression of the predictor variables against each other.ResultsThe multiple regression model, including proportion of light truck registration, lane miles per 100 people, and proportion of commuters who are vulnerable road users, accounted for 18% of the variability in the outcome variable (p = 0.03). An increased number of vulnerable road users were negatively associated with pedestrian and bicyclist fatality. Additionally, there appeared to be an association between motor vehicle lane miles per 100 people and proportion of light truck registrations that was also significant (p < 0.01).ConclusionThe variables affecting vulnerable road user deaths are important to understand given their increased risk exposure on the road. This state level study identifies a potential protective variable with increased vulnerable road users being associated with a decrease in pedestrian and bicyclist death rates. Additionally, light truck proportions do not appear to have a significant effect on death rates.

Comprehensive Operation Risk Assessment of a Highway Maintenance Area Based on Reliability

To study the influence of various working conditions on traffic safety in the maintenance area of a mountain expressway, 675 groups of PTV VISSIM traffic simulation experiments were designed with various traffic organization modes, traffic volumes, traffic compositions, activity area lengths, and speed limit values. The results show that the activity area length of a closed lane, a compressed lane, and a borrowed opposite lane had no marked influence on the traffic conflicts. There was a significant positive correlation between the proportion of trucks and the number of traffic conflicts, and the number of traffic conflicts increased significantly with an increase in traffic volume. In the closed lane and borrowed opposite lane scenarios, the increase in traffic conflicts was more obvious with the increase in traffic volume. There were obvious differences in the number of traffic conflicts under different forms of traffic organization. The number of conflicts in the compressed lane scenario was the lowest, and in the borrowed opposite lane scenario the number of conflicts was the highest. There was a significant correlation between a decrease in the speed limit and an increase in the number of traffic conflicts. Finally, with traffic volume, truck proportion, and speed limit values as independent variables and reliability as a dependent variable, linear regression equations of reliability were established for three traffic organization scenarios: closed lane, borrowed opposite lane, and compressed lane.

Calculation of the Roadside Clear Zone Width along Highways Based on the Safe Slope

International crash data indicate that roadside characteristics contribute to more than half of all roadside accidents involving serious injury or death. Therefore, research on roadside safety is urgently needed. Based on the vehicle departure speed, pavement height (i.e., the difference between pavement elevation and ground elevation), slope gradient, and horizontal curve radius, this study uses PC-Crash simulation software to carry out tests of trucks and cars exiting a road. A chi-squared automatic interaction detection (CHAID) decision tree is used to explore the causative mechanism of vehicle rollover, and the concept of a “safe slope” to ensure that vehicles do not roll over is proposed. Aiming at straight and curved sections, discriminant functions of vehicle rollover and nonrollover are fitted through Bayesian discriminant analysis, and safe slope calculation models for trucks and cars are then constructed. Based on the obtained safe slope models, calculation methods for the safe slope and the roadside clear zone width involving different traffic compositions are proposed by calibrating the lateral distance from the final position of nonrollover vehicles to the road edge. The results show that the factors affecting vehicle rollover are, in descending order of importance, the slope gradient, pavement height, vehicle type, departure speed, and horizontal curve radius. For a section with a large proportion of cars, the slope gradient should not be steeper than 1:3.5. The horizontal curve radius should not be less than 600 m for a section with a large proportion of trucks and a slope gradient steeper than 1:3.5 or shallower than 1:2.5. Additionally, for a section with a pavement higher than 0.5 m and a slope gradient steeper than 1:2.5, the operating speed limit should be lower than 60 km/h. These research results have theoretical value and practical significance to improve the driving safety level and reducing the risk of roadside accidents.

Temporal instability of truck volume composition on non-truck-involved crash severity using uncorrelated and correlated grouped random parameters binary logit models with space-time variations

With the growing demand for inter-city freight transport, proportion of trucks in the freeway traffic has been increasing in China and worldwide in the past decade. There have been serious safety concerns for the prevalence of truck in freeway traffic given its size and weight, and the interferences of unsafe maneuvers of trucks on other traffic. However, existing literatures mainly focus on the crash severity of truck-involved crashes only. It is rare that the effects of the presence of trucks (of various classes) on the crash severity of non-truck-involved crashes (i.e. not involving any truck) are studied. To account for the effect of unobserved heterogeneity, random parameters discrete outcome models have been deployed to model the crash severity. However, the possible correlations between random parameters are seldom considered. This study aims to investigate the role of the presence and prevalence of trucks of various classes on the severity of non-truck involved crashes, with which both the uncorrelated and correlated panel-level space–time-varying heterogeneities are considered. To account for possible temporal instability, the uncorrelated and correlated grouped random parameters binary logit models are established for each separate year, based on the comprehensive crash and traffic data of eight freeway segments in Shandong of China during the period from 2016 to 2019. 4,008 crashes are extracted and grouped with space (i.e. road segment)-time (i.e. time of the day) variations considered. Results indicate that the proposed correlated grouped random parameters model is superior to the uncorrelated grouped random parameters logit model in three out of the four years. Also, correlation in the heterogeneous effects between super-large truck volume and average speed is significant. Moreover, temporal instability is revealed for majority of the factors, to different extents, including the heterogenous ones and volumes of all truck types. Nevertheless, this study addresses and discusses the fundamental issues of the temporally unstable correlation in unobserved heterogeneity between possible factors in crash severity analysis.