Proportion Of Trucks Research Articles

A mixed logit model with mean-variance heterogeneity to investigate factors of crash occurrence

This study thoroughly investigated factors affecting crash occurrence using detailed data of crash, traffic condition and freeway geometries. To fully account for heterogeneity induced by unobserved characteristics of crash factors, a mixed logit model with mean-variance heterogeneity was estimated as an alternative to the commonly used mixed logit model and the fixed parameters logit model. Results indicate that the mixed logit model with mean-variance heterogeneity could improve the goodness-of-fit and was more flexible in accounting for unobserved heterogeneity compared with its counterparts. Additionally, by allowing means and variances of random parameters to be estimable functions of explanatory variables, the safety effect of interactions among multiple factors was concluded, for example: (1) sharp curves resulted in an increasing risk of crash and the rate of increase was positively correlated with the distance travelled by vehicles along a steep downgrade; (2) the adverse safety effect of steep downgrade increased with the distance covered by vehicles, especially for segments with high proportion of heavy trucks; (3) downhill segments with steep slopes were particularly dangerous. Findings from this study are expected to provide an insightful knowledge to the mechanism of crash occurrence and should be beneficial to design and manage safer freeways.

Road traffic and air pollution: Evidence from a nationwide traffic control during coronavirus disease 2019 outbreak

Existing estimations of air pollution from automobile sources are based on either experiments or small-scale governmental interventions. China's nationwide traffic control during the coronavirus disease 2019 outbreak provided us a unique opportunity to assess the direct dose-effect relationship between vehicle density and air pollution. We found that, during the coronavirus disease 2019 outbreak, the nationwide reduced air pollution (except for O3) could be largely explained by traffic control measures. During the traffic control period, every doubling of vehicle density was associated with a decrease of 4.2 (2.0, 6.4) μg/m3 in PM2.5, 5.5 (2.9, 8.1) μg/m3 in PM10, 1.5 (0.9, 2.0) μg/m3 in NO2, and 0.04 (0.02, 0.07) mg/m3 in CO comparing cities with different vehicle densities. Similarly, for every 10% increase in the truck proportion, PM2.5 decreased by 12.3 (4.1, 20.6) μg/m3, PM10 decreased by 14.3 (4.6, 23.9) μg/m3, and CO decreased by 0.14 (0.05, 0.23) mg/m3. Moreover, the associations between vehicle density and reduction in PM2.5, PM10, and CO during the traffic control period were stronger and showed near-complete linearity in cities with low green coverage rate (All P < 0.05 for interaction). According to our estimation, PM2.5 emissions from every doubling of vehicle density can lead to over 8000 excess deaths per year, 66% of which were caused by cardiopulmonary diseases. This natural experiment study is the first to observe the dose-effect relationship between on-road traffic and traffic-generated air pollution, as well as the mitigating effect of urban greening. Findings provide key evidence to the assessment and control of traffic-generated air pollution and its public health impact.

The Impact of Truck Proportion on Traffic Safety Using Surrogate Safety Measures in China

The purpose of this study is to investigate the impact of the truck proportion on surrogate safety measures to explore the relationship between truck proportion and traffic safety. The relationship between truck proportion and traffic flow parameters was analyzed by correlation and partial correlation analysis, and the value of the 85th percentile speed minus the 15th percentile speed (85%V–15%V) and the speed variation coefficient were selected as surrogate safety measures to explore the impact of truck proportion on traffic status. The k-means algorithm and the support vector machine were employed to evaluate traffic status on a freeway under different truck proportions in different periods. The major results are that the relationship between truck proportion and the value of 85%V–15%V and the speed variation coefficient is consistent in different aggregation periods. With increasing truck proportion, the value of 85%V–15%V, as well as the speed variation coefficient, increases initially and then decreases. In addition, the traffic flow status tends to be dangerous when the truck proportion ranges from 0.4 to 0.6 and when the value of 85%V–15%V and the speed variation coefficient are above 42 km/h and 0.223, respectively. While the truck proportion is from 0.1 to 0.3 and from 0.7 to 0.9, the traffic flow is relatively safe on the condition that the value of 85%V–15%V and the speed variation coefficient were under 42 km/h and 0.223, respectively. Therefore, the relationship between truck proportion and traffic safety could be well revealed by two surrogate safety measures, that is, the value of 85%V–15%V and the speed variation coefficient. In addition, the k-means algorithm and the support vector machine can well reveal the impact of truck proportion on traffic safety in different periods. The findings of this study indicate a need for decreasing the disturbance of mixed traffic and the impact of the truck proportion on traffic safety status.

Study of work zone traffic safety under adverse driving conditions with a microscopic traffic simulation approach

Work zone traffic safety under adverse weather conditions has been a serious concern for drivers and transportation agencies. Existing studies on work zone traffic safety with statistical approaches are limited by the availability of data from historical crashes. To date, there is no comprehensive simulation framework to assess work zone traffic safety under adverse driving environments by considering both multi-vehicle and single-vehicle crashes. To fill this gap, this paper presents an integrated framework to evaluate traffic safety in work zone under adverse driving conditions by considering specific work zone configuration, weather and road surface conditions. A new risk index is introduced to assess the traffic safety risk of work zones by integrating the risks of multi-vehicle crashes and single-vehicle crashes. Traffic safety of a typical work zone under different weather conditions is studied to demonstrate the proposed framework. The impacts of the differential speed limits (DSL) and truck proportion on the work zone traffic safety are also investigated. Results show that adverse weather may increase the crash risk in work zones. The effect of DSL on the work zone traffic safety is found to be insignificant while the truck ratio influences the work zone safety in the rainy and snowy weather by primarily affecting the multi-vehicle crash risks.

Effect of Speed Bumps on Pavement Condition

Speed bumps are efficient traffic calming devices that allow transport authorities to control vehicle speeds and improve safety in specific locations. Though frequently used in residential areas, they have become increasing prevalent in other higher volume roadways (particularly in developing countries), because of their effectiveness, low-cost of implementation, and easy installation process. However, in these countries there is no proper technical guidance for speed bump design and implementation, which is why one can often find these devices placed on an arterial highway or on roads where the proportion of trucks is high. The most important consequence of placing a speed bump on a road with large numbers of trucks is that the pavement deterioration process will accelerate as a result of the induced vibrations and bouncing of a truck’s suspension. In this paper the relationship between the bump geometry—length and height—and the dynamic load transmitted to the pavement is studied. A full truck model has been used to simulate the vehicle dynamics while passing over the bump, to estimate the demand imposed over the pavement. Damage was calculated for each simulation scenario using a combination of linear-elastic analysis and empirical damage functions. The geometry that leads to less damage is the circular, with a minimum length of two meters and a maximum height of ten centimeters.

Fatigue Flexural Performance of Short-Span Reinforced Concrete T-Beams Considering Overloading Effect

Traffic volume increase and higher proportion of heavier trucks have raised the potential risk of fatigue failure of short-span reinforced concrete beams. To investigate the fatigue behavior of short-span reinforced concrete beams with and without the overload effect, nine 5 m reinforced concrete T-beams were cast and tested. Two beams were tested under static loading to determine the ultimate strength; the remaining seven beams were subjected to cyclic loading with constant-amplitude load ranges. In addition, two of the seven beams were subjected to instant overloading. It was observed that the typical failure mode under cyclic loading was the fatigue fracture of tensile reinforcing bars. The introduction of instant overloading resulted in a remarkable reduction of fatigue life. Among all the parameters, the stress range of the reinforcing bars showed the highest effect on the fatigue life. In the end, the fatigue safety provisions in the current reinforced concrete beam design codes were evaluated based on the fatigue limits and S-N curves.

Severity assessment of accidents involving roadside trees based on occupant injury analysis.

The aims of this study were to achieve a quantitative assessment of the severity of accidents involving roadside trees on highways and to propose corresponding safety measures to reduce accident losses. This paper used the acceleration severity index (ASI), head injury criteria (HIC) and chest resultant acceleration (CRA) as indicators of occupant injuries and horizontal radii, vehicle departure speeds, tree diameters and roadside tree spacing as research variables to carry out bias collision tests between cars, trucks and trees by constructing a vehicle rigid body system and an occupant multibody system in PC-crash 10.0® simulation software. A total of 2,256 data points were collected. For straight and curved segments of highways, the occupant injury evaluation models of cars were fitted based on the CRA, and occupant injury evaluation models of trucks and cars were fitted based on the ASI. According to the Fisher optimal segmentation method, reasonable classification standards of severities of accidents involving roadside trees and the corresponding ASI and CRA thresholds were determined, and severity assessment methods for accidents involving roadside trees based on the CRA and ASI were provided. Additionally, a new index by which to evaluate the accuracy of the accident severity classification and the degree of misclassification was built and applied for the validity verification of the proposed severity assessment methods. A proportion of trucks was introduced to further improve the ASI evaluation model. For the same simulation conditions, the results show that driver chest injuries are more serious than driver head injuries and that the average ASI of cars is greater than that of trucks. The CRA and ASI have a positive linear correlation with the departure speed and a logarithmic correlation with the roadside tree diameters. The larger the spacing of roadside trees is and the smaller the horizontal radius is, the smaller the chance that a vehicle will experience a second collision and the lower the risk of occupant injury. In method validation, the evaluation results from two proposed severity assessment methods based on the CRA and ASI are consistent, and the degrees of misclassification are 4.65% and 4.26%, respectively, which verifies the accuracy of the methods proposed in this paper and confirms that the ASI can be employed as an effective index for evaluating occupant injuries in accidents involving roadside trees.

Investigating the Effects of Pavement Roughness on Freeway Safety using Data from Five States

Traffic crashes occur usually because of a combination of human, roadway/environment, and vehicle factors. Pavement condition is closely related to these three factors as it is one of the most important roadway/environment factors and it affects driving behavior and vehicle performance at the same time. Previous studies have shown that pavement condition plays an important role in safety. This study develops four different safety performance functions to evaluate the effect of pavement roughness, measured by the International Roughness Index (IRI), on the number of crashes using the interstate highway data from five U.S. states representing different geographical and weather regions: Arizona, Colorado, Florida, Maryland, and Michigan. The modeling results identify many significant variables including traffic volume and proportion of trucks, through lane count, shoulder type, median width, high-occupancy vehicle (HOV) lane operation and HOV lane count, speed limit, area type along with IRI-related factors. The results indicate that increased IRI (deterioration of pavement quality) contributes to large numbers of total crashes. On interstate highways with speed limits of 70 mph and higher, the effects of IRI are relatively smaller. On the other hand, the effects of IRI increase with a larger traffic volume. Based on the modeling results, seven crash modification functions of IRI values by crash type and speed limit were estimated. The findings from this study are expected to be useful for both pavement and safety engineers to understand the relationship between IRI and safety on freeways.

Safety Evaluation for Connected and Autonomous Vehicles’ Exclusive Lanes considering Penetrate Ratios and Impact of Trucks Using Surrogate Safety Measures

Plenty of studies on exclusive lanes for Connected and Autonomous Vehicle (CAV) have been conducted recently about traffic efficiency and safety. However, most of the previous research studies neglected comprehensive consideration of the safety impact on different market penetration rates (MPRs) of CAVs, traffic demands, and proportion of trucks in mixture CAVs with human’s driven vehicle environment. On this basis, this study is to (1) identify the safety impact on exclusive lanes for CAVs under different MPRs with different traffic demands and (2) investigate the safety impact of trucks for CAV exclusive lanes on mixture environment. Based on the Intelligent Driver Model (IDM), a CAV platooning control algorithm is proposed for modeling the driving behaviors of CAVs. A calibrated 7-kilometer freeway section microscopic simulation environment is built by VISSIM. Four surrogate safety measures, including both longitudinal and lateral safety risk indexes, are employed to evaluate the overall safety impacts of setting exclusive lanes. Main results indicate that (1) setting one exclusive lane is capable to improve overall safety environment in low demand, and two exclusive lanes are more suitable for high-demand scenario; (2) existence of trucks worsens overall longitudinal safety environment, and improper setting of exclusive lanes in high trucks, low MPR scenario has adverse effect on longitudinal safety; and (3) setting exclusive lanes have better longitudinal and lateral safety improvement in high-truck proportion scenarios. Setting one or two exclusive lanes led to [+42.4% to −52.90%] and [+45.7% to −55.2%] of longitudinal risks while [−1.8% to −87.1%] and [−2.1% to −85.3%] of lateral conflicts compared with the base scenario, respectively. Results of this study provide useful insight for the setting of exclusive lanes for CAVs in a mixture environment.

Exploring the Influence of Truck Proportion on Freeway Traffic Safety Using Adaptive Network-Based Fuzzy Inference System

The truck operation of freeway has an impact on traffic safety. In particular, the gradually increasing in truck proportion will inevitably affect the freeway traffic operation of different traffic volume. In this paper, VISSIM simulation is used to supply the field data and orthogonal experimental is designed for calibrate the simulation data. Then, SSAM modeling is combined to analyze the impact of truck proportion on traffic flow parameters and traffic conflicts. The serious and general conflict prediction model based on the Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed to determine the impact of the truck proportion on freeway traffic safety. The results show that when the truck proportion is around 0.4 under 3200 veh/h and 0.6 under 2600 veh/h, there are more traffic conflicts and the number of serious conflicts is more than the number of general conflicts, which also reflect the relationship between truck proportion and traffic safety. Under 3000 veh/h, travel time and average delay increasing while mean speed and mean speed of small car decreases with truck proportion increases. The mean time headway rises largely with the truck proportion increasing above 3000 veh/h. The speed standard deviation increases initially and then fall with truck proportion increasing. The lane-changing decreases while truck proportion increasing. In addition, ANFIS can accurately determine the impact of truck proportion on traffic conflicts under different traffic volume, and also validate the learning ability of ANFIS.

Characteristics and sources of PM2.5 and reactive gases near roadways in two metropolitan areas in Canada

With concern in recent years about adverse health effects for populations living or spending significant amounts of time near large roadways, an investigation of the air quality characteristics and potential sources influencing levels of PM and its chemical composition was undertaken in Toronto and Vancouver. Three near-road monitoring stations were established in the downtown area and beside a large highway in Toronto, and beside a major trucking route in Vancouver. 24-hour integrated samples were concurrently collected at these near-road and nearby urban background sites. This study has provided detailed chemical data for PM2.5 and reactive gases (NH3, HONO and HNO3) for a year in 2015–2016. Differences between pollutant concentrations at the near-road and background urban sites were identified, and compared with observations at other urban locations. The traffic contribution was quantified as the concentration increment between the near-road and background sites. The highest increments due to traffic were observed for elemental carbon, select trace metals (e.g. Fe, Ba, Cu, Sb, Zn) and reactive gases (NH3, HONO). In general, the percent contribution of local traffic-related emissions followed a descending order of Toronto highway > Vancouver truck route > downtown Toronto for most of these pollutants. It appears that the influence of traffic-related emissions on air pollution near roads depends more on the proportion of large trucks in the fleet than the total traffic volume. Application of principal component analysis (PCA) coupled with multi-linear regression (MLR) analysis to the local traffic increment pollutant data, as well as knowledge of chemical markers representative of different sources, helped to identify the possible sources of traffic-related PM2.5. These sources include non-exhaust (brake wear abrasion, resuspended road dust) and vehicular exhaust (mixed gasoline/diesel, diesel and lubricating oil combustion) emissions. The contribution of each of the sources varied between sites. In particular, the contribution of diesel exhaust emissions, presumably from highly polluting heavy-duty vehicles and trucks, was significant at the truck route (Vancouver) and the highway (Toronto) sites. Furthermore, the substantial contribution of non-exhaust emissions (brake wear and resuspension of road dust) to PM2.5, and thus metals, with differences between sites due to traffic characteristics or local meteorology was identified. Emissions related to lube oil combustion were not statistically significant. Overall, this work delivered valuable information that serves as input for further studies involving other roads and cities in order to generate reliable and representative results for air quality management and associated health outcomes.

Simulating the uncertain environmental impact of freight truck shifting programs

Shifting freight from regular hours to nighttime has been widely used to relieve traffic congestion and reduce diesel emissions. However, there are also several cities that restrict trucks from operating at night to meet the noise regulation. Uncertainty exists in terms of the environmental impact of these truck shifting programs. On one hand, shifting trucks from peak hours can reduce the total emissions by easing congestion, while the impact of moving truck emissions to nighttime will be interacted with more stable atmospheric boundary layer and lower mixing height, increasing the nighttime concentration. On the other hand, shifting trucks from the night to day avoids the emissions with a stable atmosphere at night, while worsening traffic condition at daytime, and hence increasing the total emissions. The research presents a realistic case study and explores a comparative analysis of PM2.5 concentration originated from a hypothetical freeway corridor and concentrated at a hospital located at 200-m far away from the corridor by implementing four truck strategies applied under various scenarios with traffic demands and heavy-duty trucks (HDTs) proportions. Two meteorological conditions are also considered, with one representing coast region and unstable atmospheric boundary layer, and thus good dispersion condition, and one inland region with a relatively stable atmospheric condition and bad dispersion condition. The results indicate that shifting trucks from peak-period to off-peak period is effective to reduce both emissions and 24-hr average concentrations in scenarios of high traffic demands and high truck proportions, in which the peak period traffic is congested and thus the benefits of reducing congestion and resultant emissions are quite large by altering freight trucks from peak period. It is also observed in the scenarios with lower-traffic demand where the traffic congestion is less severe (in this case, the throughput is less than 75,000 vehicles per day on a three-lane freeway segment, and average speed is higher than 60 km/h), although shifting truck from night to day increase emissions by adding the shifted traffic demand at peak period, it decreases the average concentration by avoiding nighttime emissions. Such a benefit will be further amplified in the meteorological condition of low mixing height at night. Shifting trucks from daytime to nighttime is found to be the strategy that contributes to the highest average PM2.5 concentration, although sometimes the total emissions are minimized.

Exploring Crash Injury Severity on Urban Motorways by Applying Finite Mixture Models

Abstract The effective treatment of crashes is a major concern to societies due to losses in human lives and the economic and social costs. Crash injury severity is one of the most well-known and well researched aspects of crashes in the science of road safety, with many study types across different countries and sample frames. All types of severity analyses primarily aim to determine the contributing factors for increased crash or injury severity and the respective degree of said contributions. Usually, driver and passenger-related factors are examined, as well as road environment characteristics such as geometric design aspects, traffic conditions, vehicle type and technology and weather conditions, as investigated in past literature. However, crash injury severity has not been adequately explored as the majority of relative research has a focus on analyzing crash likelihood, i.e. the probability of a crash to occur. Thus, this study aims to add to current knowledge by examining the determinants behind injury severity of occupants involved in crashes in the urban motorway Attica Tollway (‘Attiki odos’) which lies in the Greater Athens Area in Greece. In order to account or the unobserved heterogeneity, crash injury severity is explored by utilizing finite mixture logit models. Results indicate that a number of traffic parameters such as truck proportion, average flow and standard deviation of occupancy, as well as other risk factors, such as accident type and engine size have a significant effect on the injury severity outcome of vehicle occupants. Moreover, this model accounted for the heterogeneity among two distinct groups of observations. More research is needed in order to incorporate also vulnerable road users such as motorcyclists, cyclists and pedestrians and further investigate the impact of real-time traffic and weather parameters.

Effects of Revised Toll‐by‐Weight Policy on Truck Overloading Behavior and Bridge Infrastructure Damage Using Weigh‐in‐Motion Data: A Comparative Study in China

Since 2000, overloaded trucks have caused more than 50 bridges to collapse in China. In an effort to ensure the structural safety and extend the service life of the highway infrastructure, the Chinese government has proposed a series of policies in the past decade to mitigate truck overloading. This study aimed at investigating the effects of China’s recently revised toll‐by‐weight policy on truck overloading behavior and bridge infrastructure damage using weigh‐in‐motion data that spanned seven years (January 2011 to March 2018) and two successive toll‐by‐weight policies (with the new one implemented from August 2016), wherein truck data were measured from a typical national freeway segment. We first compared truck traffic volumes, compositions, and weight distributions under the initial and revised toll‐by‐weight policies. Next, we compared bridge infrastructure performance with respect to safety and fatigue based on the overloaded truck traffic observed under the initial and revised toll‐by‐weight policies. The results indicated that the revised toll‐by‐weight policy, which uses a stepwise incremental fee structure based on vehicle weight, was more effective at controlling truck overloading behavior and reducing bridge infrastructure damage than the initial toll‐by‐weight policy. Under the current policy, average daily truck volumes, overloaded truck proportions, and maximum truck weights decreased significantly. Concurrently, extreme and equivalent load effects for safety and fatigue assessments, respectively, decreased by an average of 20% for small‐ to medium‐span bridges. Despite these noted improvements, overloaded truck traffic persisted, with loads often exceeding bridge design levels. This study’s findings can support future efforts by the Chinese government to further refine their toll‐by‐weight policies and subsequently ensure a safe and viable transportation network.

Effects of freeway climbing lane on crash frequency: Application of propensity scores and potential outcomes

In this paper, the effects of climbing lane on traffic safety were investigated using propensity scores and potential outcomes. Firstly, a binary logit model was developed to estimate propensity scores, and nearest neighbor matching technique with caliper was selected as the suitable matching method. Then, a random effects negative binominal (RENB) model was estimated to further explore the mechanism of safety benefits of climbing lanes. The results indicated that installing a climbing lane on steep uphill freeway segment can reduce the crash frequency, crashes per kilometer and crashes per 100 million vehicle-kilometers traveled by 18.27%, 19.49% and 17.41%, respectively. The safety effects of traffic volume, longitudinal grade and curvature did not change significantly for a segment before and after installing a climbing lane. However, the adverse effects of heavy truck proportion and distance along composite ascending grade on safety could be considerably mitigated by installing a climbing lane.

Application of a random effects negative binomial model to examine crash frequency for freeways in China

The freeway system of China is the longest in the world and planned to expand in the following years. However, the safety conditions of freeways are drawing increasing concerns both from the authorities and the public in the country. The safety design and management for freeways are urgently needed considering the current safety situation and great demands of new freeways. The study presented in this paper thoroughly investigated factors affecting safety using detailed data of crashes, traffic characteristics and freeway geometry. A random effects negative binomial (RENB) model was applied to account for spatial variations within groups together with a negative binomial (NB) model. The results indicated a better goodness of fit of RENB model than the NB model. In addition, a good number of factors significantly contributed to crash, such as truck proportion, presence of climbing lane, median barrier offset, curvature and longitudinal grade, were identified. This study was expected to provide a better understanding of how traffic condition and freeway design affect safety and should be useful to freeway engineers to design safe freeways, or develop effective safety countermeasures.

Analyzing crash frequency in freeway tunnels: A correlated random parameters approach

The majority of past road safety studies focused on open road segments while only a few focused on tunnels. Moreover, the past tunnel studies produced some inconsistent results about the safety effects of the traffic patterns, the tunnel design, and the pavement conditions. The effects of these conditions therefore remain unknown, especially for freeway tunnels in China. The study presented in this paper investigated the safety effects of these various factors utilizing a four-year period (2009–2012) of data as well as three models: 1) a random effects negative binomial model (RENB), 2) an uncorrelated random parameters negative binomial model (URPNB), and 3) a correlated random parameters negative binomial model (CRPNB). Of these three, the results showed that the CRPNB model provided better goodness-of-fit and offered more insights into the factors that contribute to tunnel safety. The CRPNB was not only able to allocate the part of the otherwise unobserved heterogeneity to the individual model parameters but also was able to estimate the cross-correlations between these parameters. Furthermore, the study results showed that traffic volume, tunnel length, proportion of heavy trucks, curvature, and pavement rutting were associated with higher frequencies of traffic crashes, while the distance to the tunnel wall, distance to the adjacent tunnel, distress ratio, International Roughness Index (IRI), and friction coefficient were associated with lower crash frequencies. In addition, the effects of the heterogeneity of the proportion of heavy trucks, the curvature, the rutting depth, and the friction coefficient were identified and their inter-correlations were analyzed.

Crashworthiness of G4(2W) guardrail system: a finite element parametric study

ABSTRACTIn recent years, vehicle demographics have changed to include a relatively large proportion of light trucks, such as pickups, vans and sport-utility vehicles. It is found that several types of guardrail systems, including the G4(2W) guardrail system, are unable to redirect the pickup trucks to roadway safely. Therefore, in this study, several options are considered; they include improving the splice connections and adjusting the guardrail height and the post spacing to improve the performance of this system. The G4(2W) guardrail system is modelled in LS-DYNA and validated with a previous full-scale crash test conducted by the Texas A&M Transportation Institute. A parametric study based on the results of the LS-DYNA crash simulation according to Length of Need test 3-11 and 3-10 is conducted to investigate key factors of guardrail systems, including the splice configuration, the post spacing and the guardrail height. The purpose of this study is to find a model that satisfies the requirements of Test Level 3 outlined in Manual for Assessing Safety Hardware (MASH)'s criteria.

The Characteristics of Greenhouse Gas Emissions from Heavy-Duty Trucks in the Beijing-Tianjin-Hebei (BTH) Region in China

This paper aims to study the characteristics of greenhouse gas (GHG) emissions from heavy-duty trucks in the Beijing-Tianjin-Hebei (BTH) region, which is located in Northern China. The multiyear emissions of GHG (CO2, CH4 and N2O) from heavy-duty trucks fueled by diesel and natural gas during the period of 2006–2015 were compared and analyzed. The results show that the GHG emissions from heavy-duty trucks increase with time, which is consistent with the trend of the population growth. The total amount of carbon dioxide equivalence (CO2e) emissions in the BTH region was about 5.12 × 106 t in 2015. Among the three sub-regions, Hebei possesses the largest number of heavy-duty trucks due to the size of its heavy-duty industries. As a consequence, the GHG emissions are about 10 times compared to Beijing and Tianjin. Tractor trailers account for the major proportion of heavy-duty trucks and hence contribute to about 74% of GHG emissions. Diesel- and liquefied natural gas (LNG)-powered heavy-duty trucks can reduce GHG emissions more effectively under current national standard IV than can the previous standard. The widespread utilization of the alternative fuel of LNG to mitigate emissions must be accompanied with engine technology development in China. This study has provided new insight on management methods and the policy-making as regards trucks in terms of environmental demand.

Traffic volume and load data measurement using a portable weigh in motion system: A case study

Traditionally, traffic loading characteristics are collected for pavement design and performance prediction purposes using permanent roadside weigh-in-motion (WIM) stations. However, high installation and maintenance costs associated with these permanent WIM stations dictate that their deployment be mostly limited to major highways, such as the interstate network. Quite often however, pavement damage on high volume rural highways with heavy truck proportions is more severe than anticipated, and there is no effective way of quantifying the traffic loading on these highways. Therefore, this study was conducted to evaluate the potential application of portable WIM systems as a means for bringing the WIM technology to these high volume rural highways. A portable WIM unit was deployed in the Texas overweight corridor in Hidalgo County (Pharr District) near the USA-Mexico border on highway FM 1016 for collecting traffic data for a minimum of three weeks in each direction. The collected traffic data were analyzed to generate traffic parameters such as volume, load spectra, and overloading information both in terms of the gross vehicle weight (GVW) and axle weight. The computed traffic parameters were successful in partially explaining some of the existing pavement conditions on this highway. Overall, the study findings indicated that the portable WIM unit can be used as a convenient and cost-effective means for collecting reliable traffic information for design, analysis, and monitoring purposes. However, proper in-situ calibration of the portable WIM unit at each site is imperative prior to any real-time traffic data collection.