Research on Truck Traffic Volume Conditions of Auxiliary Lanes on Two-Lane Highways

The larger the proportion of truck traffic volume, the greater the impact on traffic efficiency, and overtaking behavior will also have an impact. Therefore, in order to clarify the truck traffic volume of the freight two-lane highway due to the difficulty of overtaking, an actual vehicle test is carried out. This involves selecting the appropriate two-lane test section, recording each moment and speed in the driver’s overtaking behavior, performing multiple regression analysis to examine the relationship between the overtaking conflict time and design speed and traffic volume, determining a reasonable evaluation series of two-lane road overtaking risk and the corresponding overtaking conflict time threshold by the Fisher optimal segmentation method, and giving an overtaking behavior risk evaluation method based on conflict time. Finally, according to the overtaking conflict time model, different truck traffic conditions are obtained. The research results show that overtaking conflict time is negatively correlated with the traffic volume and design speed of the lane. Through the risk assessment of the corresponding overtaking behavior, the three levels of serious conflict, general conflict and non-conflict are determined, and the freight traffic volume corresponding to different conflict levels at different speeds is calculated, which provides a reference for setting auxiliary lanes for the two-lane freight highway.

Perceptions of Log Truck Weight Regulations Among Loggers and Forest Industry in Georgia, USA

Georgia and other southern states have far lower gross vehicle weight (GVW) limits for log trucks than other US regions and other countries. Low GVW limits result in high hauling costs and truck traffic. In 2020, including tolerances, five-axle log tractor-trailers were allowed 38,102 kg (84,000 lb) GVW in Georgia. Telephone surveys of 30 loggers and 32 forest industry representatives from the state of Georgia were conducted to measure perceptions of weight regulations and assess support for alternative weights and configurations. The four alternatives included five axles, 39,916 kg (88,000 lb); six axles, 41,277 kg (91,000 lb); six axles, 45,359 kg (100,000 lb); and seven axles, 45,359 kg (100,000 lb) GVW. The majority of loggers and forest industry representatives stated that GVW limits for log trucks were too low. The average preferred GVW limits were 39,621 kg (87,350 lb) and 40,545 kg (89,387 lb) for loggers and forest industry, respectively. Loggers and forest industry supported the five-axle 39,916 kg (88,000 lb) configuration whereas many loggers opposed both 45,359 kg (100,000 lb) configurations. Loggers, forest industry, and policymakers should work to modernize weight laws to reduce hauling costs, maintain or improve safety, and protect public infrastructure. Study Implications Increasing gross vehicle weight (GVW) limits in combination with adding axles to tractor-trailers has been demonstrated to reduce both timber transportation costs and damage to public roads. This study found that loggers and forest industry supported additional GVW but were hesitant to support configurations that would necessitate upgrading log truck fleets. If Georgia is to make its weight limits competitive regionally and internationally, it will be necessary to clearly communicate the benefits of heavier trucks with more axles to skeptical loggers.

A Time-Varying Coupling Analysis of Expressway Traffic Volume and Manufacturing PMI

This study investigates the time-varying coupling relationship between expressway traffic volume and manufacturing purchasing manager index (PMI). First, for the traffic volume and manufacturing PMI time-series data, unit root stability test and Johansen cointegration test are applied to determine the stability of single sequence and the long-term stable correlation between variables, respectively. Then, a time-varying vector autoregressive model (TVP-VAR) is developed to quantify the time-varying correlation between variables. The time-varying parameters of TVP-VAR are estimated using the Markov chain Monte Carlo (MCMC) theory. Finally, the model is validated using examples from China. In the numeric example, three variables, i.e., expressway car traffic volume, expressway truck traffic volume, and manufacturing PMI, are selected for analysis. Results show that there is a positive interaction between expressway traffic volume (both car and truck) and manufacturing PMI. Express traffic volume slowly promotes the development of manufacturing industry. However, with the reform policy of road freight structure in China, the promotion effect of truck traffic on manufacturing PMI in the past two years has decreased significantly. Moreover, as affected by the China demand-led economic development model in recent years, the stimulus effect of manufacturing PMI on expressway passenger traffic volume has increased year by year. And, while the expressway freight structure remains stable, truck traffic volume is hardly affected by fluctuations in manufacturing PMI. These research results are helpful for policy makers to understand the time-varying coupling relationship between expressway traffic volume and manufacturing development and finally to improve the expressway management level.

A New Approach to Exploring the Relationship between Weather Phenomenon and Truck Traffic Volume in the Cold Region Highway Network

Weather events are arbitrary, and this makes it difficult to incorporate weather parameters into transportation models. Recent research on traffic weather interaction analysis conducted at the University of Regina, Canada reported traffic variations with cold temperatures and snowfall. The research team at the University of Regina proposed a linear association between snowfall and temperature to analyze the traffic variation on provincial highways during winter months. The variations were studies with the inclusion of the expected daily volume factor as an independent variable in the model structure. However, the study did not analyze the nature of the association between daily truck traffic volume and snowfall. Based on these drawbacks of the past studies, in this research, the objective is to focus on the effects of snow and temperature on traffic volume changes with a methodological help of Maximal Information Coefficient (MIC), which stems from the maximal information-based nonparametric exploration (MINE) statistics. The results obtained from the analysis indicate that the relationship between snow and truck traffic is non-linear. However, the study could not establish any functional relationship between snowfall and daily truck volume. It is desired to further conduct an hourly analysis to explore a new relationship between snowfall and truck volume.