The average percentage of heavy vehicles on freeways in China can reach 25%. With the development of large-scale and heavy-duty vehicles and the increase in heavy vehicles, the impact of heavy vehicles on freeways will become more and more serious. Multi-lane freeways are the regional arterial highways and already account for more than 18% in the freeway network. Because of the change in road infrastructure and the lane management, the impact of heavy vehicles on multi-lane freeways is changed compared with two-way four-lane freeways. This research can analyze the car-truck interaction and find out the impact of heavy vehicles on multi-lane freeway traffic state parameters, which can be used to provide the theoretical basis and technical support for traffic manager to make traffic management strategy. It is of great significance to reduce the negative effect of heavy vehicles and improve the traffic safety and operation efficiency of multi-lane freeways.
The traffic characteristics of multi-lane freeways are analyzed. The operating feature of multi-lane freeways is identified compared with two-way four-lane freeways from the road facility, traffic management, traffic environment and vehicle behavior. Combining with the actual survey data, the difference of traffic volume, velocity and headway on multi-lane freeways under normal conditions and lane management is analyzed. Through the analysis, the operating feature of heavy vehicles on the multi-lane freeway and the influence of lane management on truck operation are found. These analyses provide the basis for the subsequent studies of the impact of heavy vehicles on traffic flow.
The basic characteristics of the impact of heavy vehicles on drivers are analyzed and a quantitative analysis method of the impact of heavy vehicles on drivers is put forward. The data obtained from SP survey is analyzed statistically to find out the basic characteristics of the impact of heavy vehicles on drivers, including universality, influence range, cause, influence degree and influence effect. The quantitative analysis method of the impact of heavy vehicles on drivers is constructed, which includes the binary logistic model between the truck impact and personal attributes and the driving environment and the quantitative calculation model for the impact of heavy vehicles on drivers based on the fuzzy logic. The binary logistic model can be used to screen out the significant factors and analyze the mechanism of the truck impact on drivers. The quantitative calculation model can be used to calculate the value of the impact of trucks under different environments. These analyses provide the idea for the construction of the simulation model for driver’s behavior.
The method of constructing the cellular automata model of car-following based on the vehicle combination is proposed. The car-following behavior difference among different vehicle combinations is verified from time headway, space headway, velocity and acceleration by Kruskal–Wallis test, Mann–Whitney U test, t-test, etc. Four vehicle combinations are set as the objects in the modelling. Main factors of each vehicle combination are screened out by variance analysis and effect size and the driving behavior rule, anti-collision rule and randomization rule are then built. The model validation is implemented by error test and the proposed model is compared with some existed cellular automata model to verify the effectiveness. The results show that the proposed model can consider the behavior difference between cars and heavy vehicles and the impact of heavy vehicles on the car-following behavior of other vehicles. It has a better simulation effect.
The method of constructing the cellular automata model of lane change considering the difference of the vehicle type is proposed. The data which obtained based on the spatial location of vehicles during the lane change is used to find out the difference between car drivers and heavy vehicle drivers in lane-changing decision and lane-changing execution. In addition, the effect of heavy vehicles on the lane-changing choice is obtained. In the modeling process, cars and heavy vehicles are set as the objects, respectively. Based on the differences in lane change characteristics between different vehicle types, the lane-changing decision rule and lane-changing execution rule of cars and heavy vehicles are built. Also, the impact of heavy vehicles on the lane-changing probability is added. The model validation is implemented by error test and the proposed model is compared with some existed cellular automata model to verify the effectiveness. Because considering the differences in the lane change of cars and heavy vehicles and the impact of heavy vehicles on the lane-changing probability, the accuracy is improved.
Combining the proposed car-following model and lane-changing model, it is used to simulate the operation of the multi-lane freeway to analyze the impact of heavy vehicles on the basic segment and the on-ramp segment of the mluti-lane freeway. For the basic segment, heavy vehicle percentage, vehicle combination, maximum speed of heavy vehicle, heavy vehicle length, impact strength of heavy vehicle and lane management are variable to obtain traffic volume, velocity, congestion rate, lane-changing rate and stability of the multi-lane freeway in different scenarios. For the on-ramp segment, the impact of heavy vehicles on the on-ramp system, the main road traffic and the on-ramp traffic is analyzed, respectively. The barrier effect of heavy vehicles is studied in this part. The influence of different factors on the barrier effect of heavy vehicles are analyzed and the occurrence condition of the barrier effect of heavy vehicles is determined. Finally, some measures for improving the impact of heavy vehicles are put forward based on the analysis.