Influence of traffic flow of on-ramps on the mainline speed on freeways

Abstract

Vehicles entering from on-ramps can increase the speed dispersion of the mainline and induce frequent changing lanes or acceleration and deceleration behaviors. These complex traffic behaviors interfere with traffic on the mainline and thus result in congestion and safety issues. Reasonable management and control of ramps, especially on-ramps, has been proven to be an effective solution for traffic congestion caused by ramp traffic flow. Understanding the influence of traffic flow of on-ramps on the average speed of the freeway mainline is useful for creating effective ramp management strategies. In this study, field tests were employed to gather traffic flow data on some typical basic freeway interchanges in China. As it is difficult to obtain the required traffic conditions only through field tests, the VISSIM traffic simulation model was also utilized. The same set of field data was used in VISSIM and the driver behavior model parameters CC0 (standstill distance between vehicles) and CC1 (time headway) were calibrated based on the sensitivity analysis to truly reflect the actual traffic conditions. The simulation program was executed with the calibrated parameters and various on-ramp traffic volumes to supplement the traffic data. The gathered traffic data sets from field tests and simulations were classified into four groups based on the various on-ramp traffic flow patterns (free-flow, reasonably free-flow, unstable flow, and congested flow condition). The influence of on-ramp traffic flow on the mainline average speed is discussed for each group. The results showed that the average travel speed of the mainline is significantly affected by the v/C ratio of the on-ramp, as the v/C ratio of the entrance ramp increases, the average travel speed of the mainline significantly decreases. Additionally, the four-parameter logistic model was developed to model the mainline average speed changes with different mainline v/C ratios under various on-ramp traffic flow patterns. The results demonstrate that the model fits the data well. The findings of this study can provide reference information for the implementation of ramp management strategies.