In the context of urban traffic increasing, the study of driving behaviour in the vicinity of signalised intersection is emerging. Driving behaviour on the signalised road is different from the one on the highway due to the influence of signal lights. In this study, the authors analyse the trajectory data of Lankershim Boulevard from the next generation simulation (NGSIM) database. The car‐following data are filtered to exclude the influences of lane changing. They analyse the driving data from both micro‐ and macro‐perspectives. The quantitative relationship between acceleration and other factors, as well as the distribution of headway, are analysed. It is found that the statistic features of headway are in a discrepancy between different signal data. A simulation testbed is constructed, and two calibrated traditional car‐following models are utilised to reproduce the driving behaviour at signalised intersections. The simulation results illustrate that the traditional models cannot be used to simulate traffic flow on the signalised road, i.e. they cannot describe the difference of headway under different signal states. Based on the data analyses, the authors propose a new car‐following model with the consideration of signal status. The simulation results of the new model are in better agreement with the actual data.

Drivers are not far-sighted when they execute lane-changing manipulation. To address this issue, this study proposes a rule to improve vehicles' lane-changing decisions with accurate information of surrounding vehicles (e.g. time headway). More specifically, connected and autonomous vehicles (CAVs) change lanes in advance if they find severer flow reducing in the lanes, while CAVs should maintain the car-following state if the variations of traffic flow in all lanes have a similar trend. To illustrate the idea, this study first calibrates two classic car-following models and a lane-changing model, and then conducts numerical simulations to illustrate the short-sighted decision of drivers. The study incorporates the idea into a lane-changing decision rule by changing the lane-changing model's parameter, and conducts numerical tests to evaluate the effectiveness of the lane-changing decision rule in a multi-lane highway with a bottleneck. The results of this study indicate that the new lane-changing decision rule can substantially improve the throughput of the traffic flow, especially when the inflow exceeds the remaining capacity of the road. The lane-changing rule and results can bring insights into the control of CAVs, as well as the driver assistance system in connected vehicles.