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On the basis of one-lane comfortable driving model, this paper established a two-lane traffic cellular automata model, which improves the slow randomization effected by brake light. Considering the driver psychological characteristics and mixed traffic, we studied the lateral influence between vehicles on adjacent lanes. Through computer simulation, the space-time diagram and the fundamental figure under different conditions are obtained. The study found that aggressive driver makes a slight congestion in low-density traffic and improves the capacity of high-density traffic, when the density exceeds 20pcu/km the more aggressive drivers the greater the flow, when the density below 40pcu/km driver character makes an effect, the more cautious driver, the lower the flow. The ratio of big cars has the same effect as the ratio of aggressive drivers. Brake lights have the greatest impact on traffic flow and when the density exceeds 10pcu/km the traffic flow fluctuates. Under periodic boundary conditions, the disturbance of road length on traffic is minimal. The lateral influence only play a limited role in the medium-density conditions, and only affect the average speed of traffic at low density.

The mixed traffic of conventional vehicles (CVs) and connected and autonomous vehicles (CAVs) will dominate a long period in the popularization of CAVs and its dynamics are still not clear, especially in some complicated traffic scenarios. In this paper, an improved cellular automaton model was applied to investigate the CVs–CAVs mixed traffic dynamics around freeway merging area. The lane-changing rules were improved to consider the difference between CAVs and CVs and also the variation of drivers’ aggressive driving during the merging process. The simulation has been conducted with different traffic demands and percentages of CAVs while the phase diagram, spatial-temporal diagram, characteristics of traffic volume, velocity profiles, stability and traffic conflict around the merging area were obtained. The results show that CAVs contribute to improving the traffic operation of freeway merging area in most conditions. However, more CAVs in the merging area without proper management will have a negative impact on traffic if the traffic demand for the main road is great. For example, the stability can decrease and particularly, the on-ramp traffic will be completely blocked when the percentage of CAVs is one. The findings can provide a basis for further research on the CVs–CAVs mixed traffic.

With the rapid development and application of autonomous technology in vehicles, we are going to see more autonomous vehicles on the roads in a foreseeable future. While autonomous vehicles may have the advantage of reducing traffic accidents caused by human drivers’ neglect and/or fatigue, one of the challenges is how to develop autonomous driving algorithms such that autonomous vehicles can be safely deployed in a mixed traffic environment with both autonomous vehicles and human-driven vehicles. Instantaneous lane-changing type may be significantly different for human drivers, which would lead to traffic accidents with other vehicles including autonomous vehicles. In this paper, we propose a resilient algorithm for the prediction of the human driver’s lane-changing behaviors. The proposed algorithm uses a long-short term memory (LSTM) classifier to identify the conservative lane change and the aggressive lane changing and accordingly makes the accurate prediction on lane changes in the driving of vehicles by human drivers. The proposed method provides a useful addition in facilitating the design of more advanced driving algorithms for autonomous vehicles. Using the vehicle trajectory data in the NGSIM data set for a large number of simulations, the effectiveness of this method has been confirmed.

Forecasting traffic congestion has become an important issue than ever before. Information provided by traffic congestion prediction methods becomes helpful to traffic management agencies to take suitable actions in due time. On the other hand, with prior information, passengers can plan their trip routes accordingly. In this paper, we tackle the problem of mixed traffic congestion prediction and develop a new Bi-State Prediction Network (BiSPNet). Two heads form BiSPNet. Each head information passed through the layers of three-dimensional convolution (Conv3D), two-dimensional convolutional long short-term memory (ConvLSTM2D), and two-dimensional convolution (Conv2D). Then the dense layer merges both head outputs. These layers capacitate the model to capture the characteristics of multiple inputs. The proposed model is validated by the ground truth data collected by the video cameras installed on the roadways in Delhi, India. Experimental results confirm that the prediction accuracy of the BiSPNet model surpasses that of several state-of-the-art benchmark models. In particular, the BiSPNet reduces mean absolute error by 53.23%, 40.95%, and 0.38%, respectively, in congestion score prediction for 5, 10, and 15-min time horizons compared to the mixed deep learning network (MDLNet). The source data and code can be accessed on GitHub at the following link: https://github.com/amanojup/.