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In recent years, automated vehicles have been developing rapidly, and some automated vehicles have begun to drive on highways. The market share of automated vehicles is expected to increase and will greatly affect traffic flow characteristics. This paper focuses on the mixed traffic flow of manual and automated vehicles. The study improves the existing cellular automaton model to capture the differences between manual vehicles and automated vehicles. Computer simulations are employed to analyze the characteristic variations in the mixed traffic flow under different automated vehicle proportions, lane change probabilities, and reaction times. Several new conclusions are drawn in the paper. First, with the increment of the proportion of automated vehicles, freeway capacity increases; the capacity increment is more significant for single-lane traffic than for two-lane traffic. Second, for single-lane traffic flow, reducing the reaction time of the automated vehicle can significantly improve road traffic capacity—as much as doubling it—and reaction time reduction has no obvious effect on the capacity of the two-lane traffic. Third, with the proportion increment of automated vehicles, lane change frequency reduces significantly. Fourth, when the density is 15 < ρ < 55 vehicles/km, the addition of 20% automated vehicles to a traffic flow that consisted of only manual vehicles can decrease congestion by up to 16.7%.

In many developing countries like China, many queuing electric bikes (e-bikes) passing an intersection simultaneously greatly reduces the capacity of the intersection for motor vehicles, by invading the passing area of motor vehicles. To study the invasion effect of e-bikes on the traffic flow of motor vehicles at an urban signalized intersection, this paper proposes a social force model for the heterogeneous traffic flow of motor vehicles and e-bikes. The proposed model is calibrated and validated using real data collected in Chengdu, China. The validation results show that the proposed model can replicate the heterogeneous traffic flow with low errors. Simulations based on the proposed model are conducted to investigate what strategies can reduce the invasion of e-bikes in normal motor vehicle traffic. The results show that when the number of queuing e-bikes before the stop line is more than 20, the two strategies can be applied: the stop-line-ahead strategy and the green-signal-ahead strategy. The study suggests that the 2–4 s of green signal ahead or 3–5 m of stop line ahead for non-motor vehicles can significantly reduce the interference of e-bikes on motor vehicle traffic. In addition, the combination of the two strategies can also obtain the same effect but with smaller change to the original intersection design.

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