In the upcoming decades, connected vehicles will join regular vehicles on the road, and the characteristics of traffic flows will change accordingly. To better understand mixed traffic flow (regular vehicles and connected vehicles) characteristics, a generic car-following modeling framework for this new mixed traffic flow on an urban road under a connected vehicle environment is proposed in this paper. Considering a vehicle’s speed, which is affected by the speed of the preceding vehicle, an improved intelligent driver model (IDM) is used as the car-following model for regular vehicles. An extended cooperative adaptive cruise control (CACC) based on the nonlinear dynamic headway strategy was established as the car-following model for connected vehicles. The fundamental diagram model of mixed traffic flow under different market penetration rates of CACC vehicles is investigated, and the traffic flow operation mechanism of connected vehicles is analyzed to improve the capacity. In addition, simulation experiments on urban roads are designed to evaluate the queue time and length of vehicles passing through congested sections under different market penetration rates of CACC vehicles. The results demonstrate that the proposed model can effectively describe the current situation of traffic flow on urban roads under different market penetration rates of CACC vehicles. The increase in the market penetration rates of CACC vehicles can significantly improve the traffic flow efficiency. When CACC vehicles reach 100% of all vehicles, the queue length on congested roads can be shortened by 64.6%, and the total travel time on congested roads can be reduced by 48.3%.

A spatiotemporal speed contour (SSC, or time-space traffic) diagram that exhibits traffic dynamics in time and space is of importance in transportation research and applications. This paper empirically investigates the feasibility of using non-rectangular parallelogram (nRP) cells to construct the SSC diagram, given complete trajectory and probe vehicle data. Compared with the traditional way of using rectangular parallelogram (RP) cells, using nRP cells further considers the direction of backward-moving wave. The trajectory dataset provided by Next Generation Simulation is employed as the data source, and travel time is taken as a metric to measure the quality of the constructed SSC diagram. Various comparisons demonstrate that using nRP cells outperforms using RP cells, in particular when the traffic is congested and the cells are relatively large. Therefore, we recommend using nRP cells to construct the SSC diagram in future, which is as simple as the traditional way but more accurate particularly in terms of estimating travel time.