Multiclass First-in-First-Out Cell Transmission Model (MF-CTM): A Traffic Flow Model to Estimate Dynamic Road Capacity due to Connected Autonomous Vehicles Operating in Mixed Traffic

Connected Autonomous Vehicle (CAV) is a new technology that can operate without human intervention and able to communicate with other CAVs and connected infrastructure. Due to its ability, CAV is expected to create significant change in traffic management and creates benefits e.g., increasing road traffic performance, road safety improvement. Before CAV has a significant share on the road, there will be a transition period where CAV co-exists with normal vehicle (NV). The purpose of this study is therefore to develop a tool, based on cell transmission model (CTM), to assess the impact of CAV in mixed traffic. We name the model as multiclass first-in-first-out cell transmission model (MF-CTM). The model has three distinct features, namely: i) First-in-First-Out (FIFO), ii) Dynamic Maximum Flow Rate, and iii) Dynamic Maximum Cell Occupancy. In this study, we present the mathematical formulas, algorithm, and numerical illustration of the MF-CTM for a one-lane single road link. By simulation using MF-CTM, we demonstrate that the model can: i) represent decreasing queue length and dissipation time during congestion as CAV share increases, ii) maintain the order of vehicle groups (traffic cohort) during congested condition, iii) represent fluctuation in link outflow rates due to fluctuating CAV shares in the traffic. For further studies, the formulations and algorithm of MF-CTM can be extended into networks and include more elements e.g., signalised intersections, bus lanes, etc. In the future, we expect that the MF-CTM can be a useful tool to assess the effectiveness of traffic management measures involving CAVs.