Connected Vehicles (CVs) could enhance traffic management systems by providing detailed and real-time information. Theoretically, such information can be exploited for the provision of efficient movement of traffic, especially at intersections identified as the bottlenecks of traffic systems. Aimed at the same purpose, this paper uses information of CVs to estimate the Saturation Flow Rate (SFR), particularly in the transition period during which CVs and conventional vehicles will coexist. To this end, we retain the advantages of data-driven techniques to capture the underlying dynamics of the SFR by considering information of CVs as the only input. In this regard, we correlate the dynamic variations of the SFR to the mutual interactions among the contributing parameters extracted from the limited pieces of CVs’ information using a neural network. Comprehensive simulations under precisely designed settings in VISSIM show a hoped-for SFR estimation accuracy level, which can further augment intelligent intersection controller initiatives.

The need for transporting commodities has led to more and more freight vehicles on urban roads. Specific operational constraints of such vehicles could induce non-homogeneities in the smooth movement of traffic, especially at intersections where acceleration/deceleration events occur frequently. This leads to unnecessary wasted time for all vehicles, even in low to moderate traffic conditions. Hence, the literature reports different proposals to enhance the continuity of traffic at intersections. Among them, the green extension strategy has attracted researchers’ attention, owing to its simplicity, flexibility and practicality. In this paper, we propose a new approximate probabilistic model for the expected waiting/wasted time of all vehicles at an intersection with green time extension in low to moderate traffic conditions. Accordingly, the optimal green extension interval that minimizes the total expected waiting time can then be determined in different conditions. The proposed analysis needs few pieces of information (as opposed to microsimulation models conventionally employed to analyze such systems) and is therefore, suitable for quickly deciding on the optimal strategy based on the current situation in a dynamic environment. We have validated our approximate analysis with simulations in the VISSIM simulation tool.