Microscopic traffic flow models enable predictions of traffic operations, which allows traffic engineers to assess the efficiency and safety effects of roadway designs. Modeling vehicle trajectories inside intersections is challenging because there is an infinite number of possible paths in a two-dimensional space, and drivers can simultaneously adapt their speeds as well. To date, human driver models for simultaneous longitudinal and lateral vehicle control based on the infrastructure characteristics and interactions with other drivers inside an intersection are still lacking. The contribution of this paper is threefold. First, it proposes an integrated microscopic traffic flow model to describe human-driven vehicle maneuvers under interactions. Drivers plan their heading and acceleration in the predicted future to minimize costs representing undesirable situations. The model works with a joint optimization for an interaction cost term. The weights associated with the interaction cost reflect how selfish or altruistic drivers are. Second, the proposed model endogenously gives the order of vehicles in case of crossing paths. Third, the paper develops a clustered validation method for microscopic traffic flow models with interacting vehicles, which account for interdriver variations. Results show that the model can accurately describe vehicle passing orders of interacting maneuvers, paths, and speeds against empirical data. The model can be applied to assess various intersection designs. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71971140 and 52122215] and the Natural Science Foundation of Shanghai [Grant 20ZR1439300].

Many congested intersections have a heavy traffic volume on movements for which capacity is insufficient because of geometric limitations. An unconventional approach that increases the capacity of heavily congested intersections is presented: this approach opens up exit lanes for left-turn traffic dynamically with the help of an additional traffic light installed at the median opening (the presignal); this situation is referred to as exit lanes for left-turn (EFL) control. An optimization problem for EFL control was formulated as a mixed-integer nonlinear program, in which the geometric layout, main signal timing, and presignal timing were integrated. The mixed-integer nonlinear program was solved by transformation into a series of mixed-integer linear programs. The latter problem can be solved with the standard branch-and-bound technique. The results of extensive numerical analysis and VISSIM simulation showed that the EFL approach could increase intersection capacity and reduce traffic delay substantially, especially under high left-turn demand. Moreover, the EFL control can be applied to one or multiple legs simultaneously; thus the control is particularly useful for intersections with an unbalanced left demand and a degree of saturation in travel directions.