The diverse nature of vehicle categories and the resultant lane discipline in mixed (heterogeneous) traffic cause complex spatial interactions. As a result, the driving behavior process in mixed traffic conditions is meaningfully different, where both longitudinal and lateral movements of the vehicles continuously occur. Under prevailing homogeneous traffic conditions in developed countries, driving behavior is partially discrete, where following longitudinal behavior and outboard lane-change models can model traffic behavior. However, the established car-following and lane-change models cannot be directly used in shaping mixed traffic conditions. Such conditions also warrant the use of high-quality microlevel vehicular trajectory data. Accordingly, realizing this need, vehicular trajectory data for different traffic flow conditions were developed. The data were used to extract the parameters required for modeling the vehicles' positions using machine learning algorithms. Three established supervised machine learning algorithms (k-NN, random forest, and regression tree) and deep learning are selected to model mixed traffic conditions. The parameters which influence longitudinal and lateral movements are identified using Spearman correlation analysis. Furthermore, simulation runs are performed using the python language. The performance of the algorithms is evaluated both at the microscopic and macroscopic levels using relevant traffic indicators. The results show that a deep learning model and k-NN tend to replicate better-mixed traffic conditions than random forest and regression trees.

The paper presents the performance analysis of a well-designed truck parking terminal, which is planned for regulating truck traffic over a commercial port. The designed truck parking terminal is modeled using microscopic traffic simulation, which is validated based on the movement of vehicles to the parking bays. After validation, various scenarios were created to evaluate parking terminal performance by varying the parking volumes and number of operational parking bays. The operational efficiency of the parking terminal for design scenarios was evaluated using parking performance measures that included parking load, average parking duration, parking turnover, and load-to-capacity ratio (parking index). For the design peak load of 4,200 vehicles/day with a uniform arrival rate, the operational efficiency was found to be about 73%. Interestingly, it was observed that with an increase in the number of operational parking bays, the parking efficiency decreased for the given volume level. Considering this phenomenon, a methodology was developed to identify the optimum number of parking bays under varying demand-supply scenarios. The developed methodology can help identify the optimum number of parking bays for existing and future (expansion) conditions. Furthermore, this study highlights the importance of using simulation in evaluating operational and design aspects of transportation facilities, where the need for repeated empirical observations is eliminated. As such, this study should be of interest to traffic engineers and practitioners interested in the efficient operation of parking terminals.

The present work introduced a framework of developing comprehensive extended vehicular trajectory data under heterogeneous non-lane-based traffic conditions like the NGSIM datasets in the United States. Due to the absence of automation and instrumentation, and even the lack of sensor deployment on roads in developing economies like India, it is even more challenging to study driver behavior. A new stitching-based algorithm was used for developing the extended trajectory database for three traffic-flow levels on a 535-m long section of an urban arterial. The algorithm was used to stitch the trajectory data over the segments such that the subject vehicle with continuous trajectory data points over the entire study stretch. The developed framework is a novel tool for establishing a trajectory dataset for mixed traffic, it should be of interest to researchers in developing and developed countries.