This paper presents a Support Vector Regression (SVR) approach that can be applied to predict the multianticipative driving behavior using vehicle trajectory data. Building upon the SVR approach, a multianticipative car-following model is developed and enhanced in learning speed and predication accuracy. The model training and validation are conducted by using the field trajectory data extracted from the Next Generation Simulation (NGSIM) project. During the model training and validation tests, the estimation results show that the SVR model performs as well as IDM model with respect to the model prediction accuracy. In addition, this paper performs a relative importance analysis to quantify the multianticipation in terms of the different stimuli to which drivers react in platoon car following. The analysis results confirm that drivers respond to the behavior of not only the immediate leading vehicle in front but also the second, third, and even fourth leading vehicles. Specifically, in congested traffic conditions, drivers are observed to be more sensitive to the relative speed than to the gap. These findings provide insight into multianticipative driving behavior and illustrate the necessity of taking into account multianticipative car-following model in microscopic traffic simulation.

The underground logistics system is a relatively new concept for container transportation, which is designed to reduce the congestion and pollution on the road caused by the sharply growing number of collections and distributions of containers in the port cities. This paper considers a system where some underground logistics vehicles (ULVs) are marshaled and used to transport containers between two port terminals through a deep underground tunnel. Automated guided vehicles (AGVs) are used for horizontal transportation of containers in the above-ground yard of the terminals, and yard cranes (YCs) are used to transfer the containers vertically through a shaft linking the above-ground yard and the deep underground tunnel. To guarantee the efficiency of this system, a joint scheduling problem of the YCs and the ULVs is proposed and formulated as an integer programming model to minimize the total waiting time of the YCs and ULVs. Taking marshaling and congestion of the ULVs into consideration, a Genetic Algorithm is developed to solve the problem. Numerical experimental results prove the efficiency of the proposed algorithm, and different marshaling strategies are compared. Our research provides a scientific foundation for developing underground logistics systems in large port cities.