Transit passenger market segmentation enables transit operators to target different classes of transit users for targeted surveys and various operational and strategic planning improvements. However, the existing market segmentation studies in the literature have been generally done using passenger surveys, which have various limitations. The smart card (SC) data from an automated fare collection system facilitate the understanding of the multiday travel pattern of transit passengers and can be used to segment them into identifiable types of similar behaviors and needs. This paper proposes a comprehensive methodology for passenger segmentation solely using SC data. After reconstructing the travel itineraries from SC transactions, this paper adopts the density-based spatial clustering of application with noise (DBSCAN) algorithm to mine the travel pattern of each SC user. An a priori market segmentation approach then segments transit passengers into four identifiable types. The methodology proposed in this paper assists transit operators to understand their passengers and provides them oriented information and services.

This paper proposes a vehicle trajectory planning method for automated on-ramp merging. Trajectory planning tasks of an on-ramp merging vehicle and a mainline facilitating vehicle are formulated as two related optimal control problems. Rather than specifying the merge point via external computational procedures, the location and time that the on-ramp vehicle merges into the mainline are determined endogenously by the optimal control problem of the facilitating vehicle. Bounds on vehicle acceleration are explicitly considered. The Pontryagin Maximum Principle is applied to find the solutions of the optimal control problems. In order to accommodate the constantly changing external environment, the proposed optimal control method is subsequently implemented in a recursive planning framework. Because of the nature of the problem, the length of the planning horizon is time-varying, unlike the conventional model predictive control applications where the planning horizon is a fixed length. Numerical experiments are conducted to study the performances of the proposed methodology under the influence of different leading vehicle trajectories and with different lengths of the planning updating interval. In particular, an experiment involving a real-world leading vehicle trajectory and considering different traffic demand levels are presented. The proposed methodology performs well in these experiments and has demonstrated good potential in real-time applications.