Constructing activity–mobility trajectories of college students based on smart card transaction data

In this research, we use UB card as a convenient source of combined smart transaction data in order to define a campus-wide model for constructing students’ activity–mobility trajectories in time–space dimension. UB Card is a student's official ID at the University at Buffalo and is used across campus for various activities including Stampedes and Shuttles (on-campus bus system), facilities access, library services, dining, shopping, and etc. Two activity–mobility trajectory construction algorithms are developed. The base algorithm constructs students’ activity–mobility patterns in space–time dimension using a set of smart card transaction data points as the only inputs. The modified individualized algorithm constructs activity–mobility patterns with prior knowledge of students’ previous patterns as they have similar patterns for certain days of the week. A database of 37 students’ travel survey and UB card transactions that contains a period of 5 days have been used to illustrate the results of the study. Three measures of errors have been proposed to capture the time allocation, location deviation, and activity sequences. These errors present an acceptable accuracy (12–25% error ranges for activity types and average 0.04–0.16 miles of error for location predictions) and show the potential of inferring activity–mobility behaviors based on smart card transaction type data sets.