Constructing Activity-Mobility Patterns of University at Buffalo Students Based on UB Card Transactions
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Understanding activity-mobility patterns can provide crucial information for various applications including urban planning, traffic management, spread of biological and mobile viruses, disaster management, etc. In recent years, proliferation of modern data sources such as GPS data, mobile phone calls, credit card transactions, metro card transactions, and social media significantly improved the quality of the activity-mobility pattern observations and reduced the cost of data collection. In this research, we propose to use UB card as a convenient source of combined data in order to define a campus-wide model for constructing students’ activity-mobility patterns 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 and shopping. Therefore, it could be a reliable source of data to identify time, location, and activity types of individual students. We developed two activity-mobility construction algorithms. The base algorithm constructs students’ activity-mobility patterns in space-time dimension using a set of UB card transaction data points as the only input. Then we modified the base algorithm to construct activity-mobility patterns with prior knowledge of students’ previous patterns as they have similar patterns for certain days of the week. A data base 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. These Travel surveys contain detailed information of the students’ daily routine from home to school and back as well as other activities such as social, shopping, exercise, etc, that is used to validate the performance of these algorithms. Three measures of error have been proposed to capture the time allocation, location deviation, and activity sequences.