01622542

Component

Understanding individual activity-mobility behavior at a finer spatio-temporal resolution has various applications including urban planning, traffic management, spread of biological and mobile viruses, and disaster management. In recent years, proliferation of modern data sources such as GPS observations, mobile phone call records, smart card transactions, and social media activities significantly improved the quality of the activity-mobility pattern observations and reduced the cost of data collection. In this research, the authors 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 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 and shopping. Therefore, it could be a reliable source of data to identify time, location, and activity types of individual students. In this paper, the authors present two activity-mobility trajectory reconstruction algorithms. 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. Then the authors 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 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. 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 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 transaction type data sets.

This paper was sponsored by TRB committee ABJ40 Standing Committee on Travel Survey Methods.

01618707

17-00905

English

22p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References; Tables

Activity choices; Algorithms; College students; Data collection; Mobility; Smart cards; Trajectory; Travel behavior

Data and Information Technology; Planning and Forecasting; Public Transportation

Transportation Research Board Annual Meeting 2017 Paper #17-00905

TRIS, TRB, ATRI

Dec 8 2016 10:14AM