Abstract
In recent years, mobility data from smart cards, mobile phones and sensors have become increasingly available. However, they often lack some of the key information including the purposes of trips for each individual user. Information on trip purposes is crucial for projecting the future travel patterns as well as understanding the characteristics of each area of a city and how it is changing. This paper proposes a method called EAT-CD (Extraction of Activity Types and Change Detection). It estimates the volume of passengers by activity types (e.g. commuting, leisure) using non-negative matrix factorization and detects changes in the number of visitors for each activity (e.g. increase in shopping trips triggered by the development of a new commercial facility). Validity of EAT-CD is tested through empirical analysis using smart card data of public transportation in Western Japan. The results showed that EAT-CD is effective in deriving activity patterns, which showed strong correlation with travel survey data. The results also confirmed that EAT-CD detects changes in travel patterns (e.g. start and end of semesters) and land uses (e.g. establishment of new facilities).
Highlights
Trips within and between cities are manifested through the need to access places and participate in activities [1]
We propose a method called extraction of activity types and (EAT-CD) (Extraction of Activity Types and Change Detection) that estimates the volume of passengers by each activity and detects changes in the number of visitors for each activity; e.g. increase in shopping trips triggered by the development of a new commercial facility
Basic distributions extracted by negative matrix factorization (NMF) Using EAT-CD, the smart card data of public transport were classified into nine types of trip activities
Summary
Trips within and between cities are manifested through the need to access places and participate in activities [1]. They range from the daily commute to workplaces to ad hoc excursions. Analyzing the patterns of such human mobility marks an important step towards understanding human activities in a city and, thereby, help planning and managing public transport and road traffic. To this end, urban planners, social scientists and real-estate developers have often relied on travel survey data (e.g. Axhausen et al [2]). Given the extensive and comprehensive nature of mobility data, they are expected to help contribute to the planning and improvement of cities in
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