Abstract
Although stable in the short term, individual travel behavior generally tends to change over the long term. The ability to detect such changes is important for product and service providers in continuously changing environments. The aim of this paper is to develop a methodology that detects changes in the patterns of individual travel behavior from vehicle global positioning system (GPS)/global navigation satellite system (GNSS) data. For this purpose, we first define individual travel behavior patterns in two dimensions: a spatial pattern and a frequency pattern. Then, we develop a method that can detect such patterns from GPS/GNSS data using a clustering algorithm. Finally, we define three basic pattern-change scenarios for individual travel behavior and introduce a pattern-matching metric for detecting these changes. The proposed methodology is tested using GPS datasets from three randomly selected anonymous users, collected by a Chinese automotive manufacturer. The results show that our methodology can successfully identify significant changes in individual travel behavior patterns.
Highlights
Understanding individual travel behavior is important in commercial advertising, location-based service (LBS) design, travel demand management, and urban planning [1]
We present a methodology that detects changes in individual travel behavior from vehicle global positioning system (GPS) data
We adopt a clustering method to identify travel patterns, and we discover changes by comparing the patterns generated from datasets of different time periods
Summary
Understanding individual travel behavior is important in commercial advertising, location-based service (LBS) design, travel demand management, and urban planning [1]. Stable in the short term, individual travel patterns are subject to change over the long term. When people move from the suburbs to the city center, they may shorten their travel distance, increase their overall travel frequency and the number of locations they visit, and shift their commuting hours. Zhan et al [2] proposed a method to detect whether a pattern change has occurred and, if so, to identify the time points of such changes, referred to as changepoints. Understanding and adapting to changes in individual travel behavior is important for product and service providers in a continuously changing environment [9]. By identifying when users change the places they frequently visit, automotive manufacturers can discover new vehicle usage scenarios and can infer new user requirements and make product adjustments
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