Space-time modeling of urban population daily travel-activity patterns using GPS trajectory data
|dc.contributor.author||Scholz, Ruojing Wang ( )|
|dc.identifier.citation||Scholz, R. W. (2018). Space-time modeling of urban population daily travel-activity patterns using GPS trajectory data (Unpublished dissertation). Texas State University, San Marcos, Texas.|
People conduct travel to engage in a variety of activities every day. Their activities include working, dining, shopping, and so forth. Their travels include trips to these activity locations. Each individual may have the same or a different schedule of travels and activities (T-A for short) on different days. A group of individuals may have similar or completely different daily T-A routines. Human T-A behaviors are very complex. Similarities and differences in space, time, and attribute exist among different individuals and on different days. Modeling human T-A patterns at the individual and collective levels is a research challenge in the field of geography and transportation. Previous methods for modeling collective T-A patterns failed to combine the spatial and temporal dimensions. For individual T-A pattern modeling, existing methods do not combine travel and activity events in one model nor do they make a connection between them.
This research aims to develop effective methods to model urban population collective activity patterns and individual daily T-A patterns. To accomplish this, the proposed method for modeling collective activity patterns identified the locations and times of activity hot spots in a large, metropolitan city, San Francisco, and tracked the evolvement process of these hot spots over time. GPS trajectory data of 536 taxi cabs over twenty-two days in San Francisco were analyzed to demonstrate the effectiveness of the proposed method and reveal collective activity patterns across the city and over time. Taxi passengers' pick-up and drop-off locations and times were extracted from the trajectories and treated as passengers' activity instances. Census tracts with a significantly large number of activity instances during a one-hour interval were defined as activity hot spots. The evolving process of activity hot spots included emergence, expansion, stableness, shrinkage, displacement, and decease. This process was evaluated relative to the hot spot status at the census tract that hosted the hot spot and its neighboring tracts at two consecutive time intervals. The results indicated that collective activity patterns on a weekday were substantially different from those on a weekend day, and historical average data might be used to predict up-coming collective activity patterns. The proposed method for individual daily T-A pattern modeling identified the most frequent daily T-A events and their sequential relationships. The T-A events of an individual in one day was represented with a sequence of T-A elements. Daily T-A sequences of an individual from different days were grouped based on element similarity. The representative sequences of each group revealed the individual's different daily routines. GPS trajectory data for two individuals living in the northern area of Beijing was used to demonstrate the proposed method. The results showed that an individual might have several different daily T-A patterns or no apparent pattern. The proposed methods provide researchers with tools to study complex T-A behaviors of urban people, and calls into question a fundamental assumption in transportation geography that each individual repeats their T-A routine every day (Huff and Hanson 1986; Hanson and Huff 1988; Stopher and Zhang 2011). Further testing of this assumption may change the current design of transportation surveys as well as the modeling of transportation demand, urban planning, traffic management, the delivery of Location-Based Services (LBS), and other services.
|dc.format.medium||1 file (.pdf)|
|dc.subject||GPS trajectory data|
|dc.subject||Hot spot detection|
|dc.subject||Sequence alignment method|
|dc.subject.lcsh||Geographic information systems||en_US|
|dc.subject.lcsh||Urban transportation--California--San Francisco||en_US|
|dc.title||Space-time modeling of urban population daily travel-activity patterns using GPS trajectory data|
|thesis.degree.discipline||Geographic Information Science|
|thesis.degree.grantor||Texas State University|
|thesis.degree.name||Doctor of Philosophy|