Space-time modeling of urban population daily travel-activity patterns using GPS trajectory data
type: CreativeWork
description: 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 …
abstract: 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 …
author: Scholz, Ruojing Wang
language: en
keywords: GIS, space-time modeling, travel-activity pattern, GPS trajectory data, hot spot detection, sequence alignment method
identifier: Scholz, R. W. (2018). Space-time modeling of urban population daily travel-activity patterns using GPS trajectory data [Doctoral dissertation, Texas State University].
date published: 2018-05
date modified: 2026-07-27T21:19:19Z
provider: Texas Digital Library
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Texas Digital Library

PropertyValue
property ID: Handle
value: https://hdl.handle.net/10877/7464